Data Science Roadmap: A 10-Step Guide to Success for Beginners and Aspiring Professionals

2. Projects and Practical Experience

Along with theory, you need hands-on experience. To build your portfolio, work on small, real-world projects using platforms like upGrad, Kaggle, Google Colab, or GitHub. 

For example:

• Create a model that predicts house prices using Python.
• Analyze public datasets, such as COVID-19 trends or customer buying behavior, to uncover insights.

You can try upGrad’s analyzing patterns and data storytelling course, which will help you explore pattern analysis and insight creation!

3. Networking and Continuous Learning

Stepping into the data science community can accelerate your learning. Networking connects you with mentors and peers who can guide you through challenges and provide invaluable career advice.

• Join LinkedIn communities like data science central, attend industry webinars, and participate in forums like Reddit’s r/datascience or Kaggle discussions.
• At the same time, keep updating your skills through platforms like upGrad, where you can explore emerging topics like deep learning, artificial intelligence, and more.

4. Internships and Work Experience

Internships provide practical exposure to industry workflows and tools like SQL, Python, and Tableau. These roles help you understand how businesses solve real-world problems using data. 

Companies like TCS, Infosys, and Wipro often offer internships for freshers and early professionals.

Step 2: Strengthen Your Mathematical Foundation

Mathematics forms the basis for understanding algorithms, interpreting data, and building machine learning models. To succeed in a data science career, you must develop a firm grasp of mathematical concepts and related technical skills.

1. Mathematics: The Foundation of Data Science

Mathematics underpins everything in data science, from solving optimization problems to designing algorithms. Key areas to focus on include:

• Linear Algebra: Essential for understanding machine learning models, particularly neural networks.
• Calculus: Helps optimize functions, a core aspect of training machine learning models.

2. Probability: Essential for Machine Learning

Probability enables data scientists to model uncertainty and randomness in data. It’s critical for designing predictive models and working with algorithms like Bayesian Networks and Naive Bayes.

3. Statistics: Core of Data Analysis

Statistics helps you make sense of data. You’ll use concepts like:

• Descriptive Statistics: (mean, median, variance) for summarizing data.
• Inferential Statistics: to draw conclusions and test hypotheses.

Mastering statistics is essential for data cleaning, feature selection, and model evaluation.

4. Programming: Building Data Science Expertise

Programming is how you bring mathematical concepts to life. Python and R are the most popular programming languages in data science due to their versatility and rich libraries like NumPy, Pandas, and Matplotlib.

5. Machine Learning: Understanding Algorithms

Machine learning is the heart of data science. Begin with supervised and unsupervised learning algorithms such as:

• Linear Regression, Logistic Regression
• K-Means Clustering, Decision Trees, and Random Forests

Understanding their mathematical foundations will equip you to tune models and solve real-world problems.

6. Deep Learning: Neural Networks and Beyond

Deep learning, a subset of machine learning, focuses on neural networks. These models are indispensable for tasks like image recognition and speech processing. Explore frameworks like TensorFlow and PyTorch to implement neural networks.

7. Feature Engineering: Improving Models

Feature engineering is the art of selecting, creating, or transforming variables to improve model performance. It requires creativity and domain knowledge to extract meaningful insights from raw data.

8. Natural Language Processing (NLP): Working with Text Data

With the rise of chatbots and voice assistants, NLP is crucial. Learn how to process and analyze text using techniques like tokenization, sentiment analysis, and topic modeling.

9. Data Visualization Tools: Presenting Insights

The ability to visualize data is as important as analyzing it. Tools like Tableau, Power BI, and Python Seaborn help create impactful dashboards and charts that make your insights accessible to stakeholders.

10. Deployment: Bringing Models into Production

Creating a model is only half the job. Deployment ensures your model generates value in real-world scenarios. Learn tools like Flask, FastAPI, or cloud platforms like AWS and Google Cloud for deploying machine learning models.

Some other key areas to focus on include:

• Big Data Tools: Master frameworks like Hadoop and Spark to handle large datasets.
• Version Control: Use Git to manage and collaborate on code.
• Domain Knowledge: Understanding the industry (e.g., finance, healthcare) adds significant value to your work.

Also Read: Top 10 Big Data Tools You Need to Know To Boost Your Data Skills in 2025

Step 3: Gain Expertise in Your Chosen Domain

While technical skills form the core of a data science career, domain expertise sets you apart. A deep understanding of domain-specific problems enables you to craft tailored solutions and create a real impact.

Here’s a breakdown of how domain expertise applies across industries:

1. Healthcare:

Predict patient outcomes, optimize hospital operations, or develop personalized treatments using electronic health records and advanced analytics.

• Example: Using PyCaret for anomaly detection in patient health data or training models to detect diseases like cancer with diagnostic imaging data.
• Datasets: Utilize resources like the MIMIC-III database (for electronic health records) or Chest X-ray datasets for medical imaging projects.

2. Finance:

Leverage predictive analysis for fraud detection, credit risk assessment, or algorithmic trading.

• Example: Employing NLP to analyze financial news for stock price predictions or TensorFlow for fraud detection in transaction datasets.
• Datasets: Use datasets like the Kaggle Credit Card Fraud Detection Dataset or Quandl’s financial data.

3. Retail and E-commerce:

Predict customer preferences, optimize supply chains, and enhance the shopping experience with recommendation systems.

• Example: Building recommendation engines using Scikit-learn or PySpark to analyze customer behavior data as Amazon does.
• Datasets: Explore datasets like the UCI Online Retail Dataset or the Kaggle Retail Product Data for customer purchase analysis.

4. Manufacturing:

Implement predictive maintenance to reduce downtime and optimize production lines using IoT data.

• Example: Using Hadoop or Apache Spark to process large-scale sensor data or deploying time-series models like ARIMA to predict equipment failure.
• Datasets: Refer to the NASA Turbofan Engine Degradation Dataset or the CMAPSS Dataset for predictive maintenance projects.

5. Marketing and Media:

Use data analytics to understand audience preferences, optimize ad campaigns, and enhance content strategies.

• Examples include utilizing Google Analytics Data APIs for audience insights or applying Tableau to create interactive dashboards for ad performance monitoring.
• Leverage datasets like the Facebook Ad Dataset on Kaggle for advertising analytics or the Google Merchandise Store Dataset for campaign analysis.

Follow relevant news sources like MIT technology reviews, learn industry tools like Tableau and Power BI, and collaborate with experts to understand how data science is transforming your chosen field.

Step 4: Develop Strong Computer Science Skills

To become a successful data scientist, a strong foundation in computer science is non-negotiable. Unlike the earlier focus on foundational skills, this step emphasizes advanced computer science knowledge that sets you apart.

1. Programming Languages (Python, R, SQL)

Programming is the backbone of data science workflows.

• Python: The most versatile language for data science, thanks to libraries like NumPy, Pandas, Scikit-learn, and Matplotlib.
• R: Ideal for statistical analysis and visualization.
• SQL: Crucial for querying and managing structured data in relational databases. Advanced SQL skills allow you to efficiently optimize complex queries and retrieve large datasets.

2. Data Structures

Mastering data structures helps you write optimized code for data manipulation and analysis. Focus on:

• Arrays and Linked Lists for efficient storage and retrieval.
• Hash Maps for fast lookups.
• Trees and Graphs to work on hierarchical or network-based datasets. 

Understanding these structures is critical for designing machine learning algorithms and handling large-scale data.

3. Distributed Computing (Hadoop, Spark)

As datasets grow exponentially, distributed computing becomes essential for scaling operations.

• Hadoop: An open-source framework for storing and processing large datasets in a distributed environment.
• Spark: Faster and more efficient than Hadoop, Spark is ideal for real-time analytics and stream processing.

4. Git, Linux, and MongoDB

• Git: Version control is indispensable for collaborative coding in data science projects. Learn to use Git to track changes, manage branches, and collaborate effectively on platforms like GitHub or GitLab.
• Linux: A majority of data science tools and servers run on Linux. Familiarity with shell commands, file systems, and processes is highly beneficial for managing systems efficiently.
• MongoDB: This NoSQL database is perfect for handling unstructured or semi-structured data like JSON files, making it highly relevant for projects involving diverse data formats.

Step 5: Improve Your Communication Abilities

Technical skills are vital in data science but are only half the equation. The ability to communicate your findings effectively is what turns analysis into action.

1. Communicate Complex Data Findings Clearly

The primary challenge lies in simplifying intricate data-driven insights for a non-technical audience. To do this:

• Know Your Audience: Tailor your explanations to the technical understanding of your audience, whether they’re C-suite executives or product managers.
• Storytelling with Data: Craft a narrative around your findings. Consider using the ABT Framework (And, But, Therefore) to structure your conclusions effectively: 
  • And: Start by setting the context. State the current situation and highlight the key data insights.
  • But: Introduce the challenge or conflict your analysis has uncovered.
  • Therefore: Conclude with a data-backed recommendation or actionable insight.
• Avoid Jargon: Use simple, relatable language and analogies to explain technical concepts and mathematical concepts. For example, you can explain variance as "how spread out the data points are." Use a relatable analogy, such as: 

"If you’re measuring how tall a group of people are, variance shows whether everyone is roughly the same height or if there are outliers, like a child standing next to a basketball player."

2. Create Insightful Data Visualizations and Reports

Data visualizations bridge the gap between raw data and actionable insights. Mastering this skill ensures your audience can grasp the significance of your findings at a glance.

• Choose the Right Visualization: Use bar charts for comparisons, line charts for trends, and heatmaps for correlations. Misaligned visualizations can confuse rather than clarify.
• Leverage Visualization Tools: Tools like Tableau, Power BI, Matplotlib, or Seaborn help you create compelling, interactive visuals.
• Focus on Clarity and Simplicity: Avoid clutter and highlight key takeaways in your charts and graphs.
• Actionable Reporting: Accompany visualizations with concise, well-structured reports summarizing your analysis, implications, and recommendations.

Step 6: Learn Core Concepts in Data Science

Mastering the core concepts of data science is essential to building a strong foundation for problem-solving and creating impactful solutions. Let’s explore the key areas you need to focus on.

1. Data Collection, Cleaning, and Preparation

The first step in any data science project is gathering and preparing data for analysis.

• Data Collection: Learn to source data from APIs, web scraping, databases, or public datasets.
• Data Cleaning: Handle missing values, outliers, and inconsistent formats to ensure reliable data.
• Data Preparation: Transform data into a usable format by scaling, encoding, or normalizing it for analysis or model training.

2. Explore and Visualize Data Effectively

Exploratory Data Analysis (EDA) helps you uncover patterns and relationships within data.

• Use libraries like Matplotlib and Seaborn or tools like Tableau to visualize data trends.
• Learn to identify correlations, distributions, and anomalies that inform model-building decisions.
• Visualization isn’t just about creating charts — it’s about telling a story through data.

Visualizing data allows you to generate hypotheses and communicate findings to stakeholders effectively.

3. Machine Learning and Deep Learning

Machine learning is the core of predictive analytics, while deep learning takes it further with neural networks.

• Supervised Learning: Master techniques like linear regression, decision trees, and random forests to predict outcomes from labeled data.
• Unsupervised Learning: Explore clustering and dimensionality reduction techniques, such as k-means and PCA, to analyze unlabeled datasets.
• Deep Learning: Learn frameworks like TensorFlow or PyTorch to design neural networks for advanced tasks like image recognition or natural language processing.

4. Understand Supervised Learning Techniques

Supervised learning involves training models on labeled datasets. Key algorithms include:

• Linear and Logistic Regression for prediction and classification tasks.
• Support Vector Machines (SVM) for high-dimensional data.
• Gradient Boosting Methods (e.g., XGBoost) for powerful ensemble models.

Supervised learning is foundational for applications like fraud detection, recommendation systems, and churn prediction.

5. Get Familiar with Unsupervised Learning Approaches

Unsupervised learning focuses on finding hidden patterns within unlabeled data.

• Clustering: Group similar data points using methods like k-means or hierarchical clustering.
• Dimensionality Reduction: Simplify complex datasets using techniques like PCA (Principal Component Analysis).

Retailers use clustering to segment customers and tailor marketing strategies.

6. Learn About Big Data and Cloud Computing

Handling large-scale datasets requires proficiency in big data tools and cloud platforms.

• Big Data Tools: Learn frameworks like Hadoop and Apache Spark to process massive datasets efficiently.
• Cloud Platforms: Familiarize yourself with AWS, Google Cloud, or Microsoft Azure to store and analyze data in scalable environments.

Also Read: Supervised vs Unsupervised Learning: Difference Between Supervised and Unsupervised Learning

Step 7: Learn Essential Data Science Tools

Mastering data science tools allows you to manage, analyze, visualize, and deploy data-driven solutions effectively. 

Here’s an overview of the must-have tools every aspiring data scientist should focus on.

1. Programming: Python, R, and SQL

Programming lies at the heart of data science. These three languages are indispensable:

• Python: Known for its simplicity and extensive libraries (like NumPy, Pandas, and Matplotlib), Python is the go-to language for data manipulation, analysis, and machine learning.
• R: Preferred for statistical modeling and advanced data visualization, R is especially useful in academic and research environments.
• SQL: Essential for querying, managing, and manipulating structured data in relational databases, SQL helps you retrieve insights from massive datasets quickly.

2. Visualization: Power BI vs Tableau

The ability to present insights visually is critical in data science. Here’ a quick comparison of these visualization tools:

3. Machine Learning: Keras, TensorFlow, Scikit-learn

These frameworks and libraries make implementing machine learning algorithms efficient and scalable:

• Keras: Simplifies building and training deep learning models, making it beginner-friendly for neural networks.
• TensorFlow: A more advanced tool for scalable machine learning and deep learning across CPUs, GPUs, and TPUs.
• Scikit-learn: Ideal for implementing basic machine learning algorithms like regression, classification, and clustering.

4. Cloud Platforms: AWS, Google Cloud, and Azure

Cloud platforms are essential for working with large-scale data and deploying models.

• AWS (Amazon Web Services): Offers services like S3 for data storage and SageMaker for building and deploying machine learning models.
• Google Cloud: Known for its BigQuery and AI Platform, it’s a favorite for analytics and machine learning in the cloud.
• Microsoft Azure: Provides a suite of AI and machine learning tools, including Azure ML Studio, for easy deployment.

5. Databases: MongoDB, MySQL, PostgreSQL

Understanding how to work with databases is key for managing and querying data.

• MongoDB: A NoSQL database that handles unstructured and semi-structured data, ideal for modern applications dealing with JSON-like formats.
• MySQL: It is a widely used relational database system for structured data in many businesses.
• PostgreSQL: Known for its flexibility and support for advanced data types, PostgreSQL is great for complex queries and analytical workloads.

Also Read: Top 30 Data Science Tools: Benefits and How to Choose the Right Tool for Your Needs in 2025

Step 8: Explore How Data Science Impacts Various Industries

Data science has transformed industries by enabling data-driven decisions, optimizing processes, and driving innovation. Here’s how data science is shaping key sectors:

1. Speech & Image Recognition: Revolutionizing AI Assistants and Social Media

These advancements have improved user experience, automated tasks and made everyday technologies more intuitive.

• AI Assistants: Virtual assistants like Siri, Alexa, and Google Assistant rely on speech recognition models powered by data science to process voice commands. 

NLP models like BERT (Bidirectional Encoder Representations from Transformers) or GPT (Generative Pre-trained Transformers) analyze the context and intent of the query.

• Social Media Tagging: Platforms like Facebook and Instagram use image recognition algorithms to suggest tags and identify objects or faces in photos.

2. Healthcare: Redefining Patient Care and Medical Advancements

Data science saves lives by enabling early diagnoses, personalizing treatments, and making healthcare more accessible.

• Medical Imaging Technology: Algorithms analyze CT scans, X-rays, and MRIs to detect conditions like cancer accurately.
• Drug Discovery: Data science accelerates the development of new medicines by analyzing molecular structures and predicting drug efficacy.
• Virtual Health Bots: Chatbots powered by natural language processing (NLP) provide 24/7 assistance to patients, answering health-related queries and scheduling appointments.

3. Transport: Pioneering Self-Driving Cars and Route Optimization

Data science improves safety, reduces fuel consumption, and enhances user convenience in transportation.

• Self-Driving Cars: Companies like Tesla and Waymo use data science to train autonomous vehicles to recognize road signs, avoid obstacles, and navigate safely.
• Route Optimization: Ride-hailing apps like Uber and logistics firms use predictive analytics to identify the fastest, most efficient routes based on traffic patterns and demand.

4. Government: Enhancing Public Services with Predictive Analytics

Using data, governments can make more informed decisions, improve transparency, and enhance service delivery.

• Predictive Analytics: Governments use data science to forecast trends like incarceration rates, tax evasion, and public health risks.
• Resource Allocation: Data-driven models help allocate budgets and resources efficiently, such as optimizing emergency response systems during natural disasters.

5. Finance: Securing Transactions and Reducing Risks

Data science ensures the financial sector operates securely and efficiently while improving customer experience.

• Fraud Detection: Machine learning algorithms analyze transaction patterns to detect and prevent real-time fraudulent activities.
• Risk Analysis: Banks use predictive models to assess the creditworthiness of loan applicants, minimizing default risks.
• Credit Scoring: Financial institutions leverage data to create accurate credit scores, which guide lending decisions.

Also Read: Fraud Detection in Machine Learning: What You Need To Know

Step 9: Understand Advanced Techniques in Data Science

As you progress on your data science roadmap, mastering advanced techniques is crucial for solving complex problems and predicting future trends. Let’s see ahead.

1. Time Series Analysis: Analyzing Data Points Over Time

Time series analysis focuses on datasets collected at regular intervals, such as stock prices, weather patterns, or website traffic.

Key Techniques:

• Autoregressive Integrated Moving Average (ARIMA): Used to model and forecast time-series data. Examples include predicting stock prices.
• Seasonal Decomposition of Time Series (STL): Helps identify trends, seasonality, and residuals.
• Exponential Smoothing: Captures short-term patterns in time-series data.

2. Predictive Modeling: Forecasting Trends and Future Events

Predictive modeling uses historical data to make informed predictions about future outcomes.

Key Applications:

• Sales Forecasting: Predicting future sales trends to optimize production and marketing strategies.
• Stock Price Prediction: Financial institutions use machine learning models to anticipate stock price movements.
• Weather Forecasting: Models analyze atmospheric data to predict temperature, rainfall, or storm patterns.

3. Advanced Machine Learning Algorithms

Exploring advanced machine learning techniques enhances your ability to tackle sophisticated problems:

• Reinforcement Learning: Focuses on training agents to make sequential decisions. Most common examples include Game AI and personalized recommendation systems.
• Ensemble Methods: Techniques like Gradient Boosting, Random Forest, and AdaBoost improve model accuracy by combining predictions from multiple models.

Also Read: Top 10 Latest Data Science Techniques You Should be Using

Step 10: Stay Updated with Emerging Data Science Trends

The field of data science evolves rapidly, with new technologies and practices constantly reshaping the industry. Let’s explore the key trends shaping data science today.

1. Cloud Migration and Cloud-Native Solutions

The shift to cloud platforms is transforming how organizations manage and analyze data.

• Cloud Migration: Companies are moving from on-premises systems to cloud environments like AWS, Google Cloud, and Microsoft Azure, enabling scalability and flexibility in data storage and processing.

Companies like Infosys and TCS use AWS for scalable infrastructure and analytics solutions.

• Cloud-Native Solutions: Tools like Kubernetes and Docker allow for seamless deployment and management of data science models on the cloud.

2. Widespread Use of Predictive Analytics and AI

Predictive analytics and AI are at the forefront of innovation across industries.

• Predictive Analytics: Used extensively in marketing, healthcare, and finance to forecast trends, detect anomalies, and optimize operations.
• AI Adoption: From chatbots to autonomous vehicles, AI applications are becoming more pervasive, with data science playing a central role in their development.

3. Enhanced Consumer Interfaces and Improved Data Regulation

• Improved Consumer Interfaces: With advancements in NLP and computer vision, user interfaces are becoming more intuitive. Virtual assistants, real-time translation, and AI-powered customer service are examples of how data science improves user experiences.
• Data Regulation: Governments and organizations emphasize stricter compliance with data privacy laws, such as GDPR compliance and India’s Data Protection Bill, ensuring ethical handling of consumer data.

Beyond knowing what’s trending, you need a proactive approach to stay informed. Here are specific resources to help you stay ahead:

• Participate in Kaggle competitions, which offers competitions, datasets, and forums where you can solve real-world problems.
• Enroll in online courses and certifications by upGrad, Coursera’s “AI for Everyone,” and more, which global experts lead.
• Stay updated with the latest breakthroughs by accessing IEEE research papers on technical advancements in AI, Ml, and data privacy

Also Read: What is the Future of Data Science Technology in India?

Now that you are familiar with all these processes let’s see what skills you need to excel in your data science career!

Essential Skills for a Career in Data Science

Data science is a multidisciplinary field that requires a blend of technical expertise, business acumen, and interpersonal skills. Professionals entering this field in 2025 must cultivate a well-rounded skill set beyond coding and statistics. 

Let’s explore the essential technical and soft skills for a data science career.

1. Technical Skills: The Foundation of Data Science

While tools and programming languages evolve, the core abilities of a successful data scientist remain timeless. Here’s what’s essential in 2025:

• Adaptability to Emerging Technologies: With advancements in AI, quantum computing, and edge analytics, staying adaptable to new tools and frameworks is necessary.
• Data Governance and Security Knowledge: As stricter regulations emerge globally, understanding data privacy laws and ethical AI practices is crucial for building compliant solutions.
• API Integration: Modern data solutions often require integrating with APIs, allowing businesses to harness third-party services and external datasets effectively.
• Hybrid Problem-Solving: Beyond algorithms, data scientists must creatively combine business logic, domain expertise, and technical insights to deliver practical outcomes.

Looking to master innovative problem-solving techniques? Here’s upGrad’s complete guide to problem-solving skills to learn to tackle challenges and drive impactful results!

2. Soft Skills for Success in 2025

The technical aspects of data science are only part of the equation. Soft skills differentiate a competent data scientist from a truly exceptional one. 

In 2025, employers will prioritize candidates with the following abilities:

• Strategic Thinking:
  You must move beyond data manipulation and adopt a strategic mindset. This means aligning insights with business goals, predicting trends, and proactively identifying growth opportunities. 
• Curiosity and Lifelong Learning:
  With the rapid evolution of tools and trends, curiosity is a non-negotiable trait. You must constantly explore new methods, seek innovative solutions, and upskill through certifications and industry research.
• Storytelling with Data:
  Transforming raw data into actionable narratives is one of the most sought-after skills. A compelling story backed by data can convince stakeholders, secure project funding, and drive informed decision-making.
• Collaboration Across Teams:
  Data scientists rarely work in isolation. They collaborate with engineers, product managers, marketing teams, and executives. Strong interpersonal skills ensure that insights are understood and implemented effectively.
• Resilience Under Pressure:
  Deadlines, unexpected data issues, and evolving priorities can make data science a high-pressure field. Resilience and problem-solving under stress are critical to maintaining productivity.

3. The Rising Importance of Emotional Intelligence (EQ)

In 2025, companies will increasingly value emotional intelligence in data science roles. EQ encompasses empathy, self-awareness, and social skills, which are vital for:

• Understanding the needs of non-technical stakeholders.
• Building trust and collaboration within cross-functional teams.
• Communicating insights with tact and sensitivity, especially when presenting complex findings.

By cultivating all these skills, you’ll position yourself as a well-rounded data scientist capable of thriving in an ever-changing, competitive landscape.

Wish to enhance your soft skills? Explore upGrad’s free soft skills courses, boost your confidence, enhance employability, and get certified!

Now, let’s dive into the roles and salaries you can expect in your data science career!

Top Data Science Career Opportunities in India

India has become a global hub for data science, with industries increasingly adopting data-driven strategies to gain a competitive edge. This has made data science one of the most sought-after and profitable career paths in 2025.

Additionally, companies are actively hiring across sectors such as IT, finance, healthcare, and e-commerce, making this field highly paying.

Below is a table showcasing key roles and their average annual salaries:

Source: Glassdoor

With over 93,000 unfilled data science roles in India, the field is rife with opportunities for students and professionals to advance their data science careers. 

As more companies embrace AI and analytics, the demand for skilled data scientists will continue to rise, making this an ideal time to enter the profession!

Also Read: Importance of Data Science in 2025 [A Simple Guide]

How can upGrad guide your data science career journey?

Wish to turn your data science dreams into reality but feeling unsure where to start? upGrad, India’s leading online learning platform, is here to walk with you every step of the way. 

Whether you’re a student exploring your career options or a working professional eager to upskill, upGrad equips you with personalized courses, mentorship, and opportunities in data science.

Some of the top programs include:

• Professional Certificate Program in AI and Data Science
• Data Science in E-commerce
• Analyzing Patterns in Data and Storytelling
• Programming with Python

Still wondering if a data science career is right for you? Why not talk to an expert who can guide you? Schedule a career counseling session with upGrad today!

Explore More: Dive Into Our Power-Packed Self-Help Blogs on Data Science Courses!

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References: 
https://scoop.market.us/data-science-statistics/ 
https://www.appliedaicourse.com/blog/what-is-the-scope-of-data-science-in-india/ 
https://www.glassdoor.co.in/Salaries/data-scientist-salary-SRCH_KO0,14.htm 
https://www.glassdoor.co.in/Salaries/data-analyst-salary-SRCH_KO0,12.htm 
https://www.glassdoor.co.in/Salaries/machine-learning-engineer-salary-SRCH_KO0,25.htm 
https://www.glassdoor.co.in/Salaries/business-intelligence-analyst-salary-SRCH_KO0,29.htm 
https://www.glassdoor.co.in/Salaries/big-data-engineer-salary-SRCH_KO0,17.htm 
https://www.glassdoor.co.in/Salaries/deep-learning-engineer-salary-SRCH_KO0,22.htm