data analyst

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

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

 

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

 

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

 

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

Technical skills to practice as a data analyst: 

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

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

1. Probability and statistics:  

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

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

 

2. Programming:  

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

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

3. Data visualization 

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

 

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

 

4. Database management:  

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

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

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

Non-technical skills to adopt as a data analyst:  

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

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

1. Communication 

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

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

 

Read about Data Storytelling and its importance

2. Problem-solving:   

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

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

3. Attention to detail: 

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

4. Time management:  

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

 

Final word 

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

 

13 most common Data Analysts interview questions you must prepare for
Ayesha Saleem
| October 24, 2022

Get hired as a Data Analyst by confidently responding to the most 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. 

 

data analyst interview question
Data analyst interview question – Data Science Dojo

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 you have worked with in the past? 

What they’re really asking: 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 should 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. 

 

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 which 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 it 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.    

 

Did we miss any Data Analysts interview questions? 

Share with us in the comments below and help each other to ace the next data analyst job. 

  

Hazel Jones
| October 11, 2022

Data analysis and data science are very closely related professions in many respects. If one enjoys problem-solving, data-driven decision-making, and critical thinking, both occupations are a good fit. While all alternatives draw on the same core skill set and strive toward comparable goals, there are differences in schooling, talents, daily responsibilities, and compensation ranges. 

 

The data science certification course offers insight into the tools, technology, and trends driving the data science revolution. We have developed this guide to enable you to go through the abilities and background required to become a data scientist or data analyst, and their corresponding course fee.

 

Data Scientist vs. Data Analyst

Data analysis and data science are often misunderstood since they rely on the same fundamental skills, not to mention the very same broad educational foundation (e.g., advanced mathematics, and statistical analysis). 

However, the day-to-day responsibilities of each role are vastly different. The difference, in its most basic form, is how they utilize the data they collect.

data analyst vs data scientist
Key differences between a data analyst and a data scientist

Role of a Data Analyst

A data analyst examines gathered information, organizes it, and cleans it to make it clear and helpful. Based on the data acquired, they make recommendations and judgments. They are part of a team that converts raw data into knowledge that can assist organizations in making sound choices and investments.

 

Role of a Data Scientist

A data scientist creates the tools that will be used by an analyst. They write programs, algorithms, and data-gathering technologies. Data scientists are innovative problem solvers who are constantly thinking of new methods to acquire, store, and view data.

 

Differences in the role of data scientist and data analyst

data analyst vs data scientist job role
Job roles of data analyst and data scientist

 

While both data analysts and data scientists deal with data, the primary distinction is what they do with it. Data analysts evaluate big data sets for insights, generate infographics, and generate visualizations to assist corporations in making better strategic choices. Data scientists, on the other hand, use models, methods, predictive analytics, and specialized analyses to create and build current innovations for data modeling and manufacturing.

 

Data experts and data scientists typically have comparable academic qualifications. Most have Bachelor’s degrees in economics, statistics, computer programming, or machine intelligence. They have in-depth knowledge of data, marketing, communication, and algorithms. They can work with advanced systems, databases, and Programming environments.

 

What is data analysis?

Data analysis is the thorough examination of data to uncover trends that can be turned into meaningful information. When formatted and analyzed correctly, previously meaningless data can become a wealth of useful and valuable information that firms in various industries can use.

 

Data analysis, for example, can tell a technical store what product is most successful at what period and with which population, which can then help employees decide what kind of incentives to run. Data analysis may also assist social media companies in determining when, what, and how they should promote particular users to optimize clicks.

 

What is data science?

Data science and data analysis both aim to unearth significant insights within piles of complicated or seemingly minor information. Rather than performing the actual analytics, data science frequently aims at developing the models and implementing the techniques that will be used during the process of data analysis.

 

While data analysis seeks to reveal insights from previous data to influence future actions, data science seeks to anticipate the result of future decisions. Artificial image processing and pattern recognition, which are still in their early stages, are used to create predictions based on large amounts of historical data.

 

Responsibilities: Data Scientist vs Data Analyst

Professionals in data science and data analysis must be familiar with managing data, information systems, statistics, and data analysis. They must alter and organize data for relevant stakeholders to find it useful and comprehensible. They also assess how effectively firms perform on predefined metrics, uncover trends, and explain the differentiated strategy. While job responsibilities frequently overlap, there are contrasts between data scientists and data analysts, and the methods they utilize to attain these goals.

 

Data Analyst Data Scientist
Data analyzers are expert interpreters. They use massive amounts of information to comprehend what is going on in the industry and how corporate actions affect how customers perceive and engage with the company. They are motivated by the need to understand people’s perspectives and behaviors through data analysis.  Data scientists build the framework for capturing data and better understanding the narrative it conveys about the industry, enterprise, and decisions taken. They are designers that can create a system that can handle the volume of data required while also making it valuable for understanding patterns and advising the management team. 
Everyday data analyst tasks may involve examining both historical and current patterns and trends. Data scientists are typically responsible for the scrubbing and information retrieval.
Create operational and financial reports. Data collection statistical analysis.
Forecasting in tools such as Excel. Deep learning framework training and development.
Designing infographics. Creating architecture that can manage large amounts of data.
Data interpretation and clear communication. Developing automation that streamlines data gathering and processing chores daily.
Data screening is accomplished by analyzing documents and fixing data corruption.  Presenting insights to the executive team and assisting with data-driven decision making
Using predictive modeling to discover and impact future trends.

 

Role: Data Scientist vs Data Analyst

Data Analyst job description

A data analyst, unsurprisingly, analyzes data. This entails gathering information from various sources and processing it via data manipulation and statistical techniques. These procedures organize and extract insights from data, which are subsequently given to individuals who may act on them.

Become a pro with Data Analytics with these 12 amazing books

Users and decision-makers frequently ask data analysts to discover answers to their inquiries. This entails gathering and comparing pertinent facts and stitching it together to form a larger picture. Knowledgehut looks more closely at a career path in analytics and science, and helps you determine which employment best matches your interests, experience, and ambitions.

 

Data Scientist job description

A data scientist can have various tasks inside a corporation, among which are very comparable to those of a data analyst, such as gathering, processing, and analyzing data to get meaningful information. 

 

Whereas a data analyst is likely to have been given particular questions to answer, a data scientist may indeed evaluate the same collection of data with the goal of diverse variables that may lead to a new line of inquiry. In other words, a data scientist must identify both the appropriate questions and the proper answers.

 

A data scientist will make designs and write algorithms and software to assist them as well as their research analyst team members with the analysis of data. A data scientist is also deeply engaged in the field of artificial intelligence and tries to push the limits and develop new methods to apply this technology in a corporate context.

 

How can Data Scientists become ethical hackers?

Yes, you heard it right. Data scientists can definitely become ethical hackers. There are several skills data scientists possess that can help them with the smooth transition from data scientists to ethical hackers. The skills are extensive knowledge of programming languages, databases, and operating systems. Data science is an important tool that can prevent hacking.

 

The necessary skills for a data scientist to become an ethical hacker include mathematical and statistical expertise, and extensive hacking skills. With the rise of cybercrimes, the need for cyber security is increasing. When data scientists become ethical hackers, they can protect an organization’s data and prevent cyber-attacks. 

 

Skill set required for data analysis and data science

 

Data analysis Data science
Qualification: A Bachelor’s or Master’s degree in a related discipline, such as mathematics or statistics. Qualification: An advanced degree, such as a master’s degree or possibly a Ph.D., in a relevant discipline, such as statistics, computer science, or mathematics.
Language skills: To understand data analysis, such as Python, SQL, CQL, and R. Language skills: Demonstrate proficiency in data-related programming languages such as SQL, R, Java, and Python.
Soft skills: 

  • Written and verbal communication skills
  • Exceptional analytical skills 
  • Organizational skills
  • The ability to manage many products at the same time may be required.
Soft skills: 

  • Substantial experience with data mining 
  • Specialized statistical activities and tools
  • Generating generalized linear model regressions, statistical tests, designing data structures, and text mining. 
Technical skills: 

  • Expertise in data gathering and some of the most recent data analytics technology.
Technical skills: 

  • Experience with data sources and web services
  • Web services such as Spark, Hadoop, DigitalOcean and S3 
  • Trained to use information obtained from third-party suppliers such as Google Analytic, Crimson Hexagon, Coremetrics, Site Catalyst
Microsoft Office proficiency: 

Proficient in Microsoft Office applications, notably Excel, to properly explain their findings and translate them for others to grasp. 

Knowledge of statistical techniques and technology: Data processing technologies such as MySQL and Gurobi, as well as technological advances such as machine learning models, deep learning, artificial intelligence, artificial neural networks, and decision tree learning, will play a significant role.

 

Conclusion 

Each career is a good fit for an individual who enjoys statistics, analytics, and evaluating business decisions. As a data analyst or data scientist, you will make logical sense of large amounts of data, articulate patterns and trends, and participate in great responsibilities in a corporate or government organization.

When picking between a data analytics and a data science profession, evaluate your career aspirations, skills, and how much time you want to devote to higher learning and intensive training. Start your data analyst or data scientist journey with a data science course with nominal data science course fee to learn in-demand skills used in realistic, long-term projects, strengthening your resume and commercial viability.

 

FAQs

 

  1. Which is better: Data science or data analyst?

Data science is suitable for candidates who want to develop advanced machine learning models and make human tasks easier. On the other hand, the data analyst role is appropriate for candidates who want to begin their career in data analysis. 

 

  1. What is the career path for data analytics and data science?

Most data analysts will begin their careers as junior members of a bigger data analysis team, where they will learn the fundamentals of the work in a hands-on environment and gain valuable experience in data manipulation. At senior level, data analysts become team leaders, in control of project selection and allocation.

A junior data scientist will most likely obtain a post with a focus on data manipulation before delving into the depths of learning algorithms and mapping out forecasts. The procedure of preparing data for analysis varies so much from case to case that it’s far simpler to learn by doing. 

Once conversant with the mechanics of data analysis, data scientists might expand their understanding of artificial intelligence and its applications by designing algorithms and tools. A more experienced data scientist may pursue team lead or management positions, distributing projects and collaborating closely with users and decision-makers. Alternatively, they could use their seniority to tackle the most difficult and valuable problems using their specialist expertise in patterns and machine learning.

 

  1. What is the salary for a data scientist and a data analyst in India?

2 to 4 years (Senior Data Analyst): $98,682 whereas the average data scientist salary is $100,560, according to the U.S. Bureau of Labor Statistics.

 

References

Difference Between Data Science and Data Analytics – GeeksforGeeks

Business analytics vs data science – Data Science Dojo

Data Analyst vs. Data Scientist: Key Differences Explained | Upwork

Data Analyst vs. Data Scientist: What’s the Difference? | Coursera

Data Analytics vs. Data Science: A Breakdown (northeastern.edu)

Data Analyst vs. Data Scientist: Salary, Skills, & Background (springboard.com)

Data Analyst vs. Data Scientist: Which Should You Pursue? – UT Austin Boot Camps (utexas.edu)

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