Data Analytics

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. However, 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.

Written by Ahmad Benny

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.  

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.PubMed – PubMed 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.

Conclusion

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.

 

Written by Prasad D Wilagama

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.

 

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.

Conclusion

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.

 

Written by: Hamza Mannan Samad

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.

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. 

 

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!

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. 

Conclusion 

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.

 

Written by Manthan Koolwal

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. 

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. 

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: 

 

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! 

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. 

 

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. 

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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. 

Conclusion 

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.

An overview of data analysis, the data analysis methods, its process, 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. 

 

 

 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, metadata, 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, and 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

 

Data analysis methods

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. 

 

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 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 can assess 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, and 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. 

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. 

 

 

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. 

 

 

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. 

 

Goals-based approach

In this blog, we will discuss what Data Analytics RFP is and the five steps involved in the data analytics RFP process.

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In this blog, we are going to discuss about data storytelling for successful brand building, its components and brand storytelling

What is data storytelling? 

Data storytelling is a process of driving insights from a dataset using analysis and making it presentable through visualization. It not only helps capture insights but makes content visually presentable so that stakeholders can make data-driven decisions.  

With data storytelling, you can influence and inform your audience based on your analysis.  

 

There are 3 important components of data storytelling.  

1. Data: You analyze to build a foundation of your data story. This could be descriptive, diagnostic, predictive, or prescriptive analysis to help get a full picture. 
2. Narrative: Also known as a storyline, a narrative is used to communicate insights gained from your analysis. 
3. Visualization: Visualization helps communicate that story clearly and effectively. Making use of graphs, charts, diagrams, and audio-visuals for the purpose. 

 

The benefits of data storytelling

 

So, the question arises why do we even need storytelling for data? The simple answer is it helps with decision-making. But let’s take a look at some of the benefits of data storytelling. 

• Adding value to your data and insights. 
• Interpreting complex information and highlighting essential key points for the audience. 
• Providing a human touch to your data. 

• Offering value to your audience and industry. 
• Building credibility as an industry and topic thought leader.

 

For example, Airbnb uses data storytelling to help consumers find the right hotel at the right price and also for hosts to set up Airbnb at the most lucrative place.  

 

Data storytelling helps AirBnB deliver personalized experience and recommendations. Their price tip feature is constantly updated to help guide hosts on how likely are they to get a booking at a chosen price.  Other features include host/guest interactions, current events, and local market history in real-time available through its app. 

 

Data-driven brand storytelling 

Now that we have an understanding of data storytelling, let’s talk about how brand storytelling works. Data-driven brand storytelling is when a company uses research, studies, and analytics to share information about a brand and tell a story to consumers.  

It turns complex datasets into an insightful easy to understand visually comprehensible story. It is different than creative storytelling where the brand only focuses on creating a perception. Here the story is based on factual data. 

Storytelling is a great way to build brand association and connect with your consumers. Data-driven storytelling uses visualization that captures attention.  

 

Learn how to create and execute data visualization and tell a story with your data by enrolling in our 5-day live Power BI training.  

 

Studies show that our brains process images 60,000 times faster than words, 90% of information transmitted to the brain is visual in nature and we’re 65% more likely to retain information that is visual. 

That’s why infographics, charts, and images are so useful.  

For example, Tower Electric Bikes, a direct-to-consumer e-bike brand used a infographic to rank the most and the least bike-friendly cities across the US. This way they turned an enormous amount of data into visually friendly info-graphic that bike consumers can interpret with just a glance. 

 

  

Using the power of storytelling for marketing content 

Even though all content is interpreted as data by consumer but visual content provides the most value in terms of memorability, impact, and capturing their attention. The job of any successful brand is to build a positive association in consumers’ minds. 

Storytelling helps create those positive associations by providing high-value engaging content, capturing attention, and giving meaning to not-so-visually appealing datasets. 

We live in a world that is highly cluttered by advertising and paid promotional content. To make your content stand out from competitors you need to have good visualization and a story behind it. Storytelling helps assign meaning and context to data that would otherwise look unappealing and dry.  

Consumers gain clarity, and better understanding, and share more if it makes sense to them. Data storytelling helps extract and communicate insight that in turn helps your consumer’s buying journey.

It could be content relevant to any stage of their buyer journey or even outside of the sales cycle. Storytelling helps create engaging and memorable marketing content that would help grow your brand. 

Learn how to use data visualization, narratives, and real-life examples to bring your story to life with our free community event Storytelling Data. 

 

Executing flawless data-driven brand storytelling 

Now that we have a better understanding of brand storytelling, let’s have a look at how to go about crafting a story and important steps involved. 

Craft a compelling narrative 

The most important element in building a story is the narrative. You need a compelling narrative for your story. There are 4 key elements to any story. 

Characters: These are your key players or stakeholders in your story. They can be customers, suppliers, competitors, environmental groups, government, or any other group that has to do with your brand.  

Setting: This is where you use your data to reinforce the narrative. Whether it’s an improved feature in your product that increases safety or a manufacturing process that takes into account environmental impact. This is the stage where you define environment that concerns your stakeholders.

Conflict: Here you describe the root issue or problem you’re trying to solve with data. This could be marketing content that generated sales revenue, you want your team to have a better understanding of it to create helpful content for the sales team. Conflict plays a crucial role in making your story relevant and engaging. There needs to be a problem for a data solution.  

Resolution: Finally, you want to propose a solution to the identified problem. You can present a short-term fix along with a long-term pivot depending on the type of problem you are solving. At this stage, your marketing outreach should be consistent with a very visible message across all channels.

You don’t want to create confusion, whatever resolution/result you’ve achieved through analysis should be clearly indicated with supporting evidence and compelling visualization to make your story come to life. 

Your storytelling needs to have all these steps to be able to communicate your message effectively to the desired audience. With these steps, your audience will walk through a compelling, engaging and impactful story. 

Start learning data storytelling today

Our brains are hard-wired to love stories and visuals. Storytelling is not something new it dates back to 1700 BCE, from cave paintings to symbol language. That is the reason it resonates so well in today’s fast-paced cluttered consumer environment.  

Brands can use storytelling based on factual data to engage, create positive associations and finally encourage action. The best way to come up with a story narrative is to use internal data, success stories, and insights driven by your research and analysis. Then translate those insights into a story and visuals for better retention and brand building. 

 

Here’s a chance for you to learn all about data and how to generate data-driven insights that are useful for your business!

 

Written by Bilal Awan

In this article, we’re going to talk about how data analytics can help your business generate more leads and why you should rely on data when making decisions regarding a digital marketing strategy. 

Some people believe that marketing is about creativity – unique and interesting campaigns, quirky content, and beautiful imagery. Contrary to their beliefs, data analytics is what actually powers marketing – creativity is simply a way to accomplish the goals determined by analytics. 

Now, if you’re still not sure how you can use data analytics to generate more leads, here are our top 10 suggestions. 

1. Know how your audience behaves

Most businesses have an idea or two about who their target audience is. But having an idea or two is not good enough if you want to grow your business significantly – you need to be absolutely sure who your audience is and how they behave when they come to your website. 

Now, the best way to do that is to analyze the website data.  

You can tell quite a lot by simply looking at the right numbers. For instance, if you want to know whether the users can easily find the information they’re looking for, keep track of how much time they spend on a certain webpage. If they leave the webpage as soon as it loads, they probably didn’t find what they needed. 

We know that looking at spreadsheets is a bit boring, but you can easily obtain Power BI Certification and use Microsoft Power BI to make data visuals that are easy to understand and pleasing to the eye. 

 

Read the top 12 data analytics books to learn more about it

 

2. Segment your audience

A great way to satisfy the needs of different subgroups within your target audience is to use audience segmentation. Using that, you can create multiple funnels for the users to move through instead of just one, thereby increasing your lead generation. 

Now, before you segment your audience, you need to have enough information about these subgroups so that you can divide them and identify their needs. Since you can’t individually interview users and ask them for the necessary information, you can use data analytics instead. 

Once you have that, it’s time to identify their pain points and address them differently for different subgroups, and voilàa – you’ve got yourself more leads. 

3. Use data analytics to improve buyer persona

Knowing your target audience is a must but identifying a buyer persona will take things to the next level. A buyer persona doesn’t only contain basic information about your customers. It goes deeper than that and tells you their exact age, gender, hobbies, location, and interests.  

It’s like describing a specific person instead of a group of people. 

Of course, not all your customers will fit that description to a T, but that’s not the point. The point is to have that one idea of a person (or maybe two or three buyer personas) in your mind when creating content for your business.  

 

4. Use predictive marketing 

While data analytics should absolutely be used in retrospectives, there’s another purpose for the information you obtain through analytics – predictive marketing. 

Predictive marketing is basically using big data to develop accurate forecasts of customers’ behavior. It uses complex machine-learning algorithms to build predictive models. 

A good example of how that works is Amazon’s landing page, which includes personalized recommendations.  

Amazon doesn’t only keep track of the user’s previous purchases, but also what they have clicked on in the past and the types of items they’ve shown interest in. By combining that with the season of purchase and time, they are able to make recommendations that are nearly 100% accurate. 

 

If you’re curious to find out how data science works, we suggest that you enroll in the Data Science Bootcamp. 

 

5. Know where website traffic comes from 

Users come to your website from different places.  

Some have searched for it directly on Google, some have run into an interesting blog piece on your website, while others have seen your ad on Instagram. This means that the time and effort you put into optimizing your website and creating interesting content pays off. 

But imagine creating a YouTube ad that doesn’t bring much traffic – that doesn’t pay off at all. You’d then want to rework your campaign or redirect your efforts elsewhere.  

This is exactly why knowing where website traffic comes from is valuable. You don’t want to invest your time and money into something that doesn’t bring you any benefits. 

6. Understand which products work 

Most of the time, you can determine what your target audience will like and dislike. The more information you have about your target audience, the better you can satisfy their needs.  

But no one is perfect, and anyone can make a mistake. 

Heinz, a company known for producing ketchup and other food, once released their new product: – EZ Squirt ketchup in shades of purple, green, and blue. At first, the kids loved it, but this didn’t last for long. Six years later after that, Heinz halted production of these products. 

As you can see, even big and experienced companies flop sometimes. A good way to avoid that is by tracking which product pages have the least traffic and don’t sell well. 

7. Perform competitor analysis 

Keeping an eye on your competitors is never a bad idea. No matter how well you’re doing and how unique you are, others will try to surpass you and become better. 

The good news is that there are quite a few tools online that you can use for competitor analysis. SEMrush, for instance, can help you see what the competition is doing to get qualified leads so that you can use it to your advantage. 

Even if there wasn’t a tool you need, you can always enroll in a Python for Data Science course and learn to build your own tools that can track the data you need to drive your lead generation. 

8. Nurture your leads

Nurturing your leads means developing a personalized relationship with your prospects at every stage of the sales funnel in order to get them to buy your products and become your customers. 

Because lead nurturing offers a personalized approach, you’ll need information about your leads: – what is their title, role, industry, and similar info, depending on what your business does. Once you have that, you can provide them with the relevant content that will help them decide to buy your products and build brand loyalty along the way. 

This is something b2b lead generation companies can help you with if you’re hesitant to do it on your own.  

9. Gain more customers

Having an insight into your conversion rate, churn rate, sources of website traffic, and other relevant data will ultimately lead to more customers. For instance, your sales team will be able to calculate which sources convert most effectively and prepare resources before running a campaign. 

The more information you have, the better you’ll perform, and this is exactly why Data Science for Business is important – you’ll be able to see the bigger picture and make better decisions. 

10. Avoid significant losses 

Finally, data can help you avoid certain losses by halting the launch of a product that won’t do well. 

For instance, you can use the Coming soon page to research the market and see if your customers are interested in a new product you planned on launching. If enough people show interest, you can start producing, and if not – you won’t waste your money on something that was bound to fail. 

 

Conclusion:

Applications of data analytics go beyond simple data analysis, especially for advanced analytics projects. The majority of the labor is done up front in the data collection, integration, and preparation stages, followed by the creation, testing, and revision of analytical models to make sure they give reliable findings.

Data engineers, who build data pipelines and aid in the preparation of data sets for analysis, are frequently included within analytics teams in addition to data scientists and other data analysts.

 

Written by Ava-Mae

A hands-on guide to collect and store twitter data for timeseries analysis 

“A couple of weeks back, I was working on a project in which I had to scrape tweets from twitter and after storing them in a csv file, I had to plot some graphs for timeseries analysis. I requested Twitter for Twitter developer API, but unfortunately my request was not fulfilled. Then I started searching for python libraries which can allow me to scrape tweets without the official Twitter API.

To my amazement, there were several libraries through which you can scrape tweets easily but for my project I found ‘Snscrape’ to be the best library, which met my requirements!” 

What is SNScrape? 

A scraper for social networking platforms known as snscrape (SNS). It retrieves objects, such as pertinent posts, by scraping things like user profiles, hashtags, or searches. 

 

Install Snscrape 

Snscrape requires Python 3.8 or higher. The Python package dependencies are installed automatically when you install Snscrape. You can install using the following commands. 

• pip3 install snscrape 

• pip3 install git+https://github.com/JustAnotherArchivist/snscrape.git (Development Version) 

 

For this tutorial we will be using the development version of Snscrape. Paste the second command in command prompt(cmd), make sure you have git installed on your system. 

 

Code walkthrough for scraping

Before starting make sure you have the following python libraries: 

• Pandas 
• Numpy 
• Snscrape 
• Tqdm 
• Seaborn 

• Matplotlit 

Importing Relevant Libraries 

To run the scraping program, you will first need to import the libraries 

import pandas as pd 

import numpy as np 

import snscrape.modules.twitter as sntwitter 

import datetime 

from tqdm.notebook import tqdm_notebook 

import seaborn as sns 

import matplotlib.pyplot as plt 

sns.set_theme(style="whitegrid") 

 

 

Taking User Input 

To scrape tweets, you can provide many filters such as the username or start date or end date etc. We will be taking the following user inputs which will then be used in Snscrape. 

• Text: The query to be matched. (Optional) 
• Username: Specific username from twitter account. (Required) 
• Since: Start Date in this format yyyy-mm-dd. (Optional) 
• Until: End Date in this format yyyy-mm-dd. (Optional) 
• Count: Max number of tweets to retrieve. (Required) 

• Retweet: Include or Exclude Retweets. (Required) 
• Replies: Include or Exclude Replies. (Required) 

 

For this tutorial we used the following inputs: 

text = input('Enter query text to be matched (or leave it blank by pressing enter)') 

username = input('Enter specific username(s) from a twitter account without @ (or leave it blank by pressing enter): ') 

since = input('Enter startdate in this format yyyy-mm-dd (or leave it blank by pressing enter): ') 

until = input('Enter enddate in this format yyyy-mm-dd (or leave it blank by pressing enter): ') 

count = int(input('Enter max number of tweets or enter -1 to retrieve all possible tweets: ')) 

retweet = input('Exclude Retweets? (y/n): ') 

replies = input('Exclude Replies? (y/n): ') 

 

Which field can we Scrape? 

Here is the list of fields which we can scrape using Snscrape Library. 

• url: str 

• date: datetime.datetime 
• rawContent: str 
• renderedContent: str 
• id: int 
• user: ‘User’ 

• replyCount: int 
• retweetCount: int 
• likeCount: int 
• quoteCount: int 
• conversationId: int 

• lang: str 
• source: str 
• sourceUrl: typing.Optional[str] = None 
• sourceLabel: typing.Optional[str] = None 
• links: typing.Optional[typing.List[‘TextLink’]] = None 

• media: typing.Optional[typing.List[‘Medium’]] = None 
• retweetedTweet: typing.Optional[‘Tweet’] = None 
• quotedTweet: typing.Optional[‘Tweet’] = None 
• inReplyToTweetId: typing.Optional[int] = None 
• inReplyToUser: typing.Optional[‘User’] = None 

• mentionedUsers: typing.Optional[typing.List[‘User’]] = None 
• coordinates: typing.Optional[‘Coordinates’] = None 
• place: typing.Optional[‘Place’] = None 
• hashtags: typing.Optional[typing.List[str]] = None 
• cashtags: typing.Optional[typing.List[str]] = None 

• card: typing.Optional[‘Card’] = None 

 

For this tutorial we will not scrape all the fields but a few relevant fields from the above list. 

The search function

Next, we will define a search function which takes in the following inputs as arguments and creates a query string to be passed inside SNS twitter search scraper function. 

• Text 
• Username 
• Since 
• Until 
• Retweet 

• Replies 

 

def search(text,username,since,until,retweet,replies): 

    global filename 

    q = text 

    if username!='': 

        q += f" from:{username}"     

    if until=='': 

        until = datetime.datetime.strftime(datetime.date.today(), '%Y-%m-%d') 

    q += f" until:{until}" 

    if since=='': 

        since = datetime.datetime.strftime(datetime.datetime.strptime(until, '%Y-%m-%d') -  

                                           datetime.timedelta(days=7), '%Y-%m-%d') 

    q += f" since:{since}" 

    if retweet == 'y': 

        q += f" exclude:retweets" 

    if replies == 'y': 

        q += f" exclude:replies" 

    if username!='' and text!='': 

        filename = f"{since}_{until}_{username}_{text}.csv" 

    elif username!="": 

        filename = f"{since}_{until}_{username}.csv" 

    else: 

        filename = f"{since}_{until}_{text}.csv" 

    print(filename) 

    return q 

 

Here we have defined different conditions and based on those conditions we are creating the query string. For example, if variable until (end date) is empty then we are assigning it the current date and appending it in a query string and if the variable since (start date) is empty then we are assigning it a date of past 7 days from the current date. Along with the query string, we are creating filename string which will be used to name our csv file. 

 

 

Calling the Search Function and creating Dataframe 

 

q = search(text,username,since,until,retweet,replies) 

# Creating list to append tweet data  

tweets_list1 = [] 

 

# Using TwitterSearchScraper to scrape data and append tweets to list 

if count == -1: 

    for i,tweet in enumerate(tqdm_notebook(sntwitter.TwitterSearchScraper(q).get_items())): 

        tweets_list1.append([tweet.date, tweet.id, tweet.rawContent, tweet.user.username,tweet.lang, 

        tweet.hashtags,tweet.replyCount,tweet.retweetCount, tweet.likeCount,tweet.quoteCount,tweet.media]) 

else: 

    with tqdm_notebook(total=count) as pbar: 

        for i,tweet in enumerate(sntwitter.TwitterSearchScraper(q).get_items()): #declare a username  

            if i>=count: #number of tweets you want to scrape 

                break 

            tweets_list1.append([tweet. Date, tweet.id, tweet.rawContent, tweet.user.username,tweet.lang,tweet.hashtags,tweet.replyCount, 

                                tweet.retweetCount,tweet.likeCount,tweet.quoteCount,tweet.media]) 

            pbar.update(1) 

# Creating a dataframe from the tweets list above  

tweets_df1 = pd.DataFrame(tweets_list1, columns=['DateTime', 'TweetId', 'Text', 'Username','Language', 

                                'Hashtags','ReplyCount','RetweetCount','LikeCount','QuoteCount','Media']) 

 

 

 

In this snippet we have invoked the search function and the query string is stored inside variable ‘q’. Next, we have defined an empty list which will be used for appending tweet data. If the count is specified as -1 then the for loop will iterate over all the tweets.

TwitterSearchScraper class constructor takes the query string as an argument and then we invoke the get_items() method of TwitterSearchScraper class to retrieve all the tweets. Inside for loop we append scraped data in the tweets_list1 variable which we defined earlier. If count is defined, then we use it to break the for loop. Finally, using this list, we create the pandas dataframe by specifying the column names. 

 

tweets_df1.sort_values(by='DateTime',ascending=False) 

 

Data Preprocessing

Before saving the data frame in a csv file, we will first process the data, so that we can easily perform analysis on it. 

 

 

Data Description 

tweets_df1.info() 

 

Data Transformation 

Now we will add more columns to facilitate timeseries analysis 

tweets_df1['Hour'] = tweets_df1['DateTime'].dt.hour 

tweets_df1['Year'] = tweets_df1['DateTime'].dt.year   

tweets_df1['Month'] = tweets_df1['DateTime'].dt.month 

tweets_df1['MonthName'] = tweets_df1['DateTime'].dt.month_name() 

tweets_df1['MonthDay'] = tweets_df1['DateTime'].dt.day 

tweets_df1['DayName'] = tweets_df1['DateTime'].dt.day_name() 

tweets_df1['Week'] = tweets_df1['DateTime'].dt.isocalendar().week 

 

The Datetime column contains both date and time, therefore it is better to split data and time in separate columns. 

tweets_df1['Date'] = [d.date() for d in tweets_df1['DateTime']] 

tweets_df1['Time'] = [d.time() for d in tweets_df1['DateTime']] 

 

After splitting we will drop the DateTime column. 

tweets_df1.drop('DateTime',axis=1,inplace=True) 

tweets_df1 

 

Finally our data is prepared, we will now save the dataframe as csv using df.to_csv() function which takes filename as an input parameter. 

tweets_df1.to_csv(f"{filename}",index=False)

Visualizing timeseries data using barplot, lineplot, histplot and kdeplot 

It is time to visualize our prepared data so that we can find useful insights. Firstly, we will load the saved csv in a dateframe using the read_csv() function of pandas which take filename as input parameter. 

tweets = pd.read_csv("2018-01-01_2022-09-27_DataScienceDojo.csv") 

tweets 

 

 

Count by Year 

The countplot function of seaborn allows us to plot count of tweets by year. 

f, ax = plt.subplots(figsize=(15, 10)) 

sns.countplot(x= tweets['Year']) 

for p in ax.patches: 

    ax.annotate(int(p.get_height()), (p.get_x()+0.05, p.get_height()+20), fontsize = 12) 

 

 

plt.figure(figsize=(15, 8)) 

 

ax=plt.subplot(221) 

sns.lineplot(tweets.Year.value_counts()) 

ax.set_xlabel("Year") 

ax.set_ylabel('Count') 

plt.xticks(np.arange(2018,2023,1)) 

 

plt.subplot(222) 

sns.histplot(x=tweets.Year,stat='count',binwidth=1,kde='true',discrete=True) 

plt.xticks(np.arange(2018,2023,1)) 

plt.grid() 

 

plt.subplot(223) 

sns.kdeplot(x=tweets.Year,fill=True) 

plt.xticks(np.arange(2018,2023,1)) 

plt.grid() 

 

plt.subplot(224) 

sns.kdeplot(x=tweets.Year,fill=True,bw_adjust=3) 

plt.xticks(np.arange(2018,2023,1)) 

plt.grid() 

 

plt.tight_layout() 

plt.show() 

 

 

Count by Month 

We will follow the same steps for count by month, by week, by day of month and by hour. 

 

f, ax = plt.subplots(figsize=(15, 10)) 

sns.countplot(x= tweets['Month']) 

for p in ax.patches: 

    ax.annotate(int(p.get_height()), (p.get_x()+0.05, p.get_height()+20), fontsize = 12) 

 

 

plt.figure(figsize=(15, 8)) 

 

ax=plt.subplot(221) 

sns.lineplot(tweets.Month.value_counts()) 

ax.set_xlabel("Month") 

ax.set_ylabel('Count') 

plt.xticks(np.arange(1,13,1)) 

 

plt.subplot(222) 

sns.histplot(x=tweets.Month,stat='count',binwidth=1,kde='true',discrete=True) 

plt.xticks(np.arange(1,13,1)) 

plt.grid() 

 

plt.subplot(223) 

sns.kdeplot(x=tweets.Month,fill=True) 

plt.xticks(np.arange(1,13,1)) 

plt.grid() 

 

plt.subplot(224) 

sns.kdeplot(x=tweets.Month,fill=True,bw_adjust=3) 

plt.xticks(np.arange(1,13,1)) 

plt.grid() 

 

plt.tight_layout() 

plt.show() 

 

 

 

Count by Week 

f, ax = plt.subplots(figsize=(15, 10)) 

sns.countplot(x= tweets['Week']) 

for p in ax.patches: 

    ax.annotate(int(p.get_height()), (p.get_x()+0.005, p.get_height()+5), fontsize = 10) 

 

 

 

plt.figure(figsize=(15, 8)) 

 

ax=plt.subplot(221) 

sns.lineplot(tweets.Week.value_counts()) 

ax.set_xlabel("Week") 

ax.set_ylabel('Count') 

 

plt.subplot(222) 

sns.histplot(x=tweets.Week,stat='count',binwidth=1,kde='true',discrete=True) 

plt.grid() 

 

plt.subplot(223) 

sns.kdeplot(x=tweets.Week,fill=True) 

plt.grid() 

 

plt.subplot(224) 

sns.kdeplot(x=tweets.Week,fill=True,bw_adjust=3) 

plt.grid() 

 

plt.tight_layout() 

plt.show()  

 

 

 

Count by Day of Month 

f, ax = plt.subplots(figsize=(15, 10)) 

sns.countplot(x= tweets['MonthDay']) 

for p in ax.patches: 

    ax.annotate(int(p.get_height()), (p.get_x()+0.05, p.get_height()+5), fontsize = 12) 

 

 

plt.figure(figsize=(15, 8)) 

 

ax=plt.subplot(221) 

sns.lineplot(tweets.MonthDay.value_counts()) 

ax.set_xlabel("MonthDay") 

ax.set_ylabel('Count') 

 

plt.subplot(222) 

sns.histplot(x=tweets.MonthDay,stat='count',binwidth=1,kde='true',discrete=True) 

plt.grid() 

 

plt.subplot(223) 

sns.kdeplot(x=tweets.MonthDay,fill=True) 

plt.grid() 

 

plt.subplot(224) 

sns.kdeplot(x=tweets.MonthDay,fill=True,bw_adjust=3) 

plt.grid() 

 

plt.tight_layout() 

plt.show() 

 

 

 

 

 

 

 

 

 

Count by Hour 

f, ax = plt.subplots(figsize=(15, 10)) 

sns.countplot(x= tweets['Hour']) 

for p in ax.patches: 

    ax.annotate(int(p.get_height()), (p.get_x()+0.05, p.get_height()+20), fontsize = 12) 

 

 

plt.figure(figsize=(15, 8)) 

 

ax=plt.subplot(221) 

sns.lineplot(tweets.Hour.value_counts()) 

ax.set_xlabel("Hour") 

ax.set_ylabel('Count') 

plt.xticks(np.arange(0,24,1)) 

 

plt.subplot(222) 

sns.histplot(x=tweets.Hour,stat='count',binwidth=1,kde='true',discrete=True) 

plt.xticks(np.arange(0,24,1)) 

plt.grid() 

 

plt.subplot(223) 

sns.kdeplot(x=tweets.Hour,fill=True) 

plt.xticks(np.arange(0,24,1)) 

plt.grid() 

 

plt.subplot(224) 

sns.kdeplot(x=tweets.Hour,fill=True,bw_adjust=3) 

#plt.xticks(np.arange(0,24,1)) 

plt.grid() 

 

plt.tight_layout() 

plt.show() 

 

 

 

Conclusion 

From the above time series visualizations, we can clearly understand that the peak hours of tweets from this account is between 7pm-9pm and from 4am -1pm the twitter handle is quiet. We can also point out that most of the tweets related to that topic are done in the month of August. Similarly, we can identify that the Twitter handle was not very active before 2021.  

Conclusively, we saw how we can easily scrape tweets without using Twitter API through Snscrape. Then we performed some transformations on the scraped data and stored it in csv file. Later, we used that csv file for time-series visualizations and analysis. We appreciate you following along with this hands-on guide. We hope that this guide will make it easy for you to get started on your upcoming data science project. 

<<Link to Complete Code>> 

Data Science Dojo is offering Metabase for FREE on Azure Marketplace packaged with web accessible Metabase: Open-Source server. 

 

Introduction 

Organizations often adopt strategies that enhance the productivity of their selling points. One strategy is to utilize the prior business data to identify key patterns regarding any product and then take decisions for it accordingly. However, the work is quite hectic, costly, and requires domain experts. Metabase has bridged that gap of skillset. Metabase provides marketing and business professionals with an easy-to-use query builder notebook to extract required data and simultaneously visualize it without any SQL coding, with just a few clicks. 

What is Metabase and its question? 

Metabase is an open-source business intelligence framework that provides a web interface to import data from diverse databases and then analyze and visualize it with few clicks. The methodology of Metabase is based on questions and the answers to them. They form the foundation of everything else that it provides. 

            

A question is any kind of query that you want to perform on a data. Once you are done with the specification of query functions in the notebook editor, you can visualize the query results. After that you can save this question as well for reusability and turn it into a data model for business specific purposes. 

Pro Tip: Join our 6-months instructor-led Data Science Bootcamp to become expert at data science & analytics skillset  

Challenges for businesses  

For businesses that lack expert analysts, engineers and substantial IT department, it was costly and time-consuming to hire new domain experts or managers themselves learn to code and then explore and visualize data. Apart from that, not many pre-existing applications provide diverse data source connections which was also a challenge. 

In this regard, a straightforward interactive tool that even newbies could adapt immediately and thus get the job done would be the most ideal solution. 

Data analytics with Metabase  

Metabase concept is based on questions which are basically queries and data models (special saved questions). It provides an easy-to-use notebook through which users can gather raw data, filter it, join tables, summarize information, and add other customizations without any need for SQL coding.

Users can select the dimensions of columns from tables and then create various visualizations and embed them in different sub-dashboards. Metabase is frequently utilized for pitching business proposals to executive decision-makers because the visualizations are very simple to achieve from raw data. 

 

 

 

Major characteristics 

• Metabase delivers a notebook that enables users to select data, join with other tables, filter, and other operations just by clicking on options instead of writing a SQL query 

• In case of complex queries, a user can also use an in-built optimized SQL editor 

• The choice to select from various data sources like PostgreSQL, MongoDB, Spark SQL, Druid, etc., makes Metabase flexible and adaptable 

• Under the Metabase admin dashboard, users can troubleshoot the logs regarding different tasks and jobs 

• Has the ability to enable public sharing. It enables admins to create publicly viewable links for Questions and Dashboards  

What Data Science Dojo has for you  

Metabase instance packaged by Data Science Dojo serves as an open-source easy-to-use web interface for data analytics without the burden of installation. It contains numerous pre-designed visualization categories waiting for data.

It has a query builder which is used to create questions (customized queries) with few clicks. In our service users can also use an in-browser SQL editor for performing complex queries. Any user who wants to identify the impact of their product from the raw business data can use this tool. 

Features included in this offer:  

• A rich web interface running Metabase: Open Source 
• A no-code query building notebook editor 
• In-browser optimized SQL editor for complex queries 
• Beautiful interactive visualizations 
• Ability to create data models 
• Email configuration and Slack support 
• Shareability feature 
• Easy specification for metrics and segments 
• Feature to download query results in CSV, XLSX and JSON format 

Our instance supports the following major databases: 

• Druid 
• PostgreSQL 
• MySQL 
• SQL Server 
• Amazon Redshift 
• Big Query 
• Snowflake 
• Google Analytics 
• H2 
• MongoDB 
• Presto 
• Spark SQL 
• SQLite 

Conclusion  

Metabase is a business intelligence software and beneficial for marketing and product managers. By making it possible to share analytics with various teams within an enterprise, Metabase makes it simple for developers to create reports and collaborate on projects. The responsiveness and processing speed are faster than the traditional desktop environment as it uses Microsoft cloud services. 

At Data Science Dojo, we deliver data science education, consulting, and technical services to increase the power of data. We are therefore adding a free Metabase server dedicated specifically for Data Analytics operations on Azure Market Place. Hurry up and install this offer by Data Science Dojo, your ideal companion in your journey to learn data science!  

Click on the button below to head over to the Azure Marketplace and deploy Metabase for FREE by clicking on “Get it now”. 

Note: You’ll have to sign up to Azure, for free, if you do not have an existing account. 

In this blog, we are going to discuss data mining definition, steps, and examples. Mining sounds tedious, unfruitful, and manual. After all hacking away at rock walls for hours, hoping to find gold sounds like a lot of work. But don’t hesitate, data mining is actually quite the opposite.  

With the right use of data mining, you can reap rewarding benefits without making much effort. That is because it uses modern solutions which do all the tiring jobs for us. These modern solutions are designed to sift through terabytes of data within minutes. These modern solutions provide us with valuable insights, patterns, journeys, and relationships in the data.  

Read about process mining in this blog:

Process mining: Introducing event log mining

 

So, let’s dive into what data mining is and what its examples look like.  

 It is a type of analytical process that identifies meaningful trends and relationships in raw data. This is typically done to predict future data. Data mining tools come through large patches of data sets. They consist of a broad range of techniques to discover data structures. These techniques mainly include anomalies, patterns, journeys, or correlations. 

Three data mining techniques 

Data mining has been around the early 1900’s, the data mining we use today comprises three disciplines: 

1. The first is statistics, the numerical study of data relationships 
2. The second is artificial intelligence, the extreme human-like intelligence displayed by software or machines.  
3. Lastly, we have machine learning. The ability to automatically learn from data with minimal human assistance.  

 

If you want to improve your learning in data mining, watch this video

 

Altogether, these three processes have helped us move beyond the tedious processes of the past and onto simpler and better automations for today’s complex data sets. In fact, the more complex and varied these data sets are, the more relevant and accurate their insights will be. By unveiling structures within the data, data mining yield insights that can be used by companies to: 

• Anticipate and solve problems 

• Plan for the future 

• Make informed decisions 

• Mitigate risks  

• Seize new opportunities to grow 

6 Steps of data mining process 

The overall process of data mining generally consists of six steps: 

1. Outline your business goals

It is important to understand your business objectives thoroughly. This will allow you to set the most accurate project parameters which include the period and scope of data, the primary objective of the need in question and the criteria needed to identify it as a success.  

2. Understand your data sources

Understand your data sources with a deeper grasp of your project parameters. You will be able to understand which platforms and databases are necessary to solve the problem. Whether it is from your CRM or excel spreadsheets. It helps you to identify which sources best provide the relevant data needed.  

3. Preparing your data

In this step, you will use the ETL process. ETL stands for Extract, Transform, and Load. This prepares the data ensuring it is collected from various selected sources, cleaned, and then collated.  

4. Analyzing your data

At this stage, the organized data is fed into an advanced application and different machine learning algorithms get to work on identifying relationships and patterns that can help inform decisions and forecast future trends. This application organizes the elements of data also known as your data points and standardizes how they relate to one another.

For instance, one data model for a shoe product is composed of other elements such as color, size, method of purchase, location of purchase and buyer personality type.  

5. Reviewing the results

At this step, you will be able to determine if and how well the results and insights delivered by the model can assist in confirming your predictions, answering your questions, and achieving business objectives. (“Data Mining: What It Is, How To Use It, and Why It’s Important – LinkedIn”)  

6. Deployment or implementation

Once the data mining project is completed, the results are presented to the decision makers with the help of reports. Decision makers then analyze the reports and decide, how they would like to use that information to achieve their business objectives. That’s how we implement the insight to solve real-life problems.  

 

Key benefits of data mining functionalities 

Data mining offers several benefits including increased accuracy and speed, reduced costs, improved decision-making capability and flexibility, reduced human error rates and time-to-market. Data miners also have access to a wider pool of information than traditional analysts which makes them better able to identify patterns that are happening around them in their organizations’ data sets.  

 

Read more about text mining process in this blog:

Text mining: Easy steps to convert structured to the unstructured

They can also be used for predictive analytics which aim at predicting future trends based on past events like sales volumes or customer behavior patterns – all without having to manually sift through mountains of data! 

Data miners are typically used by businesses who want to improve their decision-making capabilities by using machine learning algorithms like deep learning models as well as artificial intelligence systems for machine learning algorithms (AI).  

Data miners are used by businesses who want to improve their decision-making capabilities by using machine learning algorithms like deep learning models as well as artificial intelligence systems for machine learning algorithms (AI). 

 Is your organization using data mining?

Data mining has become more and more popular in recent years. Data mining can help companies in many ways from predicting trends to identifying new opportunities and new products. As a result, data mining has become one of the most important aspects of business today.

In fact, data mining has even been proven to be an effective way to uncover insights into complex situations such as fraud detection or credit risk management.  

Learn data mining in one-hour with this crash course:

 

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Purpose of product analytics  

Product analytics is a comprehensive collection of mechanisms for evaluating the performance of digital ventures created by product teams and managers. 

Businesses often need to measure the metrics and impact of their products, for e.g., how the audience perceives their product like how many visitors are reading a particular page or clicking on a specific button. This gives an insight into what future decisions need to be taken regarding any product. Whether it should be modified? or removed? or kept as it is? Countly has made this work easier by providing a centralized web analytics environment to track the user engagement with a product along with monitoring its health.  

  

Pro Tip: Join our 6-months instructor-led Data Science Bootcamp to become expert at data science & analytics skillset  

Challenges for individuals  

Many platforms require developers for coding to visualize analytics which is not only time consuming but also come at a cost. At the application level, having an app crash leaves anyone in shock, and that is followed by a hectic task of determining the root cause of the problem which is time-consuming. At the corporate level, the current and past data needs to be analyzed appropriately for the future strength of the company and that requires robust analysis easily acquired by anyone which was a challenge faced by many organizations  

Countly analytics 

Countly enables users to monitor and analyze the performance of their applications irrespective of the platform in real-time. It can compile data from numerous sources and presents it in a manner that makes it easier for business analysts and managers to evaluate app usage and client behavior. It offers a customizable dashboard with the freedom to innovate and improve your products in order to meet important business and revenue objectives while also ensuring privacy by design. It is a world leader in product analytics because it tracks more than 1.5 billion unique identities on more than 16,000 applications and more than 2,000 servers worldwide. 

 

 

 

 

 

 

Major characteristics 

• Interactive web interface: User-friendly web environment with customizable dashboards for easy accessibility along with pre-designed metrics and visualizations 
• Platform-independent: Supports web analytics, mobile app analytics, and desktop application analytics for macOS and Windows 
• Alerts and email reporting: Ability to receive alerts based on the metric changes and provides custom email reporting 

• Users’ role and access manager: Provides global administrators the ability to manage users, groups, and their roles and permissions 
• Logs Management: Maintains server and audit logs on the web server regarding user actions on data 

What Data Science Dojo has for you  

Countly Server packaged by Data Science Dojo provides a web analytics service that provides insights about your product in real-time, no matter if it’s a web application or mobile app, or even desktop application without the burden of installation. It comes with numerous pre-configured metrics and visualization templates to import data and observe trends. It’s helpful for businesses to identify the application usage and determine the client response to the apps.  

Features included in this offer:  

• A VM configured with Countly Server: Community Edition accessible from a web browser 
• Ability to track user analytics, user loyalty, session analytics, technology, and geo insights  
• Easy-to-use customizable dashboard 
• Logs manager 
• Alerting and reporting feature 

• User permissions and roles manager 
• Built-in Countly DB viewer 
• Cache management 
• Flexibility to define data limits  

Conclusion  

Countly provides the feasibility to analyze data in real-time. It is highly extensible and possesses various features to manage different operations like alerting, reporting, logging, job management, etc. The analytics throughput can be increased by using multi-cores on Azure Virtual Machine. Also, Countly can handle different platform applications at once. This might slow down the server if you have thousands upon thousands of active client requests on different applications. The CPU and RAM usage may also be affected but through Azure Virtual Machine all these problems are taken care of. 

At Data Science Dojo, we deliver data science education, consulting, and technical services to increase the power of data. We are therefore adding a free Countly Server dedicated specifically for Data Analytics operations on Azure Market Place. Hurry up and install this offer by Data Science Dojo, your ideal companion in your journey to learn data science! 

Click on the button below to head over to the Azure Marketplace and deploy Countly for FREE by clicking on “Try now”. 

Note: You’ll have to sign up to Azure, for free, if you do not have an existing account. 

 

Get hired as a Data Analyst by confidently responding to the most frequently asked interview questions. No matter how qualified or experienced you are, if you stumble over your thoughts while answering the interviewer, it might take away some of your chances of getting onboard. 

 

In this blog, you will find the top data analysts interview questions covering both technical and non-technical areas of expertise.  

List of Data Analysts interview questions 

1. Share about your most successful/most challenging data analysis project? 

In this question, you can also share your strengths and weaknesses with the interviewer.   

When answering questions like these, data analysts must attempt to share both their strengths and weaknesses. How do you deal with challenges and how do you measure the success of a data project? You can discuss how you succeeded with your project and what made it successful.  

Take a look at the original job description to see if you can incorporate some of the requirements and skills listed. If you were asked the negative version of the question, be honest about what went wrong and what you would do differently in the future to fix the problem. Despite our human nature, mistakes are a part of life. What’s critical is your ability to learn from them. 

Further talk about any SAAS platforms, programming languages, and libraries. Why did you use them and how did you use them to accomplish yours?

Discuss the entire pipeline of your projects from collecting data, to turning it into valuable insights. Describe the ETL pipeline, including data cleaning, data preprocessing, and exploratory data analysis. What were your learnings and what issues did you encounter, and how did you deal with them. 

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2. Tell us about the largest data set you’ve worked with? Or what type of data have you worked with in the past? 

What they’re really asking is: Can you handle large data sets?  

Data sets of varying sizes and compositions are becoming increasingly common in many businesses. Answering questions about data size and variety requires a thorough understanding of the type of data and its nature. What data sets did you handle? What types of data were present? 

It is not necessary that you only mention a dataset you worked with at your job. But you can also share about varying sizes, specifically large datasets, you worked with as a part of a data analysis course, Bootcamp, certificate program, or degree. As you put together a portfolio, you may also complete some independent projects where you find and analyze a data set. All of this is valid material to build your answer.  

The more versatile your experience with datasets will be, the greater the chances there are of getting hired.  

Read more about several types of datasets here:

32 datasets to uplift your skills in data science

 

3. What is your process for cleaning data? 

The expected answer to this question will include details about: How you handle missing data, outliers, duplicate data, etc.?c.? 

Data analysts are widely responsible for data preparation, data cleansing, or data cleaning. Organizations expect data analysts to spend a significant amount of time preparing data for an employer. As you answer this question, share in detail with the employer why data cleaning is so important. 

In your answer, give a short description of what data cleaning is and why it’s important to the overall process. Then walk through the steps you typically take to clean a data set. 

 Learn about Data Science Interview Questions and begin your career as a data scientist today.

4. Name some data analytics software you are familiar with. OR what data software have you used in the past? OR What data analytics software are you trained in? 

What they need to know: Do you have basic competency with common tools? How much training will you need? 

Before you appear for the interview, it’s a good time to look at the job listing to see what software was mentioned. As you answer this question, describe how you have used that software or something similar in the past. Show your knowledge of the tool by employing associated words.  

Mention software solutions you have used for a variety of data analysis phases. You don’t need to provide a lengthy explanation. What data analytics tools you used and for what purpose will satisfy the interviewer. 

  

5. What statistical methods have you used in data analysis? OR what is your knowledge of statistics? OR how have you used statistics in your work as a Data Analyst? 

What they’re really asking: Do you have basic statistical knowledge? 

Data analysts should have at least a rudimentary grasp of statistics and know-how that statistical analysis helps business goals. Organizations look for a sound knowledge of statistics in Data analysts to handle complex projects conveniently. If you used any statistical calculations in the past, be sure to mention it. If you haven’t yet, familiarize yourself with the following statistical concepts: 

• Mean 
• Standard deviation 
• Variance
• Regression 
• Sample size 
• Descriptive and inferential statistics 

While speaking of these, share information that you can derive from them. What knowledge can you gain about your dataset? 

Read these amazing 12 Data Analytics books to strengthen your knowledge

 

12 excellent Data Analytics books you should read in 2022

 

6. What scripting languages are you trained in? 

In order to be a data analyst, you will almost certainly need both SQL and a statistical programming language like R or Python. If you are already proficient in the programming language of your choice at the job interview, that’s fine. If not, you can demonstrate your enthusiasm for learning it.  

In addition to your current languages’ expertise, mention how you are developing your expertise in other languages. If there are any plans for completing a programming language course, highlight its details during the interview. 

To gain some extra points, do not hesitate to mention why and in which situations SQL is used, and why R and python are used. 

 

7. How can you handle missing values in a dataset? 

This is one of the most frequently asked data analyst interview questions, and the interviewer expects you to give a detailed answer here, and not just the name of the methods. There are four methods to handle missing values in a dataset. 

• Listwise Deletion 

In the listwise deletion method, an entire record is excluded from analysis if any single value is missing. 

• Average Imputation  

Take the average value of the other participants’ responses and fill in the missing value. 

• Regression Substitution 

You can use multiple-regression analyses to estimate a missing value. 

• Multiple Imputations 

It creates plausible values based on the correlations for the missing data and then averages the simulated datasets by incorporating random errors in your predictions. 

 

8. What is Time Series analysis? 

Data analysts are responsible for analyzing data points collected at different intervals. While answering this question you also need to talk about the correlation between the data evident in time-series data. 

Watch this short video to learn in detail:

 

9. What is the difference between data profiling and data mining?

Profiling data attributes such as data type, frequency, and length, as well as their discrete values and value ranges, can provide valuable information on data attributes. It also assesses source data to understand its structure and quality through data collection and quality checks. 

On the other hand, data mining is a type of analytical process that identifies meaningful trends and relationships in raw data. This is typically done to predict future data. 

 

10. Explain the difference between R-Squared and Adjusted R-Squared.

The most vital difference between adjusted R-squared and R-squared is simply that adjusted R-squared considers and tests different independent variables against the model, and R-squared does not. 

An R-squared value is an important statistic for comparing two variables. However, when examining the relationship between a single stock and the rest of the S&P500, it is important to use adjusted R-squared to determine any discrepancies in correlation. 

 

11. Explain univariate, bivariate, and multivariate analysis.

Bivariate analysis, which is simpler than univariate analysis, is used when the data set only has one variable and does not involve causes or effects.  

Univariate analysis, which is more complicated than bivariate analysis, is used when the data set has two variables and researchers are looking to compare them.  

When the data set has two variables and researchers are investigating similarities between them, multivariate analysis is the right type of statistical approach. 

 

12. How would you go about measuring the business performance of our company, and what information do you think would be most important to consider?

Before appearing for an interview, make sure you study the company thoroughly and gain enough knowledge about it. It will leave an impression on the employer regarding your interest and enthusiasm to work with them. Also, in your answer you talk about the added value you will bring to the company by improving its business performance. 

 

13. What do you think are the three best qualities that great data analysts share?

List down some of the most critical qualities of a Data Analyst. This may include problem-solving, research, and attention to detail. Apart from these qualities, do not forget to mention soft skills, which are necessary to communicate with team members and across the department.    

 

Are you interested in learning more about data science for a boost to your professional career? Join our Data Science Bootcamp and learn all you need to know about the world of data!

Data Science Dojo is offering Apache Superset for FREE on Azure Marketplace packaged with pre-installed SQL lab and interactive visualizations to get started.  

  

What is Business Intelligence?  

  

Business Intelligence (BI) depends on the idea of utilizing information to perform activities. It expects to give business pioneers noteworthy bits of knowledge through data handling and analytics. For instance, a business breaks down the KPIs (Key Performance Indicators) to distinguish its benefits and shortcomings. Hence, the decision-makers can conclude in which department the organization can work to increase efficiency.  

Recently two elements in BI have resulted in sensational enhancements in metrics like speed and proficiency. The two elements include:  

 

• Automation  
• Data Visualization  

 

Apache Superset widely focuses on the latter model which has changed the course of business insights.  

 

But what were the challenges faced by analysts before there were popular exploratory tools like Superset?  

  

Pro Tip: Join our 6-months instructor-led Data Science Bootcamp to master data science.  

  

Challenges of Data Analysts

  

Scalability, framework compatibility, and absence of business-explicit customization were a few challenges faced by data analysts. Apart from that exploring petabytes of data and visualizing it would cause the system to collapse or hang at times.  

In these circumstances, a tool having the ability to query data as per business needs and envision it in various diagrams and plots was required. Additionally, a system scalable and elastic enough to handle and explore large volumes of data would be an ideal solution.  

 

Data Analytics with Superset  

  

Apache Superset is an open-source tool that equips you with a web-based environment for interactive data analytics, visualization, and exploration. It provides a vast collection of different types of vibrant and interactive visualizations, charts, and tables. It can customize the layouts and the dynamic dashboard elements along with quick filtering, making it flexible and user-friendly. Apache Superset is extremely beneficial for businesses and researchers who want to identify key trends and patterns from raw data to aid in the decision-making process.  

 

 

  

It is a powerhouse of SQL as it not only allows connection to several databases but also provides an in-browser SQL editor by the name SQL Lab  

 

Key attributes  

  

• Superset delivers an interactive UI that enriches the plots, charts, and other diagrams. You can customize your dashboard and canvas as per requirement. The hover feature and side-by-side layout make it coherent  
• An open-source easy-to-use tool with a no-code environment. Drag and drop and one-click alterations make it more user-friendly  

• Contains a powerful built-in SQL editor to query data from any database quickly  
• The choice to select from various databases like Druid, Hive, MySQL, SparkSQL, etc., and the ability to connect additional databases makes Superset flexible and adaptable  
• In-built functionality to create alerts and notifications by setting specific conditions at a particular schedule  
• Superset provides a section about managing different users and their roles and permissions. It also has a tab for logging the ongoing events  

 

What does Data Science Dojo have for you  

  

Superset instance packaged by Data Science Dojo serves as a web-accessible no-code environment with miscellaneous analysis capabilities without the burden of installation. It has many samples of chart and dataset projects to get started. In our service users can customize dashboards and canvas as per business needs.

It comes with drag-and-drop feasibility which makes it user-friendly and easy to use. Users can create different visualizations to detect key trends in any volume of data.  

 

What is included in this offer:  

 

• A VM configured with a web-accessible Superset application  
• Many sample charts and datasets to get started  
• In-browser optimized SQL editor called SQL Lab  
• User access and roles manager  
• Alert and report feature  
• Feasibility of drag and drop  
• In-build functionality of event logging  

 

Our instance supports the following major databases:  

 

• Druid  
• Hive  
• SparkSQL  
• MySQL  
• PostgreSQL  
• Presto  
• Oracle  
• SQLite  
• Trino  
• Apart from these any data engine that has Python DB-API driver and a SQL Alchemy dialect can be connected  

 

Conclusion  

  

Efficient resource requirement for exploring and visualizing large volumes of data was one of the areas of concern when working on traditional desktop environments. The other area of concern includes the ad-hoc SQL querying of data from different database connections. With our Superset instance, both concerns are put to rest.

When coupled with Microsoft cloud services and processing speed, it outperforms its traditional counterparts since data-intensive computations aren’t performed locally but in the cloud. It has a lightweight semantic layer and is designed as a cloud-native architecture.  

At Data Science Dojo, we deliver data science education, consulting, and technical services to increase the power of data. We are therefore adding a free Superset instance dedicated specifically to Data Science & Analytics on Azure Marketplace. Now hurry up and avail this offer by Data Science Dojo, your ideal companion in your journey to learn data science!  

  

Click on the button below to head over to the Azure Marketplace and deploy Apache Superset for FREE by clicking on “Get it now”.  

 

 

Note: You’ll have to sign up to Azure, for free, if you do not have an existing account.