What Does a Data Scientist Do? Role, Skills and How to Become One

For example: An online shopping company may want to predict which products will sell best next month. A data scientist can analyse past sales, customer behaviour, and seasonal trends to build a model that estimates future demand. The company can then use these predictions to plan stock and marketing campaigns.

Skills and Tools a Data Scientist Needs

Data scientists need a mix of mathematical knowledge, programming skills, and practical experience with data. These skills help them analyse information, build models, and explain their findings.

1. Statistics and Applied Mathematics

Statistics helps data scientists identify patterns, test ideas, and draw conclusions from data. Important topics include:

• Probability: Understand how likely an event is to occur.
• Descriptive statistics: Summarise data using averages, percentages, and distributions.
• Statistical testing: Check whether patterns in data are meaningful.

These skills are useful for analysing results and checking how reliable a model's predictions are.

You can also explore statistician career path if you have interest in statistics and understand what a statistician does.

2. Programming and Coding

Programming helps data scientists clean data, automate tasks, and build machine learning models. Common languages include:

• Python: Used for data analysis, data cleaning, and machine learning.
• R: Useful for statistical analysis and visualisation.
• SQL: Used to retrieve and manage data stored in databases.

Python is a good starting point for beginners. You can learn more in this guide to Python for data science.

3. Big Data Technologies

Big data technologies help process large datasets that may be difficult to handle on a single computer. Data scientists use these tools to work with information from websites, apps, and business systems.

Some commonly used technologies are:

• Apache Hadoop: Supports distributed storage and processing of large datasets.
• Apache Spark: Processes large amounts of data across multiple computers.
• Apache Hive: Helps query and analyse data stored in distributed systems.

4. Databases and SQL

Data scientists often need to retrieve information from databases before analysing it. Understanding databases helps them find, organise, and manage the data required for a project.

Common database systems include:

• MySQL and PostgreSQL: Relational databases that organise data into tables.
• MongoDB: A database that stores data in flexible, document-based formats.
• Cassandra: A database designed to handle large amounts of data across multiple systems.

Also Read: Top 50 SQL Interview Questions With Answers

5. Multivariable Calculus and Linear Algebra

Calculus and linear algebra support many machine learning methods. They help data scientists understand how models learn patterns and make predictions.

Key concepts include:

• Vectors and matrices: Used to represent and process data.
• Derivatives and gradients: Help explain how model parameters change during training.
• Multivariable calculus: Helps understand relationships between multiple variables.

Beginners can learn these concepts gradually as they explore different machine learning methods.

6. Data Wrangling

Data wrangling, also called data munging, is the process of preparing raw data for analysis. Real-world datasets often contain missing values, duplicate records, errors, or inconsistent formats.

Common data-wrangling tasks include:

• Removing duplicate records and correcting errors.
• Handling missing values.
• Converting data into consistent formats.
• Combining information from different sources.

7. Data Visualisation

Data visualisation helps data scientists present findings in a clear and useful way. Charts, graphs, and dashboards make it easier for teams to understand patterns and make decisions.

Common visualisation tools include:

• Tableau and Power BI: Used to create interactive dashboards and reports.
• Matplotlib and Seaborn: Python libraries for creating charts and graphs.
• Plotly: Used to create interactive data visualisations.

If you are interested in finding out more about the different concepts and methods used in data science, make sure to check out upGrad’s Executive PG Program in Data Science courses. Taught by faculty members from top national and foreign universities.  

Data Scientist vs Data Analyst vs Machine Learning Engineer

Data scientists, data analysts, and machine learning engineers all work with data, but their responsibilities are different. Data analysts study data to find useful information. Data scientists use data to solve complex problems and build models. Machine learning engineers build and set up systems that learn from data.

For More Information, Read: Data Analyst vs Data Scientist - Spot the Difference

How Do These Roles Work Together?

Think about  an online shopping company that wants to improve product recommendations:

• What a data analyst does? : Studies customer purchases to identify popular products and shopping patterns.
• What a data scientist does? : Uses customer and sales data to develop a model that recommends relevant products.
• What a Machine learning engineer does? : Deploys the model into the shopping platform and maintains its performance.

These roles can overlap, mostly in smaller companies. The exact responsibilities depend on the organisation, project, and skills required.

Data Scientist Salary in India by Experience

A data scientist's salary in India depends on work experience, technical skills, industry, and employer. As professionals gain experience in data analysis, Python, statistics, and machine learning, they can qualify for higher-paying roles.

Source: AmbitionBox, checked on October 2026.

Understand Data, Features, and how models learn from patterns with Machine Learning Courses. You can master neural networks, NLP & reinforcement with Python through courses from Top Universities 

How to Become a Data Scientist: Degree, Certificate or Self-Study?

There are many different ways to enter data science, depending on your education, current skills, and learning goals. You can pursue a relevant degree, earn a certificate, or learn independently from online resources and practical projects.

1. Choose a Learning Path

• Degree: A bachelor's or master's degree in computer science, statistics, mathematics, or a related field can help build a strong foundation.
• Certificate: Online data science courses can help you learn key tools and concepts from well structured lessons and practical exercises.
• Self-study: Use online tutorials, books, and free datasets to learn Python, SQL, statistics, and machine learning at your own pace.

2. Gain Practical Experience Through Real Projects

Practical projects help you apply what you learn and build a portfolio for job applications.

• Start with projects like sales forecasting, customer segmentation, or customer churn prediction.
• Use public datasets to practise data cleaning, analysis, and model building.
• Explain the problem, your approach, and the results in each project.

You can find ideas in these real-world data science projects.

3. Take Part in Data Science Competitions

Platforms like Kaggle let you practise with real datasets, compare approaches, and learn from other data science learners. Competitions can help you improve your problem-solving skills and understand how others build models.

Can You Become a Data Scientist Without Coding?

You can start learning data analysis and some machine learning concepts using low-code or no-code tools, but coding is an important skill for most data scientist roles.

If you are a beginner, you can:

• Use tools like Power BI, Tableau, or KNIME to explore and visualise data.
• Learn basic statistics and how to interpret results.
• Practise SQL to retrieve data from databases.
• Gradually learn Python to clean data, build models, and automate tasks.

No-code tools can be a useful starting point, but learning programming will give you more options when applying for data scientist roles.

You can also get a free personalized career consultation with upGrad's experts and find out top programs, skill-based courses and degrees that can help you achieve a bright career in data science.

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