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Data Analytics

Decoding Data Analysis Expressions 101: A beginner’s guide to DAX
Ruhma Khawaja
| July 21, 2023

Data Analysis Expressions (DAX) is a language used in Analysis Services, Power BI, and Power Pivot in Excel. DAX formulas include functions, operators, and values to perform advanced calculations and queries on data in related tables and columns in tabular data models. 

 The Basics of DAX for Data Analysis 

DAX is a powerful language that can be used to create dynamic and informative reports that can help you make better decisions. By understanding the basics of Data Analysis Expressions, you can: 

  • Perform advanced calculations on data 
  • Create dynamic filters and calculations 
  • Create measures that can be used in reports 
  • Build tabular data models 
Data Analysis Expressions
Data Analysis Expressions

Creating DAX Tables, Columns, and Measures 

Data Analysis Expressions tables are similar to Excel tables, but they can contain calculated columns and measures. Calculated columns are formulas that are applied to all rows in a column, while measures are formulas that are calculated based on data in multiple columns. 

To create a DAX table, right-click on the Tables pane and select New Table. In the Create Table dialog box, enter a name for the table and select the columns that you want to include. 

To create a calculated column, right-click on the Columns pane and select New Calculated Column. In the Create Calculated Column dialog box, enter a name for the column and type in the formula that you want to use.

To create a measure, right-click on the Measures pane and select New Measure. In the Create Measure dialog box, enter a name for the measure and type in the formula that you want to use. 

Executing DAX Operators 

Data Analysis Expressions operators are used to perform calculations on data. Some common DAX operators include: 

  • Arithmetic operators: These operators are used to perform basic arithmetic operations, such as addition, subtraction, multiplication, and division. 
  • Comparison operators: These operators are used to compare two values and return a Boolean value (true or false). 
  • Logical operators: These operators are used to combine Boolean values and return a Boolean value. 
  • Text operators: These operators are used to manipulate text strings. 

Read more –> Data Analysis Roadmap 101: A step-by-step guide

Discussing Basic Math & Statistical Functions 

DAX includes a wide variety of mathematical and statistical functions that can be used to perform calculations on data. Some common mathematical and statistical functions include: 

  • SUM: This function returns the sum of all values in a column or range. 
  • AVERAGE: This function returns the average of all values in a column or range. 
  • COUNT: This function returns the number of non-empty values in a column or range. 
  • MAX: This function returns the maximum value in a column or range. 
  • MIN: This function returns the minimum value in a column or range. 
DAX Functions
DAX Functions

Implementing Date & Time Functions 

Data Analysis Expressions includes a number of date and time functions that can be used to manipulate date and time data. Some common date and time functions include: 

  • DATEADD: This function adds a specified number of days, months, years, or hours to a date. 
  • DATEDIFF: This function returns the number of days, months, years, or hours between two dates. 
  • TODAY: This function returns the current date. 
  • NOW: This function returns the current date and time. 

Using Text Functions 

DAX includes a number of text functions that can be used to manipulate text data. Some common text functions include: 

  • LEFT: This function returns the leftmost characters of a string. 
  • RIGHT: This function returns the rightmost characters of a string. 
  • MID: This function returns a substring from a string. 
  • LEN: This function returns the length of a string. 
  • TRIM: This function removes leading and trailing spaces from a string. 

Using calculate & filter functions 

Data Analysis Expressions includes a number of calculate and filter functions that can be used to create dynamic calculations and filters. Some common calculate and filter functions include: 

  • CALCULATE: This function allows you to create dynamic calculations that are based on the current context. 
  • FILTER: This function allows you to filter data based on a condition. 


Data Analysis Expressions is a powerful language that can be used to perform advanced calculations and queries on data in Analysis Services, Power BI, and Power Pivot in Excel. By understanding the basics of DAX, you can create dynamic and informative reports that can help you make better decisions. 

Data Analysis Roadmap 101: A step-by-step guide
Ruhma Khawaja
| July 13, 2023

If you are a novice in the field of data analysis or seeking to enhance your proficiency, a meticulously devised data analysis roadmap can serve as an invaluable tool for commencing your journey. Essentially, a data analysis roadmap encompasses a meticulously curated sequence of procedural guidelines that elucidate the fundamental stages inherent in the practice of data analysis. By adhering to such a roadmap, you can rest assured that you are undertaking the requisite measures and obviating the inadvertent omission of critical elements.

Are Data Analysts in demand in 2023?  

The world is generating more data than ever before. In fact, it is estimated that by 2025, the global datasphere will be 175 zettabytes in size. This data is being generated by everything from our smartphones to our smart homes to our cars 

As the amount of data grows, so does the need for data analysts. Data analysts are responsible for collecting, cleaning, and analyzing data to extract insights. These insights can then be used to improve business decisions, optimize operations, and develop new products and services.  

The demand for data analysts is expected to continue to grow in the years to come. In fact, a recent report by the World Economic Forum found that data and AI is one of the seven high-growth emerging professions. Of all seven professions identified, data and AI shows the highest growth rate at 41% per year. 

There are a number of reasons why the demand for data analysts is so high. First, the amount of data being generated is increasing exponentially. Second, businesses are increasingly using data to make decisions. Third, there is a shortage of qualified data analysts in the workforce. 

If you are considering a career in data analytics, there are a number of things you can do to prepare. First, you should develop your skills in data analysis and data science. Second, you should gain experience working with data. Third, you should network with other data analysts.   

Here are some additional reasons why data analysts are in demand in 2023: 

  • The increasing use of big data analytics by businesses to improve decision-making and operations. 
  • The growing need for data-driven marketing and advertising. 
  • The rise of artificial intelligence and machine learning, which require data analysts to help interpret and act on the insights generated by these technologies. 

Step-by-step data analysis roadmap 

Data Analysis Roadmap
Data Analysis Roadmap

Step 1: Get familiar with the fundamentals 

Read about data analytics and its applications in the real world. Familiarize yourself with different types of data analysis and techniques such as regression analysis, factor analysis, cohort analysis, cluster analysis, and time-series analysis. 

Start practicing with data analytics tools like Microsoft Excel by working with open source datasets and performing basic tasks such as creating pivot tables and using formulas. 

Step 2: Commit to the process with a structured course   

Research and choose a specialized data analytics program or course that fits your schedule, budget, and offers the following: 

Mentorship to provide guidance and feedback. Practical, hands-on curriculum with exercises and projects to build your skills and portfolio. Career coaching and job guarantee to assist with finding employment. 

Step 3: Hone your soft skills  

Identify and enhance your soft skills, such as problem-solving, research, and presentation skills.  Look for opportunities within your current job to develop these skills, such as running a small research project or tackling a specific business problem. 

Seek additional opportunities outside of work, such as attending meetups or joining online communities related to data analytics. 

Step 4: Start networking   

Connect with professionals in the data analytics field through platforms like LinkedIn, data analytics groups, and local meetups. Take advantage of your student community or online student groups to connect with peers and gain support.  Networking can provide career prospects, mentorship, and learning opportunities, so embrace it as an essential part of your journey. 

Leanring never stops – Prepare for the job market 

Build a portfolio showcasing your data analytics projects and practical skills. Seek feedback from mentors or professionals to improve your portfolio presentation.Optimize your online presence, particularly your LinkedIn profile, to highlight your skills and career goals. 

Prepare for job interviews by familiarizing yourself with common data analytics interview questions and how to answer them effectively. 

By following these steps, you can progress from an aspiring data analyst to a fully-fledged professional, equipped with the necessary skills, knowledge, and network to succeed in the field. Remember to stay committed, continue learning, and adapt to the evolving landscape of data analytics.  

Additional Tips: 

Here are some additional tips for creating a data analysis roadmap: 

  • Start small. Don’t try to do too much at once. Start with a small project that you can complete in a reasonable amount of time. 
  • Be flexible. Your roadmap may need to change as you learn more about your data and your goals. 
  • Get help. There are many resources available to help you with data analysis. Don’t be afraid to ask for help from a mentor, a colleague, or an online community. 

Data analysis can be a daunting task, but it doesn’t have to be. By following a data analysis roadmap, you can break the process down into manageable steps and make sure that you are on the right track. 

In a nuthsell   

Data analysis is a powerful tool that can help businesses of all sizes improve their operations and make better decisions. By following a data analysis roadmap, you can ensure that you are taking the right steps and that you are not missing anything important. 

If you are new to data analysis, or if you are looking to improve your skills, I encourage you to start by creating a data analysis roadmap. This will help you get started on the right foot and make sure that you are on the path to success. 

Emily Thompson
| June 22, 2023

Digital marketing and SEO have become essential as all businesses strive for a strong online presence to stay ahead of the curve.

For the unversed, SEO refers to search engine optimization, which helps websites stay at the top in the search engine results. This will improve the website’s visibility, traffic and conversions. Different SEO practices are employed to make it possible.

SEO in digital marketing
SEO in digital marketing – Source: Freepik

SEO practices demand technical knowledge and skills to execute them effectively. So, if you want to become a pro in SEO, you should consider joining a learning program. 

This blog will walk you through some of the best practices you should learn to make the website SEO optimized. So, let’s get started to learn to help your website rank at the top of the search engine results.  

6 best SEO practices to improve digital marketing initiatives   

1. Conduct keyword research 

The most fundamental part of SEO is keyword research. In this practice, you analyze the terms people use in the search engine. When you conduct keyword research , you will be able to identify the most popular and relevant keywords for your industry or niche and incorporate them into the content on your website. The keyword-optimized content will improve the website’s rank on the search engines. When you target those specific keywords, your website will come to the top when the users search for anything related to those keywords.  

Various tools are used to conduct keyword research effectively, such as Google Keyword Planner, SEMrush, and Ahrefs. When identifying the keywords, consider search volume, competition, and relevance factors.  

2. Optimize your website’s structure  

It is not only the content but the website structure as well that needs to be optimized. How search engines index your site depends heavily on its structure and navigation. It is simpler for search engines to comprehend and effectively index your site’s content if your website’s navigation and structure are optimized. 

Ensure your website’s structure is logically organized with proper categories and subcategories mentioned properly. This will make it easier for visitors and search engines to navigate your site and find the information they seek. One of the most essential SEO best practices is to use internal linking to connect the related pages on your site. This practice will help search engines understand the connectivity between the pages and improve your search engine rankings 

3. Create high-quality, relevant content 

All the SEO best practices will not be of no use if your website lacks this part. Content is the key to making your website SEO-optimized. Therefore, creating original and valuable content is the most important SEO practice. High-quality content will help you to drive more traffic towards your website and will make it more engaging.  

Before you publish content on your site, understand the needs and preferences of your target market. Make sure the content of your post is what they are looking for. You must understand your audience’s needs and preferences to create high-quality, relevant content. You can ensure this by researching the topics and formats that resonate with your target audience. Moreover, use the relevant keywords and effective on-page optimization to improve it. 

4. Use on-page optimization techniques 

On-page optimization involves the techniques used to optimize web pages. With these techniques, you ensure that your meta descriptions, meta titles, header tags, and image alt tags are optimized with relevant keywords and clearly describe your content. 

This is one of the effective SEO best practices that helps enhance your website’s relevancy and visibility on search engines. Search engines can better grasp the content of your website and rank it higher in search results by using important keywords in the page titles and descriptions. Moreover, using header and image alt tags makes your site more accessible and user-friendly. 

5. Build high-quality backlinks 

The links to the other websites placed on certain keywords on your website refer to backlinks. You can enhance your search engine ranking and the website’s authenticity by acquiring high-quality backlinks from reliable, pertinent websites. 

To develop high-quality backlinks, you must produce worthwhile content that other websites will want to connect to. You can achieve this by writing guest posts or producing excellent content. Moreover, you can also market your content and draw backlinks using social media and other digital marketing platforms. 

6. Monitor SEO analytics 

SEO analytics refers to analyzing the data to understand your website’s performance. This is the most essential practice because it results from all the other practices. Monitoring your website’s analytics helps you understand how visitors interact with your site and what can be done to improvise it. You can implement that data in your decisions to optimize your website’s performance and improve your search engine rankings. 

In SEO analytics, you basically analyze the tracking traffic, bounce rate, and engagement metrics. When you monitor the analytics regularly, you can identify which pages and content are performing well and which are not and take measures to make improvements.  

Wrap up!

SEO best practices are essential for all business websites striving for increased traffic and online exposure. By following the methods highlighted in the blog, you can up your SEO game and increase your chances of reaching the top in search results.

Make sure to keep up with the most recent trends and best to maintain your website’s competitiveness in the ever-evolving digital landscape. You may achieve long-term success and benefit from a strong online presence with the appropriate strategy and constant effort. 


Data-driven marketing in 2023: The science behind data analysis and effective campaigns
Tom Koh
| June 21, 2023

Hello there, dear reader! It’s an absolute pleasure to have you here. Today, we’re embarking on a thrilling journey into the heart of data-driven marketing. Don’t worry, though; this isn’t your average marketing chat!

We’re delving into the very science that makes marketing tick. So, grab a cup of tea, sit back, and let’s unravel the fascinating ties between marketing Trust me, it’s going to be a real hoot! 

The art and science of marketing 

Isn’t it remarkable how marketing has evolved over the years? We’ve moved from straightforward newspaper adverts and radio jingles to a more complex, intricate world of digital marketing. It’s not just about catchy slogans and vibrant posters anymore.

No, no, marketing now is a careful blend of creativity, psychology, technology, and – you’ve guessed it: science. Marketing, you see, isn’t just an art; it’s a science. It involves careful experimentation, research, and above all, analysis.

Understanding data-driven marketing in 2023
Understanding data-driven marketing in 2023

We’re in a world brimming with data, and marketers are akin to modern-day alchemists. They skilfully transmute raw, overwhelming data into golden insights, driving powerful marketing strategies. 

And that, dear friends, is what we’re delving into today – the captivating world of data analysis in marketing. Exciting, isn’t it? Let’s forge ahead! 

The role of data analysis in marketing 

Data, dear reader, is the unsung hero of our digital age. It’s everywhere, and it’s valuable. In marketing, it’s like a crystal ball that shows trends, customer behaviors, campaign performance, and more. The trick, though, lies in making sense of this raw data, and that’s where data analysis sweeps in. 

Data analysis in marketing is like decoding a treasure map. It involves scrutinizing information to identify patterns, trends, and insights.

These insights then guide decision-making, inform strategies, and help evaluate the success of campaigns.

And it’s not just about retrospective analysis; predictive analytics can forecast future trends, helping businesses stay one step ahead. Quite incredible, wouldn’t you say? 

Understanding your audience: The heart of effective marketing 

No matter how innovative or creative your marketing strategies are, they’ll fall flat without a deep understanding of your audience. And guess what? Data analysis is the key to unlocking this understanding. 

Data analysis helps peel back the layers of your audience’s behaviours, preferences, and needs. It’s like having a conversation with your customers without them saying a word. You learn what makes them tick, what they love, and what they don’t. 

This level of understanding enables businesses to create highly targeted marketing campaigns that resonate with their audience. It’s all about delivering the right message, to the right people, at the right time. And it’s data analysis that helps nail this trifecta. 

The impact of data-driven marketing 

The magic of data-driven marketing lies in its power to deliver measurable, tangible results. It’s not just about casting a wide net and hoping for the best. Instead, it’s about making informed decisions based on real, credible data. 

When done right, data-driven marketing can skyrocket brand visibility, foster customer loyalty, and drive business growth. It’s a bit like having a secret weapon in the competitive business landscape. And who wouldn’t want that? 

Exciting future of data-driven marketing 

If you think data-driven marketing is impressive now, just wait until you see what the future holds! We’re looking at advanced artificial intelligence (AI) models, predictive analytics, and machine learning algorithms that can dive even deeper into data, delivering unprecedented insights. 

The future of marketing is not just reactive but proactive, not just personalized but hyper-personalized. It’s about predicting customer needs even before they arise, delivering a marketing experience that’s truly tailored and unique. 

Exciting times lie ahead, dear reader, and data analysis will be at the heart of it all. So, as we embrace this data-driven era, it’s essential to appreciate the remarkable science that underpins successful marketing.

After all, data analysis isn’t just a cog in the marketing machine; it’s the engine that drives it. And that, friends, is the power and promise of data-driven marketing. 

Diving deeper into data analysis 

So, you’re still with us? Fantastic! Now that we’ve skimmed the surface, it’s time to dive deeper into the wonderful ocean of data analysis. Let’s break down the types of data your business can leverage and the techniques to analyse them. Ready? Onwards we go! 

Types of data in marketing 

Data is like the language your customers use to speak to you, and there are different ‘dialects you need to be fluent in. Here are the primary types of data used in marketing: 

  • Demographic data: This type of data includes basic information about your customers such as age, gender, location, income, and occupation. It helps businesses understand who their customers are.
  • Psychographic data: This is a step deeper. It involves understanding your customers’ attitudes, interests, lifestyles, and values. It paints a picture of why your customers behave the way they do.
  • Behavioral data: This includes purchasing behaviors, product usage, and interactions with your brand. It gives you a peek into what your customers do.
  • Feedback data: This comes directly from your customers via reviews, surveys, and social media. It shows how your customers perceive your brand.

All these types of data, when analyzed and understood, provide rich, nuanced insights about your customer base. It’s like assembling a jigsaw puzzle where every piece of data adds more detail to the picture.   

Techniques in data analysis 

Now, let’s get our hands a little dirty and dig into some common techniques used in data analysis: 

  • Descriptive Analysis: This involves understanding past trends and behaviors. It answers the question, “What happened?”
  • Diagnostic Analysis: This dives deeper into why something happened. It’s like a post-mortem that helps identify the causes of a particular outcome.
  • Predictive Analysis: As the name suggests, this technique is all about forecasting future trends and behaviors based on past data.
  • Prescriptive Analysis: This is the most advanced form of data analysis. It suggests courses of action to take for future outcomes.

Using these techniques, marketers can transform raw data into actionable insights. It’s quite similar to a cook turning raw ingredients into a delicious meal!

Data analysis tools: The magic wand for marketers 

In our data-driven world, numerous tools help marketers analyze and interpret data. These tools are like magic wands, transforming data into visually appealing and easily understandable formats. 

  • Google Analytics: It provides insights into website traffic, user behaviors, and the performance of online marketing campaigns.
  • Tableau: It’s a visual analytics platform that transforms raw data into interactive, real-time dashboards.
  • Looker: It’s a business intelligence tool that delivers detailed insights about customer behaviors and business performance.
  • HubSpot: This is an all-in-one marketing tool that offers customer relationship management, social media management, content marketing, and, of course, data analytics.

These tools empower marketers to not only collect data but also interpret it, visualize it, and share insights across their teams. 

The Power of A/B Testing 

Now, here’s something particularly exciting! Have you ever found yourself torn between two options, unable to decide which is better? Well, in marketing, there’s a fantastic way to make that decision – A/B testing! 

A/B testing, also known as split testing, is a method to compare two versions of a web page, email, or other marketing asset to see which performs better. It’s a practical, straightforward way to test changes to your marketing campaigns before implementing them. 

For instance, if you’re not sure whether a green or a red button will drive more clicks on your website, simply test both versions. The one that garners more clicks wins! It’s that simple, and it’s all thanks to the science of data analysis. 

Bringing it all together 

So, there you have it! We’ve taken a whirlwind tour through the fascinating world of data-driven marketing. But, as they say, the proof of the pudding is in the eating.

So, it’s time for businesses to roll up their sleeves and embrace data analysis in their marketing. It’s time to unlock the powerful potential of data-driven marketing. 

Remember, in our digital age, data isn’t just a byproduct; it’s a vital strategic asset. So, here’s to harnessing the power of data analysis for more effective, efficient, and successful marketing campaigns. Cheers!  

Ovais Naseem
| June 19, 2023

Organizations must become skilled in navigating vast amounts of data to extract valuable insights and make data-driven decisions in the era of big data analytics. Amidst the buzz surrounding big data technologies, one thing remains constant: the use of Relational Database Management Systems (RDBMS). 

The foundation of data – RDBMS as the bedrock 

Imagine building a skyscraper without a solid foundation—it would crumble under its own weight.  Likewise, in big data, relational databases serve as the bedrock upon which the data infrastructure stands. Like a sturdy foundation, they provide a structured data storage, management, and querying environment, enabling efficient analysis and insights and guiding the decision-making process. 

Understanding big data analytics
Understanding big data analytics

Structured data – RDBMS handling the data deluge  

The growing volume of data becomes overwhelming for organizations, leaving them grappling with its sheer magnitude. Relational databases emerge as the solution, bringing order to the data deluge.  They are used to diligently catalog and organize information into tables, columns, and relationships. This structured approach enables data scientists and analysts to navigate the vast data landscape, extracting meaningful insights seamlessly. 

Horizontal and vertical scaling: Meeting growing demands 

RDBMS offers flexibility in scaling horizontally and vertically to accommodate the ever-growing needs of big data analytics. 

Scaling vertically involves adding more resources to a single server, such as increased processing power, memory, or storage capacity. This approach allows  to handle larger datasets and complex queries efficiently. 

In contrast, horizontal scaling involves distributing the workload across multiple servers or nodes, commonly known as clustering. This load balancing allows RDBMS to handle increased data volumes, enabling parallel processing and faster query execution. 

ACID Compliance:

ACID (Atomicity, Consistency, Isolation, Durability) is a group of database properties that ensure that the data remains valid even when mishaps, such as errors or failures, occur. Because data reliability and consistency are non-negotiable matters in the high-stakes game of big data analytics, RDBMS guarantee data transaction integrity by maintaining ACID properties.    

Therefore,   when real-time data ingestion and processing are paramount, ACID can prove to be a powerful ally in ensuring data reliability and consistency. 

Efficient Querying

An RDBMS is the key to unlocking insights through its robust querying capabilities. With Structured Query Language (SQL), these systems allow data analysts to zoom in, slice and dice data, perform complex joins, and uncover hidden patterns. RDBMS empowers organizations to extract value from their data, enabling better decision-making and strategic planning. 

RDBMS for Real-Time Analytics 

Businesses across industries leverage RDBMS for real-time analytics. For example, in the finance sector, banks employ relational databases to monitor transactions in real-time, detect fraudulent activities, and trigger immediate actions to mitigate risks. 

Similarly, the retail industry benefits from RDBMS to optimize inventory management and customer experiences. Imagine a large grocery store where shelves are stocked with perishable goods. An RDBMS serves as the inventory manager, monitoring stock levels and sales data. With real-time insights, the system triggers automatic reordering to maintain required stock levels, preventing both out-of-stock and overstocking situations, as well as high inventory costs. 

In online gaming, RDBMS enables real-time analytics to enhance the gaming experience. Game developers leverage RDBMS to capture and analyze player behavior, enabling personalized recommendations, in-game advertisements, and real-time matchmaking. This dynamic environment keeps players engaged, improving the gaming experience and driving user retention and revenue growth.  

Understanding big data analytics

Big data analytics refers to examining and extracting valuable insights from vast and complex datasets known as big data. It involves employing advanced analytical techniques, such as data mining, machine learning, and statistical analysis, to uncover patterns, trends, and correlations within the data.

The goal of big data analytics is to derive meaningful and actionable insights that can drive informed decision-making, optimize business processes, and gain a competitive edge. By harnessing the power of big data analytics, organizations can discover hidden opportunities, mitigate risks, enhance operational efficiency, and deliver personalized experiences to customers.


In the age of big data analytics, RDBMS remains a vital player, offering a solid foundation for organizations to navigate the boundless sea of data. The marriage of RDBMS with big data analytics paves the way for effective decision-making, allowing organizations to seize opportunities and respond swiftly to changing market conditions. 

Vipul Bhaibav
| May 8, 2023

Many people who operate internet businesses find the concept of big data to be rather unclear. They are aware that it exists, and they have been told that it may be helpful, but they do not know how to make it relevant to their company’s operations. Using small amounts of data at first is the most effective strategy to begin using big data. 

There is a need for meaningful data and insights in every single company organization, regardless of size. Big data plays a very crucial function in the process of gaining knowledge of your target audience as well as the preferences of your customers. It enables you to even predict their requirements. The appropriate data has to be provided in an understandable manner and thoroughly assessed. It is possible for a corporate organization to accomplish a variety of objectives with its assistance. 

Understanding Big Data
Understanding Big Data

Nowadays, you may choose from a plethora of Big Data organizations. However, selecting a firm that is able to provide Big Data services heavily relies on the requirements that you have.

Big Data Companies USA not only provide corporations with frameworks, computing facilities, and pre-packaged tools, but they also assist businesses in scaling with cloud-based big data solutions. They provide assistance to organizations in determining their big data strategy and consulting services on how to improve company performance by revealing the potential of data. 

Big data has the potential to open up many new opportunities for business expansion. It offers the below ideas. 

Competence in certain areas 

You can be a start-up company with an idea or an established company with a defined solution roadmap. And the primary focus of your efforts should be directed around identifying the appropriate business that can materialize either your concept or the POC. The amount of expertise that the data engineers have, as well as the technological foundation they come from, should be the top priorities when selecting a firm. 

Development team 

Getting your development team and the Big Data service provider on the same page is one of the many benefits of forming a partnership with a Big Data service provider. These individuals have to be really imaginative and forward-thinking, in a position to comprehend your requirements and to be able to provide even more advantageous choices. You may be able to assemble the most talented group of people, but the collaboration won’t bear fruit until everyone on the team shares your perspective on the project. After you have determined that the team members’ hard talents meet your criteria, you may find that it is necessary to examine the soft skills that they possess. 

Cost and placement considerations 

The geographical location of the organization and the total cost of the project are two other elements that might have an effect on the software development process. For instance, you may decide to go with in-house development services, but keep in mind that these kinds of services are almost usually more expensive.

It’s possible that rather than getting the complete team, you’ll wind up with only two or three engineers that can work within your financial constraints. But why should one pay extra for a lower-quality result? When outsourcing your development team, choose a nation that is located in a time zone that is most convenient for you. 


In today’s business world, feedback is the most important factor in determining which organizations come out on top. Find out what other people think about the firm you’d want to associate with so that you may avoid any unpleasant surprises. Using these online resources will be of great assistance to you in arriving at a conclusion. 

What role does big data play in businesses across different industries?

Among the most prominent sectors now using big data solutions are the retail and financial sectors, followed by e-commerce, manufacturing, and telecommunications. When it comes to streamlining their operations and better managing their data flow, business owners are increasingly investing in big data solutions. Big data solutions are becoming more popular among vendors as a means of improving supply chain management. 

  • In the financial industry, it can be used to detect fraud, manage risk, and identify new market opportunities.
  • In the retail industry, it can be used to analyze consumer behavior and preferences, leading to more targeted marketing strategies and improved customer experiences.
  • In the manufacturing industry, it can be used to optimize supply chain management and improve operational efficiency.
  • In the energy industry, it can be used to monitor and manage power grids, leading to more reliable and efficient energy distribution.
  • In the transportation industry, it can be used to optimize routes, reduce congestion, and improve safety.

Bottom line

Big data, which refers to extensive volumes of historical data, facilitates the identification of important patterns and the formation of more sound judgments. Big data is having an effect on our marketing strategy as well as affecting the way we operate at this point in time. Big data analytics are being put to use by governments, businesses, research institutions, IT subcontractors, and teams in an effort to delve more deeply into the mountains of data and, as a result, come to more informed conclusions. 



Stay ahead of the curve with these 12 powerful GitHub repositories for learning data science, analytics, and engineering
Ali Haider Shalwani
| April 27, 2023

This blog lists down-trending data science, analytics, and engineering GitHub repositories that can help you with learning data science to build your own portfolio.  

What is GitHub?

GitHub is a powerful platform for data scientists, data analysts, data engineers, Python and R developers, and more. It is an excellent resource for beginners who are just starting with data science, analytics, and engineering. There are thousands of open-source repositories available on GitHub that provide code examples, datasets, and tutorials to help you get started with your projects.  

This blog lists some useful GitHub repositories that will not only help you learn new concepts but also save you time by providing pre-built code and tools that you can customize to fit your needs. 

Want to get started with data science? Do check out ourData Science Bootcamp as it can navigate your way!  

Best GitHub repositories to stay ahead of the tech Curve

With GitHub, you can easily collaborate with others, share your code, and build a portfolio of projects that showcase your skills.  

Trending GitHub Repositories
Trending GitHub Repositories
  1. Scikit-learn: A Python library for machine learning built on top of NumPy, SciPy, and matplotlib. It provides a range of algorithms for classification, regression, clustering, and more.  

Link to the repository: https://github.com/scikit-learn/scikit-learn 

  1. TensorFlow: An open-source machine learning library developed by Google Brain Team. TensorFlow is used for numerical computation using data flow graphs.  

Link to the repository: https://github.com/tensorflow/tensorflow 

  1. Keras: A deep learning library for Python that provides a user-friendly interface for building neural networks. It can run on top of TensorFlow, Theano, or CNTK.  

Link to the repository: https://github.com/keras-team/keras 

  1. Pandas: A Python library for data manipulation and analysis. It provides a range of data structures for efficient data handling and analysis.  

Link to the repository: https://github.com/pandas-dev/pandas 

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  1. PyTorch: An open-source machine learning library developed by Facebook’s AI research group. PyTorch provides tensor computation and deep neural networks on a GPU.  

Link to the repository: https://github.com/pytorch/pytorch 

  1. Apache Spark: An open-source distributed computing system used for big data processing. It can be used with a range of programming languages such as Python, R, and Java.  

Link to the repository: https://github.com/apache/spark 

  1. FastAPI: A modern web framework for building APIs with Python. It is designed for high performance, asynchronous programming, and easy integration with other libraries.  

Link to the repository: https://github.com/tiangolo/fastapi 

  1. Dask: A flexible parallel computing library for analytic computing in Python. It provides dynamic task scheduling and efficient memory management.  

Link to the repository: https://github.com/dask/dask 

  1. Matplotlib: A Python plotting library that provides a range of 2D plotting features. It can be used for creating interactive visualizations, animations, and more.  

Link to the repository: https://github.com/matplotlib/matplotlib


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  1. Seaborn: A Python data visualization library based on matplotlib. It provides a range of statistical graphics and visualization tools.  

Link to the repository: https://github.com/mwaskom/seaborn

  1. NumPy: A Python library for numerical computing that provides a range of array and matrix operations. It is used extensively in scientific computing and data analysis.  

Link to the repository: https://github.com/numpy/numpy 

  1. Tidyverse: A collection of R packages for data manipulation, visualization, and analysis. It includes popular packages such as ggplot2, dplyr, and tidyr. 

Link to the repository: https://github.com/tidyverse/tidyverse 

In a nutshell

In conclusion, GitHub is a valuable resource for developers, data scientists, and engineers who are looking to stay ahead of the technology curve. With the vast number of repositories available, it can be overwhelming to find the ones that are most useful and relevant to your interests. The repositories we have highlighted in this blog cover a range of topics, from machine learning and deep learning to data visualization and programming languages. By exploring these repositories, you can gain new skills, learn best practices, and stay up-to-date with the latest developments in the field.

Do you happen to have any others in mind? Please feel free to share them in the comments section below!  


Unleashing success: Mastering the 10 must-have skills for data analysts in 2023
Ruhma Khawaja
| April 18, 2023

Are you interested in learning more about the essential skills for data analysts to succeed in today’s data-driven world?

You are in luck if you have a knack for working with numbers and handling datasets. The good news is that you don’t need to be an engineer, scientist, or programmer to acquire the necessary data analysis skills. Whether you’re located anywhere in the world or belong to any profession, you can still develop the expertise needed to be a skilled data analyst.

Who are data analysts?

Data analysts are professionals who use data to identify patterns, trends, and insights that help organizations make informed decisions. They collect, clean, organize, and analyze data to provide valuable insights to business leaders, enabling them to make data-driven decisions.

The profession of data analysis is gaining momentum for several reasons. First, the amount of data available to organizations has grown exponentially in recent years, creating a need for professionals who can make sense of it. Second, advancements in technology, such as big data and machine learning, have made it easier and more efficient to analyze data. Finally, businesses are realizing the importance of making data-driven decisions to remain competitive in today’s market.

As we move further into the age of data-driven decision-making, the role of the data analyst continues to evolve and expand. In 2023, data analysts will be expected to have a wide range of skills and knowledge to be effective in their roles.

Skills for data analysts 2023
Skills for data analysts 2023

10 essential skills for data analysts to have in 2023

Here are 10 essential skills for data analysts to have in 2023: 

1. Data Visualization: 

Topping the list of skills for data analysts data visualization stands first. Data visualization is the process of presenting data in a visual format such as charts, graphs, or maps. Data analysts need to be able to effectively communicate their findings through visual representations of data.

They should be proficient in using tools like Tableau, PowerBI, or Python libraries like Matplotlib and Seaborn to create visually appealing and informative dashboards. Data analysts should also understand design principles such as color theory and visual hierarchy to create effective visualizations. Effective data visualization allows stakeholders to quickly understand complex data and draw actionable insights from it. 

2. Programming 

Programming is a crucial skill for data analysts. They should be proficient in languages like Python, R or SQL to effectively analyze data and create custom scripts to automate data processing and analysis. Data analysts should be able to manipulate data using programming constructs such as loops, conditional statements, and functions.

They should also be familiar with data structures such as arrays and lists, and be able to use libraries and packages such as NumPy, Pandas, or dplyr to process and manipulate data. In the skills for data analyst list, programming skills are essential since they enable data analysts to create automated workflows that can process large volumes of data quickly and efficiently, freeing up time to focus on higher-value tasks such as data modeling and visualization. 

3. Statistics 

Possessing the right skills for data analysts is essential for success in this field. A strong foundation in statistics is crucial to apply statistical methods and models to analysis, including concepts like hypothesis testing, regression, and clustering analysis.

In addition, data analysts must have a thorough understanding of probability and statistics to identify patterns in data, eliminate biases and logical errors, and generate accurate results. These abilities are critical to becoming a skilled data analyst and making informed decisions based on data analysis.

4. Data cleaning and preparation 

Data cleaning and preparation is the process of transforming raw data into a format that is suitable for analysis. This involves identifying and correcting errors, removing duplicates, handling missing values, and restructuring data.

Data analysts should be proficient in using tools like Excel, OpenRefine or Python libraries like Pandas to clean and preprocess data. They should be able to identify patterns and outliers in data and use their knowledge of statistical analysis to handle them appropriately. In addition, they should be able to create automated data-cleaning pipelines to ensure data is clean and consistent for future analysis. 

5. Data modeling 

Data modeling is the process of creating a conceptual representation of data and its relationships to support business decisions. This involves creating models that can be used to predict future outcomes based on historical data. Data analysts should have a strong understanding of concepts such as classification, regression, and time-series analysis.

They should be able to choose the appropriate model for a specific problem and evaluate the performance of the model. Data analysts should also have the ability to implement models using tools like Python’s sci-kit-learn library, R’s caret package, or IBM SPSS. 

6. Data security 

Data security is the process of protecting data from unauthorized access, use, disclosure, disruption, modification, or destruction. Data analysts should have a strong understanding of data security and privacy to ensure the data they work with is secure and compliant with regulations such as GDPR, CCPA, or HIPAA. They should be able to identify potential security risks and take measures to mitigate them.

This may include using encryption techniques to protect sensitive data, implementing access controls to restrict access to data, and ensuring that data is stored and transmitted securely. Additionally, data analysts should be familiar with legal and ethical issues surrounding data privacy and be able to ensure compliance with relevant laws and regulations.  

7. Communication 

Data analysts should be able to communicate their findings in a clear and concise manner to non-technical stakeholders. They should be able to translate complex data insights into actionable insights for decision-makers. 

8. Critical thinking 

Data analysts should have strong critical thinking skills to be able to analyze and interpret data to identify trends and patterns that may not be immediately apparent. 

9. Business acumen 

Data analysts should have a strong understanding of the business domain they work in to be able to effectively apply data analysis to business problems and make data-driven decisions. 

10. Continuous learning 

Data analysts should be committed to continuous learning and staying up-to-date with new tools, techniques, and technologies. They should be willing to invest time and effort into learning new skills and technologies to stay competitive. 

Are you ready to level up your skillset? 

In conclusion, data analysts in 2023 will need to have a diverse skill set that includes technical, business, and soft skills. They should be proficient in data visualization, programming, statistics, data modeling, and data cleaning and preparation. In addition, they should have strong communication, critical thinking, and business acumen skills.

Finally, they should be committed to continuous learning and staying up-to-date with new tools and technologies. By developing these skills, data analysts can add value to their organizations and stay competitive in the job market. 

Claudia Jeffrey
| April 10, 2023

Data mining has become increasingly crucial in today’s digital age, as the amount of data generated continues to skyrocket. In fact, it’s estimated that by 2025, the world will generate 463 exabytes of data every day, which is equivalent to 212,765,957 DVDs per day! With such an overwhelming amount of data, data mining has become an essential process for businesses and organizations to extract valuable insights and make data-driven decisions. 

According to a recent survey, 97% of organizations are now investing in data mining and analytics, recognizing the importance of this field in driving business success. However, for beginners, navigating the world of data mining can be challenging, with so many tools and techniques to choose from. 

To help beginners get started, we’ve compiled a list of ten data mining tips. From starting with small datasets to staying up-to-date with the latest trends, these tips can help beginners make sense of the world of data mining and harness the power of their data to drive business success. 

Data Mining
Data Mining – Image Source 

Importance of data mining  

Data mining is a crucial process that allows organizations to extract valuable insights from large datasets. By understanding their data, businesses can optimize their operations, reduce costs, and make data-driven decisions that can lead to long-term success. Let’s have a look at some points referring to why data mining is really essential. 

  • It allows organizations to extract valuable insights and knowledge from large datasets, which can drive business success.
  • By analyzing data, organizations can identify trends, patterns, and relationships that might be otherwise invisible to the human eye.
  • It can help organizations make data-driven decisions, allowing them to respond quickly to changes in their industry and gain a competitive edge.
  • Data mining can help businesses identify customer behavior and preferences, allowing them to tailor their marketing strategies to their target audience and improve customer satisfaction.
  • By understanding their data, businesses can optimize their operations, streamline processes, and reduce costs. 
  • It can be used to identify fraud and detect security breaches, helping to protect organizations and their customers. 
  • It can be used in healthcare to improve patient outcomes and identify potential health risks. 
  • Data mining can help governments identify areas of concern, allocate resources, and make informed policy decisions. 
  • It can be used in scientific research to identify patterns and relationships that might be otherwise impossible to detect. 
  • With the growth of the Internet of things (IoT) and the massive amounts of data generated by connected devices, data mining has become even more critical in today’s world. 

    Overall, it is a vital tool for organizations across all industries. By harnessing the power of their data, businesses can gain insights, optimize operations, and make data-driven decisions that can lead to long-term success. 

Data mining techniques and tips for beginners 

Now, without any further ado, let’s move toward some tips and techniques that can help you with data mining.

1. Start with small datasets

When starting with data mining, it’s best to begin with small datasets. Small datasets are beneficial for beginners because they are easy to manage, and they can be used to practice and experiment with various data mining techniques. When selecting a small dataset, it’s essential to choose one that is relevant to your field of interest and contains the necessary features for your analysis.

2. Choose the right tool

Data Mining tools
Data Mining Tools – Image Source  

There are several data mining tools available in the market, each with its strengths and weaknesses. As a beginner, it’s crucial to choose the right tool that matches your needs and skills. Some popular data mining tools include R, Python, and Weka. Consider factors such as ease of use, learning curve, and compatibility with your dataset when selecting a tool. 

Understand your data 

Before you can start data mining, it’s essential to understand your data. This includes knowing the data types and structures, exploring and visualizing the data, and identifying any missing values, outliers, or duplicates. By understanding your data, you can ensure that your analysis is accurate and reliable.

1. Preprocessing your data

Data preprocessing involves cleaning and transforming your data before analyzing it. It’s essential to handle missing values, outliers, and duplicates to prevent biased results. There are several preprocessing techniques available, such as normalization, discretization, and feature scaling. Choose the appropriate technique based on your dataset and analysis needs.

2. Selecting the right algorithm

There are several data mining algorithms available, each with its strengths and weaknesses. When selecting an algorithm, consider factors such as the size and type of your dataset, the problem you’re trying to solve, and the computational resources available. This is similar as you consider many factors while you pay someone for essay, which may include referencing, evidence-based argument, cohesiveness, etc. In data mining, popular algorithms include decision trees, support vector machines, and k-means clustering.

3. Feature engineering

Feature engineering involves selecting the right features that are relevant to your analysis. It’s essential to choose the appropriate features to prevent overfitting or underfitting your model. Some feature selection and extraction techniques include principal component analysis, feature selection by correlation, and forward feature selection. 

Model evaluation and validation 

Once you’ve selected an algorithm and built a model, it’s essential to evaluate and validate its performance. Model evaluation and validation involve measuring the accuracy, precision, recall, and other performance metrics of your model. Choose the appropriate evaluation metric based on your analysis needs. 

Hyperparameter tuning 

Hyperparameters are parameters that cannot be learned from the data and must be set before training the model. Hyperparameter tuning involves optimizing these parameters to improve the performance of your model. Consider factors such as the learning rate, regularization, and the number of hidden layers when tuning hyperparameters.

1. Stay up-to-date with data mining trends

Data mining is a rapidly evolving field, with new trends and techniques emerging regularly. It’s crucial to stay up-to-date with the latest trends by attending conferences, reading research papers, and following experts in the field. This will help you stay relevant and improve your skills.

2. Practice and experimentation

Like any other skill, it requires practice and experimentation to master. Experiment with different datasets, algorithms, and techniques to improve your skills and gain more experience. The practice also helps you identify common pitfalls and avoid making the same mistakes in the future. 

While summing up… 

In conclusion, data mining is a powerful tool that can help businesses and organizations extract valuable insights from their data. For beginners, it can seem daunting to dive into the world of data mining, but by following the tips outlined in this blog post, they can start their journey on the right foot. 

Starting with small datasets, choosing the right tool, understanding and preprocessing data, selecting the right algorithm, feature engineering, model evaluation and validation, hyperparameter tuning, staying up-to-date with trends, and practicing and experimenting are all crucial steps in the data mining process. 

Remember, it is an ongoing learning process, and as technology and techniques evolve, so must your skills and knowledge. By continuously improving and staying up-to-date with the latest trends and tools, beginners can become proficient in data mining and extract valuable insights from their data to drive business success.

Ahmad Benny
| March 22, 2023

The COVID-19 pandemic threw businesses into uncharted waters. Suddenly, digital transformation was more important than ever, and companies had to pivot quickly or risk extinction. And the humble QR code – once dismissed as a relic of the past – became an unlikely hero in this story. 

QR tech’s versatility and convenience allowed businesses, both large and small, to stay afloat amid challenging circumstances and even inspired some impressive growth along the way. But the real magic happened when data analytics was added to the mix. 


You see, when QR code was paired with data analytics, companies could see the impact of their actions in real-time. They were able to track customer engagement, spot trends, and get precious new insights into their customers’ preferences. This newfound knowledge enabled companies to create superior strategies, refine their campaigns, and more accurately target their audience.  

The result? Faster growth that’s both measurable and sustainable. Read on to find out how you, too, can use data analytics and QR codes to supercharge your business growth. 

Why use QR codes to track data? 

Did you ever put in a lot of effort and time to craft the perfect marketing campaign only to be left wondering how effective it was? How many people viewed it, how many responded, and what was the return on investment?  

Before, tracking offline campaigns’ MROI (Marketing Return on Investment) was an inconvenient and time-consuming process. Businesses used to rely on coupon codes and traditional media or surveys to measure campaign success.

For example, say you put up a billboard ad. Now without any coupon codes or asking people how they found out about you, it was almost impossible to know if someone had even seen the ad, let alone acted on it. But the game changed when data tracking enabled QR codes came in.

Adding these nifty pieces of technology to your offline campaigns allows you to collect valuable data and track customer behavior. All the customers have to do is scan your code, which will take them to a webpage or a landing page of your choosing. In the process, you’ll capture not only first-party data from your audience but also valuable insights into the success of your campaigns. 

For instance, if you have installed the same billboard campaign in two different locations, a QR code analytics dashboard can help you compare the results to determine which one is more effective. Say 2000 people scanned the code in location A, while only 500 scanned it in location B. That’s valuable intel you can use to adjust your strategy and ensure all your offline campaigns perform at their best. 

How does data analytics fit in the picture? 

Once you’ve employed QR codes and started tracking your campaigns, it’s time to play your trump card – analytics. 

Extracting wisdom from your data is what turns your campaigns from good to great. Analytics tools can help you dig deep into the numbers, find correlations and uncover insights to help you optimize your campaigns and boost conversions. 

For example, using trackable codes, you can find out the number of scans. But adding analytics tools to the mix can reveal how long users interacted with the content after scanning your code, what locations yielded the most scans, and more.

This transforms your data from merely informative to actionable. And arming yourself with these kinds of powerful insights will go a long way in helping you make smarter decisions and accelerate your growth. 

Getting started with QR code analytics 

Ready to start leveraging the power of QR codes and analytics? Here’s a step-by-step guide to getting started: 

Step 1: Evaluate QR codes’ suitability for your strategy 

Before you begin, ask yourself if a QR code project is actually in line with your current resource capacity and target audience. If you’re trying to target a tech-savvy group of millennials who lead busy lives, they could be the perfect solution. But it may not be the best choice if you’re aiming for an older demographic who may struggle with technology.  

Plus, keep in mind that you’ll also need dedicated resources to continually track and manage your project and the data it’ll yield. As such, make certain you have the right resource support lined up before diving in. 

Step 2: Get yourself a solid QR code generator 

The next step is to find a reliable and feature-rich QR code generator. A good one should allow you to customize your codes, track scans, and easily integrate with your other analytics tools. The internet is full of such QR code generators, so do your research, read reviews, and pick the best one that meets your needs. 

Step 3: Choose your QR code type 

QR codes come in two major types:  

  1. Static QR codes – They are the most basic type of code that points to a single, predefined destination URL and don’t allow for any data tracking.  
  2. Dynamic/ trackable QR codes – These are the codes we’ve been talking about. They are far more sophisticated as they allow you to track and measure scans, collect vital data points, and even change the destination URL on the fly if needed.

For the purpose of analytics, you will have to opt for dynamic /trackable QR codes. 

Step 4: Design and generate QR code

Now that you have your QR code generator and type sorted, you can start with the QR code creation process. Depending on the generator you picked, this can take a few clicks or involve a bit of coding.

But be sure to dress up your QR codes with your brand colors and an enticing call to action to encourage scans. A visually appealing code will be far more likely to pique people’s interest and encourage them to take action than a dull, black-and-white one. 

Step 5: Download and print out the QR code 

Once you have your code ready, save it and print it out. But before printing a big batch of copies to use in your campaigns, test your code to ensure it works as expected. Scan it from different devices and check the destination URL to verify everything is good before moving ahead with your campaign. 

Step 6: Start analyzing the data 

Most good QR code generators come with built-in analytics or allow you to integrate with popular tools like Google Analytics. So you can either go with the integrated analytics or hook up your code with your analytics tool of choice. 

Industry use cases using QR codes and analytics 

QR codes, when combined with analytics tools, can be incredibly powerful in driving business growth. Let’s look at some use cases that demonstrate the potential of this dynamic duo. 

1. Real estate – Real estate agents can use QR codes to give potential buyers a virtual tour of their properties. This tech can also be used to provide comprehensive information about the property, like floor plans and features. Furthermore, with analytics integration, real estate agents can track how many people access property information and view demographic data to better understand each property’s target market.  

2. Coaching/ Mentorship – A coaching business can use QR codes to target potential clients and measure the effectiveness of their coaching materials. For example, coaches could test different versions of their materials and track how many people scanned each QR code to determine which version resonated best with their target audience. Statistics derived from this method will let them refine their materials, hike up engagement and create a higher-end curriculum. 

3. Retail – They are an excellent way for retailers to engage customers in their stores and get detailed metrics on their shopping behavior. Retailers can create links to product pages, add loyalty programs and coupons, or offer discounts on future purchases. All these activities can be tracked using analytics, so retailers can understand customer preferences and tailor their promotions accordingly. 

QR codes and data analytics: A dynamic partnership

No longer confined to the sidelines, tech’s newfound usage has propelled it to the forefront of modern marketing and technology. By combining codes with analytics tools, you can unlock boundless opportunities to streamline processes, engage customers and drive your business further. This tried-and-true, powerful partnership is the best way to move your company digitally forward. 

Prasad D Wilagama
| March 17, 2023

In today’s digital age, with a plethora of tools available at our fingertips, researchers can now collect and analyze data with greater ease and efficiency. These research tools not only save time but also provide more accurate and reliable results. In this blog post, we will explore some of the essential research tools that every researcher should have in their toolkit.

From data collection to data analysis and presentation, this blog will cover it all. So, if you’re a researcher looking to streamline your work and improve your results, keep reading to discover the must-have tools for research success.

Revolutionize your research: The top 20 must-have research tools

Research requires various tools to collect, analyze and disseminate information effectively. Some essential research tools include search engines like Google Scholar, JSTOR, and PubMed, reference management software like Zotero, Mendeley, and EndNote, statistical analysis tools like SPSS, R, and Stata, writing tools like Microsoft Word and Grammarly, and data visualization tools like Tableau and Excel.  

Essential Research Tools for Researchers

1. Google Scholar – Google Scholar is a search engine for scholarly literature, including articles, theses, books, and conference papers.

2. JSTOR – JSTOR is a digital library of academic journals, books, and primary sources.

3.PubMedPubMed is a free search engine accessing primarily the MEDLINE database of references and abstracts on life sciences and biomedical topics. 

4. Web of Science: Web of Science is a citation index that allows you to search for articles, conference proceedings, and books across various scientific disciplines. 

5. Scopus – Scopus citation database that covers scientific, technical, medical, and social sciences literature. 

6. Zotero: Zotero is a free, open-source citation management tool that helps you organize your research sources, create bibliographies, and collaborate with others.

7. Mendeley – Mendeley is a reference management software that allows you to organize and share your research papers and collaborate with others.

8. EndNote – EndNoted is a software tool for managing bibliographies, citations, and references on the Windows and macOS operating systems. 

9. RefWorks – RefWorks is a web-based reference management tool that allows you to create and organize a personal database of references and generate citations and bibliographies.

10. Evernote – Evernote is a digital notebook that allows you to capture and organize your research notes, web clippings, and documents.

11. SPSS – SPSS is a statistical software package used for data analysis, data mining, and forecasting.

12. R – R is a free, open-source software environment for statistical computing and graphics.

13. Stata – Stata is a statistical software package that provides a suite of applications for data management and statistical analysis.

Other helpful tools for collaboration and organization include NVivo, Slack, Zoom, and Microsoft Teams. With these tools, researchers can effectively find relevant literature, manage references, analyze data, write research papers, create visual representations of data, and collaborate with peers. 

14. Excel – Excel is spreadsheet software used for organizing, analyzing, and presenting data.

15. Tableau – Tableau is a data visualization software that allows you to create interactive visualizations and dashboards.

16. NVivo – Nviva is a software tool for qualitative research and data analysis.

17. Slack – Slack is a messaging platform for team communication and collaboration.

18. Zoom – Zoom is a video conferencing software that allows you to conduct virtual meetings and webinars.

19. Microsoft Teams – Microsoft Teams is a collaboration platform that allows you to chat, share files, and collaborate with your team.

20. Qualtrics – Qualtrics is an online survey platform that allows researchers to design and distribute surveys, collect and analyze data, and generate reports.

Maximizing accuracy and efficiency with research tools

Research is a vital aspect of any academic discipline, and it is critical to have access to appropriate research tools to facilitate the research process. Researchers require access to various research tools and software to conduct research, analyze data, and report research findings. Some standard research tools researchers use include search engines, reference management software, statistical analysis tools, writing tools, and data visualization tools.

Specialized research tools are also available for researchers in specific fields, such as GIS software for geographers and geneticist gene sequence analysis tools. These tools help researchers organize data, collaborate with peers, and effectively present research findings.

It is crucial for researchers to choose the right tools for their research project, as these tools can significantly impact the accuracy and reliability of research findings.


Summing it up, researchers today have access to an array of essential research tools that can help simplify the research process. From data collection to analysis and presentation, these tools make research more accessible, efficient, and accurate. By leveraging these tools, researchers can improve their work and produce more high-quality research.

Hamza Mannan Samad
| March 14, 2023

In today’s data-driven world, businesses are constantly collecting and analyzing vast amounts of information to gain insights and make informed decisions. However, traditional methods of data analysis are often insufficient to fully capture the complexity of modern data sets. This is where graph analytics comes in.

One might say that the difference between data and graph analytics is like a movie script and a movie itself – but that is not entirely accurate. It can be compared to a movie that tells a story, while analytics is akin to the script that guides the movie’s plot. In contrast, data itself can be likened to a jumbled set of words, much like an incomplete puzzle that traditional methods cannot piece together.

What is graph analytics?

Enter graph analytics – the ultimate tool for uncovering hidden connections and patterns in your data.  

Have you ever wondered how to make sense of the overwhelming amount of data that surrounds us? It is a game-changing tool/technology that allows us to uncover patterns and connections in data that traditional methods can’t reveal. It is a way of analyzing data that is organized in a graph structure, where data is represented as nodes (vertices), and the relationships between them are represented as edges.

How graph analytics are better for handling complex data sets?

And let’s not forget, it is also great at handling large and complex data sets. It’s like having a supercomputer at your fingertips. Imagine trying to analyze a social network with traditional methods, it would be like trying to count the stars in the sky with your bare eyes. But with graph analytics, it’s like having a telescope to zoom in on the stars. 

Furthermore, graph analytics also provides a valuable addition to current machine-learning approaches. By adding graph-based features to a machine learning model, data scientists can achieve even better performance, which is a great way to leverage graph analytics for data science professionals. 

Explanation of graph structure in data representation

It is a powerful tool for data representation and analysis. It allows data to be represented as a network of nodes and edges, also known as a graph. The nodes in the graph represent entities or objects, while the edges represent the relationships or connections between them. This structure makes it easier to visualize and understand complex relationships between data points.

Comparison to traditional methods of data analysis

Without graph analytics, a data scientist’s life would be like trying to solve a jigsaw puzzle with missing pieces. Sure, you can still see the big picture, but it’s not quite complete.

Traditional methods such as statistical analysis and machine learning can only get you so far in uncovering the hidden insights in your data. It’s like trying to put together a puzzle with only half the pieces but with graph analytics, it’s like finding the missing pieces to the puzzle. It allows you to see the connections and patterns in your data that you never knew existed. 

Insights from industry experts on real-world applications

In our webinar, “Introduction to Graph Analytics,” attendees learned from industry experts Griffin Marge and Scott Heath as they shared insights on the power of graph analytics and discovered how one can begin to leverage it in their own work.

During the introductory session, a comprehensive overview of GraphDB was provided, highlighting its unique features and the ideal use cases for graph technology. Following this, the session focused on the specific use case of fraud detection and featured a demonstration of a potential graph-based solution.


Summing it all up, this talk will help you in understanding how graph analytics is being used today by some of the world’s most innovative organizations. So, don’t miss out on this opportunity to expand your data analysis skills and gain a competitive edge.


All in all, graph analytics is a powerful tool for unlocking insights in large and complex data sets that traditional methods of data analysis cannot fully capture. By representing data as a graph structure with nodes and edges, graph analytics allows for a more comprehensive understanding of relationships between data points. If you want to expand your data analysis skills and stay ahead of the curve, graph analytics is a must-have tool in your arsenal.

Top 5 data analytics conferences to attend in 2023 – Get ready to connect with the best in business
Ruhma Khawaja
| March 2, 2023

Data analytics is the driving force behind innovation, and staying ahead of the curve has never been more critical. That is why we have scoured the landscape to bring you the crème de la crème of data analytics conferences in 2023.  

Data analytics conferences provide an essential platform for professionals and enthusiasts to stay current on the latest developments and trends in the field. By attending these conferences, attendees can gain new insights, and enhance their skills in data analytics.

These events bring together experts, practitioners, and thought leaders from various industries and backgrounds to share their experiences and best practices. Such conferences also provide an opportunity to network with peers and make new connections.  

Data analytics conferences to look forward to

In 2023, there will be several conferences dedicated to this field, where experts from around the world will come together to share their knowledge and insights. In this blog, we will dive into the top data analytics conferences of 2023 that data professionals and enthusiasts should add to their calendars.

Top Data Analytics Conferences in 2023
      Top Data Analytics Conferences in 2023 – Data Science Dojo

Strata Data Conference   

The Strata Data Conference is one of the largest and most comprehensive data conferences in the world. It is organized by O’Reilly Media and will take place in San Francisco, CA in 2023. It is a leading event in data analytics and technology, focusing on data and AI to drive business value and innovation. The conference brings together professionals from various industries, including finance, healthcare, retail, and technology, to discuss the latest trends, challenges, and solutions in the field of data analytics.   

This conference will bring together some of the leading data scientists, engineers, and executives from across the world to discuss the latest trends, technologies, and challenges in data analytics. The conference will cover a wide range of topics, including artificial intelligence, machine learning, big data, cloud computing, and more. 

Big Data & Analytics Innovation Summit  

The Big Data & Analytics Innovation Summit is a premier conference that brings together experts from various industries to discuss the latest trends, challenges, and solutions in data analytics. The conference will take place in London, England in 2023 and will feature keynotes, panel discussions, and hands-on workshops focused on topics such as machine learning, artificial intelligence, data management, and more.  

Attendees can attend keynote speeches, technical sessions, and interactive workshops, where they can learn about the latest technologies and techniques for collecting, processing, and analyzing big data to drive business outcomes and make informed decisions. The connection between the Big Data & Analytics Innovation Summit and data analytics lies in its focus on the importance of big data and the impact it has on businesses and industries. 

Predictive Analytics World   

Predictive Analytics World is among the leading data analytics conferences that focus specifically on the applications of predictive analytics. It will take place in Las Vegas, NV in 2023. Attendees will learn about the latest trends, technologies, and solutions in predictive analytics and gain valuable insights into this field’s future.  

At PAW, attendees can learn about the latest advances in predictive analytics, including techniques for data collection, data preprocessing, model selection, and model evaluation. For the unversed, Predictive analytics is a branch of data analytics that uses historical data, statistical algorithms, and machine learning techniques to make predictions about future events. 

AI World Conference & Expo   

The AI World Conference & Expo is a leading conference focused on artificial intelligence and its applications in various industries. The conference will take place in Boston, MA in 2023 and will feature keynote speeches, panel discussions, and hands-on workshops from leading AI experts, business leaders, and data scientists. Attendees will learn about the latest trends, technologies, and solutions in AI and gain valuable insights into this field’s future.  

The connection between the AI World Conference & Expo and data analytics lies in its focus on the importance of AI and data in driving business value and innovation. It highlights the significance of AI and data in enhancing business value and innovation. The event offers attendees an opportunity to learn from leading experts in the field, connect with other professionals, and stay informed about the most recent developments in AI and data analytics. 

Data Science Summit   

Last on the data analytics conference list we have the Data Science Summit. It is a premier conference focused on data science applications in various industries. The meeting will take place in San Diego, CA in 2023 and feature keynote speeches, panel discussions, and hands-on workshops from leading data scientists, business leaders, and industry experts. Attendees will learn about the latest trends, technologies, and solutions in data science and gain valuable insights into this field’s future.  

Special mention – Future of Data and AI

Hosted by Data Science Dojo, Future of Data and AI is an unparalleled opportunity to connect with top industry leaders and stay at the forefront of the latest advancements. Featuring 20+ industry experts, the two-day virtual conference offers a diverse range of expert-level knowledge and training opportunities.

Don’t worry if you missed out on the Future of Data and AI Conference! You can still catch all the amazing insights and knowledge from industry experts by watching the conference on YouTube.

Bottom line

In conclusion, the world of data analytics is constantly evolving, and it is crucial for professionals to stay updated on the latest trends and developments in the field. Attending conferences is one of the most effective ways to stay ahead of the game and enhance your knowledge and skills.  

The 2023 data analytics conferences listed in this blog are some of the most highly regarded events in the industry, bringing together experts and practitioners from all over the world. Whether you are a seasoned data analyst, a new entrant in the field, or simply looking to expand your network, these conferences offer a wealth of opportunities to learn, network, and grow.

So, start planning and get ready to attend one of these top conferences in 2023 to stay ahead of the curve. 


Master Facebook scraping with Python: Tips, tricks, and tools you must know
Manthan Koolwal
| February 27, 2023

These days social platforms are quite popular. Websites like YouTube, Facebook, Instagram, etc. are used widely by billions of people.  These websites have a lot of data that can be used for sentiment analysis against any incident, election prediction, result prediction of any big event, etc. If you have this data, you can analyze the risk of any decision.

In this post, we are going to web-scrape public Facebook pages using Python and Selenium. We will also discuss the libraries and tools required for the process. So, if you’re interested in web scraping and data analysis, keep reading!

Facebook scraping with Python

Read more about web scraping with Python and BeautifulSoup and kickstart your analysis today.   

What do we need before writing the code? 

We will use Python 3.x for this tutorial, and I am assuming that you have already installed it on your machine. Other than that, we need to install two III-party libraries BeautifulSoup and Selenium. 

  • BeautifulSoup — This will help us parse raw HTML and extract the data we need. It is also known as BS4. 
  • Selenium — It will help us render JavaScript websites. 
  • We also need chromium to render websites using Selenium API. You can download it from here. 


Before installing these libraries, you have to create a folder where you will keep the python script. 

Now, create a python file inside this folder. You can use any name and then finally, install these libraries. 

What will we extract from a Facebook page? 

We are going to scrape addresses, phone numbers, and emails from our target page. 

First, we are going to extract the raw HTML using Selenium from the Facebook page and then we are going to use. find() and .find_all() methods of BS4 to parse this data out of the raw HTML. Chromium will be used in coordination with Selenium to load the website. 

Read about: How to scrape Twitter data without Twitter API using SNScrape. 

Let’s start scraping  

Let’s first write a small code to see if everything works fine for us. 

Let’s understand the above code step by step. 

  • We have imported all the libraries that we installed earlier. We have also imported the time library. It will be used for the driver to wait a little more before closing the chromium driver. 
  • Then we declared the PATH of our chromium driver. This is the path where you have kept the chromedriver. 
  • One empty list and an object to store data. 
  • target_url holds the page we are going to scrape. 
  • Then using .Chrome() method we are going to create an instance for website rendering. 
  • Then using .get() method of Selenium API we are going to open the target page. 
  • .sleep() method will pause the script for two seconds. 
  • Then using .page_source we collect all the raw HTML of the page. 
  • .close() method will close down the chrome instance. 


Once you run this code it will open a chrome instance, then it will open the target page and then after waiting for two seconds the chrome instance will be closed. For the first time, the chrome instance will open a little slow but after two or three times it will work faster. 

Once you inspect the page you will find that the intro section, contact detail section, and photo gallery section all have the same class names

with a div. But since for this tutorial, our main focus is on contact details therefore we will focus on the second div tag. 

Let’s find this element using the .find() method provided by the BS4 API. 

We have created a parse tree using BeautifulSoup and now we are going to extract crucial data from it. 

Using .find_all() method we are searching for all the div tags with class

and then we selected the second element from the list.

Now, here is a catch. Every element in this list has the same class and tag. So, we have to use regular expressions in order to find the information we need to extract. 

Let’s find all of these element tags and then later we will use a for loop to iterate over each of these elements to identify which element is what. 

Here is how we will identify the address, number, and email. 

  • The address can be identified if the text contains more than two commas. 
  • The number can be identified if the text contains more than two dash(-). 
  • Email can be identified if the text contains “@” in it. 

We ran a for loop on allDetails variable. Then we are one by one identifying which element is what. Then finally if they satisfy the if condition we are storing it in the object o. 

In the end, you can append the object o in the list l and print it. 

Once you run this code you will find this result. 

Complete Code 

We can make further changes to this code to scrape more information from the page. But for now, the code will look like this. 


Today we scraped the Facebook page to collect emails for lead generation. Now, this is just an example of scraping a single page. If you have thousands of pages, then we can use the Pandas library to store all the data in a CSV file. I leave this task for you as homework. 

I hope you like this little tutorial and if you do then please do not forget to share it with your friends and on your social media. 

Is code the ultimate representation to unlock the power of data analysis?
Ayesha Saleem
| February 16, 2023

Data analysis is an essential process in today’s world of business and science. It involves extracting insights from large sets of data to make informed decisions. One of the most common ways to represent a data analysis is through code. However, is code the best way to represent a data analysis?  

In this blog post, we will explore the pros and cons of using code to represent data analysis and examine alternative methods of representation. 

Advantages of performing data analysis through code

One of the main advantages of representing data analysis through code is the ability to automate the process. Code can be written once and then run multiple times, saving time and effort. This is particularly useful when dealing with large sets of data that need to be analyzed repeatedly.  

Additionally, code can be easily shared and reused by other analysts, making collaboration and replication of results much easier. Another advantage of code is the ability to customize and fine-tune the analysis. With it, analysts have the flexibility to adjust the analysis as needed to fit specific requirements. This allows for more accurate and tailored results.  

Furthermore, code is a powerful tool for data visualization, enabling analysts to create interactive and dynamic visualizations that can be easily shared and understood. 

Disadvantages of performing data analysis through code

One of the main disadvantages of representing data analysis through code is that it can be challenging for non-technical individuals to understand. It is often written in specific programming languages, which can be difficult for non-technical individuals to read and interpret. This can make it difficult for stakeholders to understand the results of the analysis and make informed decisions. 

Another disadvantage of code is that it can be time-consuming and requires a certain level of expertise. Analysts need to have a good understanding of programming languages and techniques to be able to write and execute code effectively. This can be a barrier for some individuals, making it difficult for them to participate in the entire process. 

Code represent data analysis
               Code represents data analysis 

Alternative methods of representing data analysis

1. Visualizations 

One alternative method of representing data analysis is through visualizations. Visualizations, such as charts and graphs, can be easily understood by non-technical individuals and can help to communicate complex ideas in a simple and clear way. Additionally, there are tools available that allow analysts to create visualizations without needing to write any code, making it more accessible to a wider range of individuals. 

2. Natural language 

Another alternative method is natural language. Natural Language Generation (NLG) software can be used to automatically generate written explanations of analysis in plain language. This makes it easier for non-technical individuals to understand the results and can be used to create reports and presentations.

Narrative: Instead of representing data through code or visualizations, a narrative format can be used to tell a story about the data. This could include writing a report or article that describes the findings and conclusions of the analysis. 

Dashboards: Creating interactive dashboards allows users to easily explore the data and understand the key findings. Dashboards can include a combination of visualizations, tables, and narrative text to present the data in a clear and actionable way. 

Machine learning models: Using machine learning models to analyze data can also be an effective way to represent the data analysis. These models can be used to make predictions or identify patterns in the data that would be difficult to uncover through traditional techniques. 

Presentation: Preparing a presentation for the data analysis is also an effective way to communicate the key findings, insights, and conclusions effectively. This can include slides, videos, or other visual aids to help explain the data and the analysis. 

Ultimately, the best way to represent data analysis will depend on the audience, the data, and the goals of the analysis. By considering multiple methods and choosing the one that best fits the situation, it can be effectively communicated and understood. 

Check out this course and learn Power BI today!

Learn to best represent your data 

Code is a powerful tool for representing data analysis and has several advantages, such as automation, customization, and visualization capabilities. However, it also has its disadvantages, such as being challenging for non-technical individuals to understand and requiring a certain level of expertise.  

Alternative methods, such as visualizations and natural language, can be used to make data analysis more accessible and understandable for a wider range of individuals. Ultimately, the best way to represent a data analysis will depend on the specific context and audience. 

Power BI sales dashboard: A 6-step guide to better sales insights
Nathan Piccini
| February 13, 2023

Are you geared to create a sales dashboard on Power BI and track key performance indicators to drive sales success? This step-by-step guide will show you through connecting to the data source, build the dashboard, and add interactivity and filters.

Creating a sales dashboard in Power BI is a straightforward process that can help your sales team to track key performance indicators (KPIs) and make data-driven decisions. Here’s a step-by-step guide on how to create a sales dashboard using the above-mentioned KPIs in Power BI: 

sales dashboard on Power BI 
Creating a sales dashboard on Power BI – Data Science Dojo

Step 1: Connect to your data source 

The first step is to connect to your data source in Power BI. This can be done by clicking on the “Get Data” button in the Home ribbon, and then selecting the appropriate connection type (e.g., Excel, SQL Server, etc.). Once you have connected to your data source, you can import the data into Power BI for analysis. 

Step 2: Create a new report 

Once you have connected to your data source, you can create a new report by clicking on the “File” menu and selecting “New” -> “Report.” This will open a new report canvas where you can begin to build your dashboard. 

Step 3: Build the dashboard 

To build the dashboard, you will need to add visualizations to the report canvas. You can do this by clicking on the “Visualizations” pane on the right-hand side of the screen, and then selecting the appropriate visualization type (e.g., bar chart, line chart, etc.). Once you have added a visualization to the report canvas, you can use the “Fields” pane on the right-hand side to add data to the visualization. 

Read more about maximizing sales success with dashboards by clicking on this link.

Step 4: Add the KPIs to the dashboard 

To add the KPIs to the dashboard, you will need to create a new card visualization for each KPI. Then, use the “Fields” pane on the right-hand side of the screen to add the appropriate data to each card. 

Sales Revenue:

To add this KPI, you’ll need to create a card visualization and add the “Total Sales Revenue” column from your data source. 

Sales Quota Attainment:

To add this KPI, you’ll need to create a card visualization and add the “Sales Quota Attainment” column from your data source. 

Lead Conversion Rate:

To add this KPI, you’ll need to create a card visualization and add the “Lead Conversion Rate” column from your data source. 

Customer Retention Rate:

To add this KPI, you’ll need to create a card visualization and add the “Customer Retention Rate” column from your data source. 

Average Order Value:

To add this KPI, you’ll need to create a card visualization and add the “Average Order Value” column from your data source. 

Step 5: Add filters and interactivity 

Once you have added all the KPIs to the dashboard, you can add filters and interactivity to the visualizations. You can do this by clicking on the “Visualizations” pane on the right-hand side of the screen and selecting the appropriate filter or interactivity option. For example, you can add a time filter to your chart to show sales data over a specific period, or you can add a hover interaction to your diagram to show more data when the user moves their mouse over a specific point.

Check out this course and learn Power BI today!

Step 6: Publish and share the dashboard 

Once you’ve completed your dashboard, you can publish it to the web or share it with specific users. To do this, click on the “File” menu and select “Publish” -> “Publish to Web” (or “Share” -> “Share with specific users” if you are sharing the dashboard with specific users). This will generate a link that can be shared with your team, or you can also publish the dashboard to the Power BI service where it can be accessed by your sales team from anywhere, at any time. You can also set up automated refresh schedules so that the dashboard is updated with the latest data from your data source.

Ready to transform your sales strategy with a custom dashboard in Power BI?

By creating a sales dashboard in Power BI, you can bring all your sales data together in one place, making it easier for your team to track key performance indicators and make informed decisions. The process is simple and straightforward, and the end result is a custom dashboard that can be customized to fit the specific needs of your sales team.

Whether you are looking to track sales revenue, sales quota attainment, lead conversion rate, customer retention rate, or average order value, Power BI has you covered. So why wait? Get started today and see how Power BI can help you drive growth and success for your sales team! 

Transform your data into insights: The data analyst’s guide to Power BI
Ruhma Khawaja
| February 9, 2023

Data is an essential component of any business, and it is the role of a data analyst to make sense of it all. Power BI is a powerful data visualization tool that helps them turn raw data into meaningful insights and actionable decisions.

In this blog, we will explore the role of data analysts and how they use Power BI to extract insights from data and drive business success. From data discovery and cleaning to report creation and sharing, we will delve into the key steps that can be taken to turn data into decisions. 

A data analyst is a professional who uses data to inform business decisions. They process and analyze large sets of data to identify trends, patterns, and insights that can help organizations make more informed decisions. 


Data Analyst using Power BI
Uses of Power BI for a Data Analyst – Data Science Dojo

Who is a data analyst?

A data analyst is a professional who works with data to extract insights, draw conclusions, and support decision-making. They use a variety of tools and techniques to clean, transform, visualize, and analyze data to understand patterns, relationships, and trends. The role of a data analyst is to turn raw data into actionable information that can inform and drive business strategy.

They use various tools and techniques to extract insights from data, such as statistical analysis, and data visualization. They may also work with databases and programming languages such as SQL and Python to manipulate and extract data. 

The importance of data analysts in an organization is that they help organizations make data-driven decisions. By analyzing data, analysts can identify new opportunities, optimize processes, and improve overall performance. They also help organizations make more informed decisions by providing insights into customer behavior, market trends, and other key metrics.

Additionally, their role and job can help organizations stay competitive by identifying areas where they may be lagging and providing recommendations for improvement. 

Defining Power BI 

Power BI provides a suite of data visualization and analysis tools to help organizations turn data into actionable insights. It allows users to connect to a variety of data sources, perform data preparation and transformations, create interactive visualizations, and share insights with others. 

Check out this course and learn Power BI today!

The platform includes features such as data modeling, data discovery, data analysis, and interactive dashboards. It enables organizations to quickly create and share visualizations, reports, and dashboards with stakeholders, regardless of their technical skill level.

Power BI also provides collaboration features, allowing team members to work together on data insights, and share information and insights with others through Power BI reports and dashboards. 

Key capabilities of Power BI  

Data Connectivity:It allows users to connect to various data sources including Excel, SQL Server, Azure SQL, and other cloud-based data sources. 

Data Transformation: It provides a wide range of data transformation tools that allow users to clean, shape, and prepare data for analysis. 

Visualization: It offers a wide range of visualization options, including charts, tables, and maps, that allow users to create interactive and visually appealing reports. 

Sharing and Collaboration: It allows users to share and collaborate on reports and visualizations with others in their organization. 

Mobile Access: It also offers mobile apps for iOS and Android, that allow users to access and interact with their data on the go. 

How does a data analyst use Power BI? 

A data analyst uses Power BI to collect, clean, transform, visualize, and analyze data to turn it into meaningful insights and decisions. The following steps outline the process of using Power BI for data analysis: 

  1. Connect to data sources: A data analyst can import data from a variety of sources, such as spreadsheets, databases, or cloud-based services. Power BI provides several ways to import data, including manual upload, data connections, and direct connections to data sources. 
  2. Clean and transform data: Before data can be analyzed, it often needs to be cleaned and prepared. This may include removing any extraneous information, correcting errors or inconsistencies, and transforming data into a format that is usable for analysis.
  3. Create visualizations: Once the data has been prepared, a data analyst can use Power BI to create visualizations of the data. This may include bar charts, line graphs, pie charts, scatter plots, and more. Power BI provides a few built-in visualizations and the ability to create custom visualizations, giving data analysts a wide range of options for presenting data. 
  4. Perform data analysis: Power BI provides a range of data analysis tools, including calculated fields and measures, and the DAX language, which allows data analysts to perform more advanced analysis. These tools allow them to uncover insights and trends that might not be immediately apparent. 
  5. Collaborate and share insights: Once insights have been uncovered, data analysts can share their findings with others through Power BI reports or dashboards. These reports provide a way to present data visualizations and analysis results to stakeholders and can be published and shared with others. 


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By following these steps, a data analyst can use Power BI to turn raw data into meaningful insights and decisions that can inform business strategy and decision-making. 


Why should you use data analytics with Power BI? 

User-friendly interface – Power BI has a user-friendly interface, which makes it easy for users with little to no technical skills to create and share interactive dashboards, reports, and visualizations. 

Real-time data visualization – It provides real-time data visualization, allowing users to analyze data in real time and make quick decisions. 

Integration with other Microsoft tools – Power BI integrates seamlessly with other Microsoft tools, such as Excel, SharePoint, and Azure, making it an ideal tool for organizations using Microsoft technology. 

Wide range of data sources – It can connect to a wide range of data sources, including databases, spreadsheets, cloud services, and web APIs, making it easy to consolidate data from multiple sources. 

Cost-effective – It is a cost-effective solution for data analytics, with both free and paid versions available, making it accessible to organizations of all sizes. 

Mobile accessibility – Power BI provides mobile accessibility, allowing users to access and analyze data from anywhere, on any device. 

Collaboration features – With robust collaboration features, it allows users to share dashboards and reports with other team members, encouraging teamwork and decision-making. 


In conclusion, Power BI is a powerful tool for data analysis that provides organizations with the ability to easily visualize, analyze, and share complex data. By preparing, cleaning, and transforming data, creating relationships between tables, using visualizations and DAX, they can create reports and dashboards that provide valuable insights into key business metrics.

The ability to publish reports, share insights, and collaborate with others makes Power BI an essential tool for any organization looking to improve performance and make informed decisions.  


Essential types of data analysis methods and processes for business success
Hudaiba Soomro
| January 17, 2023

An overview of data analysis, the data analysis process, its various methods, and implications for modern corporations. 


Studies show that 73% of corporate executives believe that companies failing to use data analysis on big data lack long-term sustainability. While data analysis can guide enterprises to make smart decisions, it can also be useful for individual decision-making 

Let’s consider an example of using data analysis at an intuitive individual level. As consumers, we are always choosing between products offered by multiple companies. These decisions, in turn, are guided by individual past experiences. Every individual analysis the data obtained via their experience to generate a final decision.  

Put more concretely, data analysis involves sifting through data, modeling it, and transforming it to yield information that guides strategic decision-making. For businesses, data analytics can provide highly impactful decisions with long-term yield. 


Data analysis methods and data analysis process
Data analysis methods and data analysis processes – Data Science Dojo


 So, let’s dive deep and look at how data analytics tools can help businesses make smarter decisions. 

 The data analysis process 

The process includes five key steps:  

1. Identify the need

Companies use data analytics for strategic decision-making regarding a specific issue. The first step, therefore, is to identify the particular problem. For example, a company decides it wants to reduce its production costs while maintaining product quality. To do so effectively, the company would need to identify step(s) of the workflow pipeline it should implement cost cuts. 

Similarly, the company might also have a hypothetical solution to its question. Data analytics can be used to judge the falsifiability of the hypothesis, allowing the decision-maker to reach the optimized solution. 

A specific question or hypothesis determines the subsequent steps of the process. Hence, this must be as clear and specific as possible. 


2. Collect the data 

Once the data analysis need is identified, the subsequent kind of data is also determined. Data collection can involve data entered in different types and formats. One broad classification is based on structure and includes structured and unstructured data. 

 Structured data, for example, is the data a company obtains from its users via internal data acquisition methods such as marketing automation tools. More importantly, it follows the usual row-column database and is suited to the company’s exact needs. 

Unstructured data, on the other hand, need not follow any such formatting. It is obtained via third parties such as Google trends, census bureaus, world health bureaus, and so on. Structured data is easier to work with as it’s already tailored to the company’s needs. However, unstructured data can provide a significantly larger data volume. 

There are many other data types to consider as well. For example, meta data, big data, real-time data, and machine data.  


3. Clean the data 

The third step, data cleaning, ensures that error-free data is used for the data analysis. This step includes procedures such as formatting data correctly and consistently, removing any duplicate or anomalous entries, dealing with missing data, fixing cross-set data errors.  

 Performing these tasks manually is tedious and hence, various tools exist to smoothen the data cleaning process. These include open-source data tools such as OpenRefine, desktop applications like Trifacta Wrangler, cloud-based software as a service (SaaS) like TIBCO Clarity, and other data management tools such as IBM Infosphere quality stage especially used for big data. 


4. Perform data analysis 

Data analysis includes several methods as described earlier. The method to be implemented depends closely on the research question to be investigated. Data analysis methods are discussed in detail later in this blog. 


5. Present the results 

Presentation of results defines how well the results are to be communicated. Visualization tools such as charts, images, and graphs effectively convey findings, establishing visual connections in the viewer’s mind. These tools emphasize patterns discovered in existing data and shed light on predicted patterns, assisting the results’ interpretation. 


Listen to the Data Analysis challenges in cybersecurity


Methods for data analysis 

Data analysts use a variety of approaches, methods, and tools to deal with data. Let’s sift through these methods from an approach-based perspective: 


1. Descriptive analysis 

Descriptive analysis involves categorizing and presenting broader datasets in a way that allows emergent patterns to be observed from them to see if there are any obvious patterns. Data aggregation techniques are one way of performing descriptive analysis. This involves first collecting the data and then sorting it to ease manageability. 

This can also involve performing statistical analysis on the data to determine, say, the measures of frequency, dispersion, and central tendencies that provide a mathematical description for the data.

2. Exploratory analysis 

Exploratory analysis involves consulting various data sets to see how certain variables may be related, or how certain patterns may be driving others. This analytic approach is crucial in framing potential hypotheses and research questions that can be investigated using data analytic techniques.  

Data mining, for example, requires data analysts to use exploratory analysis to sift through big data and generate hypotheses to be tested out. 


3. Diagnostic analysis 

Diagnostic analysis is used to answer why a particular pattern exists in the first place. For example, this kind of analysis can assist a company in understanding why its product is performing in a certain way in the market. 

Diagnostic analytics includes methods such as hypothesis testing, determining a correlations v/s causation, and diagnostic regression analysis. 


4. Predictive analysis 

Predictive analysis answers the question of what will happen. This type of analysis is key for companies in deciding new features or updates on existing products, and in determining what products will perform well in the market.  

 For predictive analysis, data analysts use existing results from the earlier described analyses while also using results from machine learning and artificial intelligence to determine precise predictions for future performance. 


5. Prescriptive analysis 

Prescriptive analysis involves determining the most effective strategy for implementing the decision arrived at. For example, an organization can use prescriptive analysis to sift through the best way to unroll a new feature. This component of data analytics actively deals with the consumer end, requiring one to work with marketing, human resources, and so on.  

 Prescriptive analysis makes use of machine learning algorithms to analyze large amounts of big data for business intelligence. These algorithms are able to asses large amounts of data by working through them via “if” and “else” statements and making recommendations accordingly. 


6. Quantitative and qualitative analysis 

Quantitative analysis computationally implements algorithms testing out a mathematical fit to describe correlation or causation observed within datasets. This includes regression analysis, null analysis, hypothesis analysis, etc.  

Qualitative analysis, on the other hand, involves non-numerical data such as interviews and pertains to answering broader social questions. It involves working closely with textual data to derive explanations.  


7. Statistical analysis 

Statistical techniques provide answers to essential decision challenges. For example, they can accurately quantify risk probabilities, predict product performance, establish relationships between variables, and so on. These techniques are used by both qualitative and quantitative analysis methods. Some of the invaluable statistical techniques for data analysts include linear regression, classification, resampling methods, subset selection.  

Statistical analysis, more importantly, lies at the heart of data analysis, providing the essential mathematical framework via which analysis is conducted. 


Data-driven businesses 

Data-driven businesses use the data analysis methods described above. As a result, they offer many advantages and are particularly suited to modern needs. Their credibility relies on them being evidence-based and using precise mathematical models to determine decisions. Some of these advantages include stronger customer needs, precise identification of business needs, devising effective strategy decisions, and performing well in a competitive market. Data-driven businesses are the way forward. 

From novice to expert data analyst: A comprehensive guide to practice key skills
Hudaiba Soomro
| January 9, 2023

It is no surprise that the demand for a skilled data analyst grows across the globe. In this blog, we will explore eight key competencies that aspiring data analysts should focus on developing. 


Data analysis is a crucial skill in today’s data-driven business world. Companies rely on data analysts to help them make informed decisions, improve their operations, and stay competitive. And so, all healthy businesses actively seek skilled data analysts. 


Technical skills and non-technical skills for data analyst
Technical skills and non-technical skills for data analyst


Becoming a skilled data analyst does not just mean that you acquire important technical skills. Rather, certain soft skills such as creative storytelling or effective communication can mean a more all-rounded profile. Additionally, these non-technical skills can be key in shaping how you make use of your data analytics skills. 

Technical skills to practice as a data analyst: 

Technical skills are an important aspect of being a data analyst. Data analysts are responsible for collecting, cleaning, and analyzing large sets of data, so a strong foundation in technical skills is necessary for them to be able to do their job effectively.

Some of the key technical skills that are important for a data analyst include:

1. Probability and statistics:  

A solid foundation in probability and statistics ensures your ability to identify patterns in data, prevent any biases and logical errors in the analysis, and lastly, provide accurate results. All these abilities are critical to becoming a skilled data analyst. 

 Consider, for example, how various kinds of probabilistic distributions are used in machine learning. Other than a strong understanding of these distributions, you will need to be able to apply statistical techniques, such as hypothesis testing and regression analysis, to understand and interpret data. 


2. Programming:  

As a data analyst, you will need to know how to code in at least one programming language, such as Python, R, or SQL. These languages are the essential tools via which you will be able to clean and manipulate data, implement algorithms and build models. 

Moreover, statistical programing languages like Python and R allow advanced analysis that interfaces like Excel cannot provide. Additionally, both Python and R are open source.  

3. Data visualization 

A crucial part of a data analyst’s job is effective communication both within and outside the data analytics community. This requires the ability to create clear and compelling data visualizations. You will need to know how to use tools like Tableau, Power BI, and D3.js to create interactive charts, graphs, and maps that help others understand your data. 


The progression of the Datasaurus Dozen dataset through all of the target shapes – Source


4. Database management:  

Managing and working with large and complex datasets means having a solid understanding of database management. This includes everything from methods of collecting, arranging, and storing data in a secure and efficient way. Moreover, you will also need to know how to design and maintain databases, as well as how to query and manipulate data within them. 

Certain companies may have roles particularly suited to this task such as data architects. However, most will require data analysts to perform these duties as data analysts responsible for collecting, organizing, and analyzing data to help inform business decisions. 

Organizations use different data management systems. Hence, it helps to gain a general understanding of database operations so that you can later specialize them to a particular management system.  

Non-technical skills to adopt as a data analyst:  

Data analysts work with various members of the community ranging from business leaders to social scientists. This implies effective communication of ideas to a non-technical audience in a way that drives informed, data-driven decisions. This makes certain soft skills like communication essential.  

Similarly, there are other non-technical skills that you may have acquired outside a formal data analytics education. These skills such as problem-solving and time management are transferable skills that are particularly suited to the everyday work life of a data analyst. 

1. Communication 

As a data analyst, you will need to be able to communicate your findings to a wide range of stakeholders. This includes being able to explain technical concepts concisely and presenting data in a visually compelling way.  

Writing skills can help you communicate your results to wider members of population via blogs and opinion pieces. Moreover, speaking and presentation skills are also invaluable in this regard. 


Read about Data Storytelling and its importance

2. Problem-solving:   

Problem-solving is a skill that individuals pick from working in different fields ranging from research to mathematics, and much more. This, too, is a transferable skill and not unique to formal data analytics training. This also involves a dash of creativity and thinking of problems outside the box to come up with unique solutions. 

Data analysis often involves solving complex problems, so you should be a skilled problem-solver who can think critically and creatively. 

3. Attention to detail: 

Working with data requires attention to detail and an elevated level of accuracy. You should be able to identify patterns and anomalies in data and be meticulous in your work. 

4. Time management:  

Data analysis projects can be time-consuming, so you should be able to manage your time effectively and prioritize tasks to meet deadlines. Time management can also be implemented by tracking your daily work using time management tools.  


Final word 

Overall, being a data analyst requires a combination of technical and non-technical skills. By mastering these skills, you can become an invaluable member of any team and make a real impact with your data analysis. 


Srishti Puri
| August 3, 2022

How does Expedia determine the hotel price to quote to site users? How come Mac users end up spending as much as 30 percent more per night on hotels? Digital marketing analytics, a torrent flowing into all the corners of the global economy has revolutionized marketing efforts, so much so, that resetting it all together. It is safe to say that marketing analytics is the science behind persuasion.

Marketers are able to learn so much about the users, their likes, dislikes, goals, inspirations, drop-off points, inspirations, needs, and demands. This wealth of information is a gold mine but only for those who know how to use it. In fact, one of the top questions that marketing managers struggle with is


“Which metrics to track?” 


Furthermore, there are several platforms that report on marketing, such as email marketing software, paid search advertising platforms, social media monitoring tools, blogging platforms, and web analytics packages. It is a marketer’s nightmare to be buried under sets of reports from different platforms while tracking a campaign all the way to conversion.

Definitely, there are smarter ways to track. But before we take a deep dive into how to track smartly, let me clarify why you should be investing half the time measuring while doing:

  • To identify what’s working
  • To identify what’s not working
  • Identify strategies to improve
  • Do more of what works

To gain a trustworthy answer to the aforementioned, you must: measure everything. While you attempt at it, arm yourself with the lexicon of marketing analytics to form statements that communicate results, for example:


“Twitter mobile drove 40% of all clicks this week on the corporate website” 

Every statement that you form to communicate analytics must state the source, the segment, value, metric, and range. Let us break down the above example:

  • Source: Twitter
  • Segment: Mobile
  • Value: 40%
  • Metric: Clicks
  • Range: This week

To be able to report such glossy statements, you will need to get your hands dirty. You can either take a campaign-based approach or a goals-based approach.


Campaign-based approach


In a campaign-based approach, you measure the impact of every campaign, for example, if you have social media platforms, blogs, and emails trying to get users to sign up for an e-learning course, this approach will enable you to get insight into each.

In this approach we will discuss the following in detail:

  1. Measure the impact on the website
  2. Measure the impact of SEO
  3. Measure the impact of paid search advertising
  4. Measure the impact of blogging efforts
  5. Measure the impact of social media marketing
  6. Measure the impact of e-mail marketing

Measure the impact on the website 


  • Unique visitors

How to use: Unique visitors account for a fresh set of eyes on your site.  If the number of unique visitors is not rising, then it is a clear indication to reassess marketing tactics.


  • Repeat visitors

How to use: If you have visitors revisiting your site or a landing page, it is a clear indication that your site sticks or offers content people want to return to. But if your repeat visitor rate is high then it is indicative of your content not gauging new audiences.


  • Sources

How to use: Sources are of three types: organic, direct, and referrals. Learning about your traffic sources will give you clarity on your SEO performance. Also, it can help you find answers to questions like what is the percentage of organic traffic of total traffic?


  • Referrals

How to use: This is when the traffic arriving on your site is from another website. Aim for referrals to deliver 20-30% of your total traffic. Referrals can help you identify the types of sites or bloggers that are linking to your site and the type of content they tend to share. This information can be fed back into your SEO strategy, and help you produce relevant content that generates inbound links.


  • Bounce rate

How to use: High bounce rate indicates trouble. Maybe the content is not relevant, or the pages are not compelling enough. Perhaps the experience is not user-friendly. Or the call-to-action buttons are too confusing? A high bounce rate reflects problems, and the reasons can be many.


Measure the impact of SEO 

Similarly, you can measure the impact of SEO using the following metrics:


  • Keyword performance and rankings:

How to use: You can use tools like Google AdWords to identify keywords that optimize your website. Check if the chosen keywords are driving traffic to your site or if they are improving your site’s keywords.


  • Total traffic from organic search:

How to use: This metric is a mirror of how relevant your content is. Low traffic from the organic search may mean it is time to ramp up content creation – videos, blogs, webinars or expand into newer areas, such as e-books and podcasts that can be ranked higher by search engines.

Measure the impact of paid search advertising

Likewise, it is equally important to measure the impact of your paid search, also known as pay per click (PPC), in which you pay for every click that is generated by paid search advertising. How much are you spending in total? Are those clicks turning into leads? How much profit are you generating from this spend? Some of the following metrics can help you clarify:


  • Click through rate:

How to use: This metric helps you determine the quality of your ad. Is it effective enough to prompt a click? Test different copy treatments, headlines, and URLs to figure out the combination that boosts the CTR for a specific term.


  • Average cost per click:

How to use: Cost per click determines the amount you spend for each click on a paid search ad. Combine this conversion rate and earning from the clicks.


  • Conversion rate:

How to use: Is conversion always a purchase? No! Each time a user takes the action you want them to on your site, such as clicking on a button, sign-up for a form, or subscribing, it is accounted as a conversion.


Measure the impact of blogging efforts 

Going beyond the website and SEO metrics, you can also measure the impact of your blogging efforts. Since a considerable amount of organizational resources is invested in creating blogs that can develop backlinks to the website. Some of the metrics that can get you clarity on whether you are generating relevant content:

  • Post Views
  • Call to action performance
  • Blog leads

Measure the impact of social media marketing

 Very well-known and quite widely implemented are the strategies to measure social media marketing. Especially now, as the e-commerce industry is expanding, social media can make or break your image online. Some of the commonly measured metrics are:

  • Reach
  • Engagement
  • Mentions to assess the brand perception
  • Traffic
  • Conversion rate


Measure the impact of e-mail marketing

Quite often, the marketing strategy runs on the crutches of e-mail. E-mails are a good place to start visibility efforts and can be very important in maintaining a sustainable relationship with your existing customer base. Some of the metrics that can help you clarify if your emails are working their magic or not are:

  • Bounce rate
  • Delivery rate
  • Click through rate
  • Share/forwarding rate
  • Unsubscribe rate
  • Frequency of emails sent

Goals-based approach

A goals-based approach is defined based on what you’re trying to achieve by a particular campaign. Are you trying to acquire new customers? Or build a loyal customer base, increase engagement and improve conversion rate? Here are a few examples:

In this approach we will discuss the following in detail:

  • Audience analysis
  • Acquisition analysis
  • Behavioral analysis
  • Conversion analysis
  • A/B testing

 Audience analysis:

The goal is to know:


“Who are your customers?” 


Audience analysis is a measure that helps you gain clarity on who your customers are. The information can include demographics, location, income, age, and so forth. The following set of metrics can help you know your customers better.


  • Unique visitors
  • Lead score

  • Cookies

  • Segment

  • Label

  • Personally Identifiable Information (PII)
  • Properties

  • Taxonomy

Acquisition analysis:


The goal is to know:


“How do customers get to your website?” 


Acquisition analysis helps you understand which channel delivers the most traffic to your site or application. Comparing incoming visitors from different channels helps determine the efficacy of your SEO efforts on organic search traffic and see how well your email campaigns are running. Some of the metrics that can help you are:


  • Omnichannel

  • Funnel

  • Impressions

  • Sources

  • UTM parameters 

  • Tracking URL

  • Direct traffic

  • Referrers

  • Retargeting

  • Attribution

  • Behavioral targeting

Behavioral analysis:

 The goal is to know:


“What do the users do on your website?” 


Behavior analytics explains what customers do on your website. What pages do they visit? Which device do they use? From where do they enter the site? What makes them stay? How long do they stay? Where on the site did, they drop off? Some of the metrics that can help you gain clarity are:

  • Actions

  • Sessions

  • Engagement rate

  • Events

  • Churn

  • Bounce rate

Conversion analysis

The goal is to know:


“Whether customers take actions that you want them to take?” 


Conversions track whether customers take actions that you want them to take. This typically involves defining funnels for important actions — such as purchases — to see how well the site encourages these actions over time. Metrics that can help you gain more clarity are:

  • Conversion rate

  • Revenue report

A/B testing:

The goal is to know:


“What digital assets are likely to be the most effective for higher conversion?” 


A/B testing enables marketers to experiment with different digital options to identify which ones are likely to be the most effective. For example, they can compare one intervention (A Control Group) to another intervention (B). Companies run A/B experiments regularly to learn what works best.

In this article, we discussed what marketing analytics is, its importance, two approaches that marketers can take to report metrics, and the marketing lingo they can use while reporting results. Pick the one that addresses your business needs and helps you get clarity on your marketing efforts. This is not an exhaustive list of all the possible metrics that can be used to measure.

Of course, there are more! But this can be a good starting point until the marketing efforts expand into a larger effort that has additional areas that need to be tracked.


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