View In Full Page

What does Data Scientist do?


Data scientists play a pivotal role in transforming raw data into valuable insights that drive informed decision-making. Their work encompasses a diverse range of tasks, starting with data collection and cleaning, ensuring the integrity and quality of the datasets they work with. Utilizing statistical methods, machine learning algorithms, and programming languages such as Python or R, data scientists analyze patterns and trends within the data to extract actionable knowledge. They are often involved in creating predictive models that help businesses anticipate future trends, customer behaviors, and potential challenges.

In addition to their technical expertise, data scientists act as communicators and interpreters between the technical and non-technical realms of an organization. They translate complex analytical results into comprehensible insights, enabling stakeholders from various departments to make informed decisions. Collaboration is key as data scientists work closely with cross-functional teams, including business analysts, engineers, and executives, ensuring that data-driven insights align with organizational goals and contribute to strategic initiatives. As the demand for data-driven decision-making continues to grow, the role of data scientists remains integral in leveraging the power of data to enhance organizational effectiveness.

Furthermore, data scientists are at the forefront of addressing ethical considerations in data usage. They navigate issues related to privacy, fairness, and bias, ensuring that their analyses adhere to ethical standards and legal regulations. With a commitment to ongoing learning and staying abreast of technological advancements, data scientists continuously evolve their skills to tackle the dynamic challenges presented by the ever-expanding landscape of data and analytics.

Data Scientist Salary in India



₹9,53,360 / year

Avg. Base Salary

₹9.53L

₹3.57L
₹20L

The average salary for a Data Scientist is ₹9,53,360 in 2023

Pay by Experience Level

Years Avg Sal
0-1 ₹ 5.91L
1-5 ₹ 8.85L
5-10 ₹ 10L
10+ ₹ 20L

How to become a Data Scientist


Step 1:Educational Foundation

Start with a solid educational background in a relevant field such as computer science, statistics, mathematics, or a related discipline. Many data scientists hold advanced degrees (master's or Ph.D.), but it's also possible to enter the field with a bachelor's degree, particularly if you gain relevant skills and experience.

Step 2:Programming Proficiency

Develop proficiency in programming languages commonly used in data science, such as Python or R. These languages are essential for data manipulation, analysis, and the implementation of machine learning models. Familiarize yourself with libraries like NumPy, pandas, scikit-learn (for Python), or dplyr, ggplot2 (for R).

Step 3:Statistical and Mathematical Competence

Build a solid foundation in statistics and mathematics, as they form the basis for many data science concepts. Understanding concepts like probability, hypothesis testing, regression, and linear algebra is crucial for developing and evaluating machine learning models. Online courses, textbooks, and practical exercises can aid in mastering these concepts.

Step 4:Machine Learning Knowledge

Gain a comprehensive understanding of machine learning algorithms and techniques. Explore supervised and unsupervised learning, classification, regression, clustering, and ensemble methods. Hands-on experience with real-world datasets and model implementation is invaluable for honing practical skills.

Step 5:Data Wrangling and Exploration

Learn data wrangling and exploration techniques to effectively prepare and clean datasets for analysis. Tools like Pandas and NumPy in Python or dplyr and ggplot2 in R are essential for manipulating and visualizing data. Understanding how to handle missing values, outliers, and feature engineering contributes to the overall quality of your analyses.

Step 6:Continuous Learning and Application

Stay updated with the latest developments in data science and machine learning. Engage in continuous learning through online courses, workshops, and attending conferences. Apply your knowledge by working on real-world projects, either through internships, personal projects, or participating in online platforms that offer datasets for practice. Building a strong portfolio showcasing your practical skills and projects is essential for standing out in the competitive field of data science.

Step 1: Learn the Basics

  1. Java or Kotlin Programming Language:

    • Java: Traditionally, Android development was done using Java. It's a versatile, object-oriented programming language.
    • Kotlin: Kotlin is now the preferred language for Android development. It's interoperable with Java, concise, and considered more modern.

  2. Understanding XML:

    • XML (eXtensible Markup Language) is used for designing layouts in Android. It defines the structure and appearance of the user interface (UI) components.

Step 2: Master Android Development Tools

  1. Android Studio:

    • Android Studio is the official IDE for Android development. It provides a rich environment with features like a visual layout editor, code analysis, debugging tools, and support for Kotlin. Regularly updating to the latest version is crucial for accessing the latest features and improvements.

  2. Emulator:

    • The Android Emulator allows you to run and test your applications on a virtual device. It's an essential tool for debugging and testing your apps on different Android versions and screen sizes.

Plan to Master as a Data Scientist


Day Focus Area Tasks
1Introduction to Data Science ConceptsResearch data scientist role, set goals
2-4Programming BasicsRefresh programming skills (choose Python or R)
5-7Data Manipulation with PandasLearn Pandas for data manipulation
8-9Data VisualizationLearn Matplotlib or Seaborn for data visualization
10Statistical FundamentalsReview basic statistics and probability concepts
11-12Machine Learning BasicsIntroduction to scikit-learn for machine learning
13-15Advanced Machine LearningLearn advanced ML: ensemble methods, model evaluation
16Basics of Deep LearningIntroduction to deep learning, choose TensorFlow or PyTorch
17-18Big Data Tools and TechnologiesBasics of Apache Spark for big data processing
19Application of Knowledge in a ProjectWork on a practical data science project
20Future Learning and Skill ImprovementReview, reflect, identify areas for further learning
Day Focus Area Tasks
1Introduction to Data Science ConceptsResearch data scientist role, set long-term goals
2-4Programming BasicsRefresh programming skills (choose Python or R)
5-7Data Manipulation with PandasLearn Pandas for data manipulation
8-10Data VisualizationLearn Matplotlib or Seaborn for data visualization
11Statistical FundamentalsReview basic statistics and probability concepts
12-14Machine Learning BasicsIntroduction to scikit-learn for machine learning
15-17Advanced Machine LearningLearn advanced ML: ensemble methods, model evaluation
18-20Basics of Deep LearningIntroduction to deep learning, choose TensorFlow or PyTorch
21-23Big Data Tools and TechnologiesBasics of Apache Spark for big data processing
24Cloud Computing for Data ScienceIntroduction to cloud platforms (e.g., AWS, Azure)
25-27SQL and Database ManagementLearn SQL for data retrieval and management
28-30Feature Engineering and Dimensionality ReductionTechniques for improving model performance
31-33Model Deployment and ProductionDeploy models using platforms like Flask or FastAPI
34-36Time Series AnalysisUnderstand and analyze time-dependent data
37-39Natural Language Processing (NLP)Introduction to processing and analyzing text data
40-42Reinforcement LearningBasics of reinforcement learning
43-45Model Interpretability and ExplainabilityTechniques for understanding and explaining models
46A/B TestingIntroduction to experimental design and analysis
47-49Data Ethics and Bias in AIUnderstand ethical considerations in data science
50-52Real-world Data Science ProjectsWork on larger, more complex projects
53-55Review and OptimizationReview concepts, optimize code, and workflows
56-58Job Search and Interview PreparationPolish resume, create a portfolio, practice interviews
59-60Continuous Learning and Future GoalsPlan for ongoing learning and set future goals
Day Focus Area Tasks
1Introduction to Data Science ConceptsResearch data scientist role, set long-term goals
2-4Programming BasicsRefresh programming skills (choose Python or R)
5-7Data Manipulation with PandasLearn Pandas for data manipulation
8-10Data VisualizationLearn Matplotlib or Seaborn for data visualization
11Statistical FundamentalsReview basic statistics and probability concepts
12-14Machine Learning BasicsIntroduction to scikit-learn for machine learning
15-17Advanced Machine LearningLearn advanced ML: ensemble methods, model evaluation
18-20Basics of Deep LearningIntroduction to deep learning, choose TensorFlow or PyTorch
21-23Big Data Tools and TechnologiesBasics of Apache Spark for big data processing
24Cloud Computing for Data ScienceIntroduction to cloud platforms (e.g., AWS, Azure)
25-27SQL and Database ManagementLearn SQL for data retrieval and management
28-30Feature Engineering and Dimensionality ReductionTechniques for improving model performance
31-33Model Deployment and ProductionDeploy models using platforms like Flask or FastAPI
34-36Time Series AnalysisUnderstand and analyze time-dependent data
37-39Natural Language Processing (NLP)Introduction to processing and analyzing text data
40-42Reinforcement LearningBasics of reinforcement learning
43-45Model Interpretability and ExplainabilityTechniques for understanding and explaining models
46A/B TestingIntroduction to experimental design and analysis
47-49Data Ethics and Bias in AIUnderstand ethical considerations in data science
50-52Real-world Data Science ProjectsWork on larger, more complex projects
53-55Review and OptimizationReview concepts, optimize code, and workflows
56-58Job Search and Interview PreparationPolish resume, create a portfolio, practice interviews
59-61Continuous Learning and SpecializationDeepen skills in a specific area (e.g., computer vision, NLP)
62-64Advanced Topics and Research PapersExplore research papers in specialized areas
65-67Advanced Statistical ModelingDive deeper into statistical models and methods
68-70Advanced Machine Learning TechniquesExplore advanced ML algorithms and methods
71-73Advanced Deep LearningDive deeper into neural networks and architectures
74-76Advanced Big Data Tools and TechnologiesExplore advanced big data tools and frameworks
77-79Specialized Topics (e.g., Bayesian Statistics)Explore specific areas of interest in-depth
80-82Capstone ProjectWork on a comprehensive data science project
83-85Portfolio Development and RefinementEnhance your data science portfolio
86-88Networking and ConferencesAttend conferences, network with professionals
89-90Reflection, Goal Setting, and Future PlanningReflect on achievements, set new goals for the future

Popular Roles as a Data Scientist

View In Full Page