Data Science
The Data Science course is designed to help learners build expertise in data analysis, machine learning, statistics, and visualization techniques. Through practical projects and industry-relevant tools, this course provides a comprehensive understanding of how data-driven insights are used to solve real-world business problems and support informed decision-making across industries.
The Job Guarantee course for you!
Apply statistical techniques to solve business problems
Develop end-to-end data science projects using real-world datasets.
Communicate data-driven insights effectively to stakeholders.
What Will You Learn?
Learn industry-relevant tools and methodologies
Develop strong problem-solving and decision-making abilities
Enhance earning potential with specialized Data Science expertise.
Prepare for diverse job roles in data and analytics
Course Curriculum
- What will you learn in this course? 0:03 Min
- Introduction 0:15 Min
- Data Science and Business Buzzwords 0:05 Min
- Difference between Analysis and Analytics 0:03 Min
- Understanding Business Analytics, Data Analytics, and Data Science 0:06 Min
- Continuing with BI, ML, and AI in Data Science 0:03 Min
- A Breakdown of our Data Science Infographic 0:02 Min
- The Various Data Science Diciplines 0:15 Min
- Traditional Data, Big Data, BI, Traditional Data Science and ML 0:11 Min
- Connecting the data science disciplines 0:15 Min
- Disciplines within Data Science 0:04 Min
- The Benefits of each Discipline 0:15 Min
- Techniques for Working with Traditional Data 0:05 Min
- Real-life Examples of Traditional data 0:02 Min
- Techniques for Working with Big Data 0:03 Min
- Types of Machine Learning 0:04 Min
- Popular data science Techniques 0:15 Min
- Why to choose data science as a Career! 0:04 Min
- Careers in Data Science 0:15 Min
- The Basic Probability Formula 0:02 Min
- Computing Expected Values 0:03 Min
- Probability 0:15 Min
- Fundamentals of combinatorics 0:02 Min
- Permutations 0:03 Min
- Probability-Combinatorics 0:15 Min
- Bayesian Inference: Ways Sets Can Interact in Data Science 0:02 Min
- Bayesian Inference 0:15 Min
- Fundamentals of Probability Distributions 0:04 Min
- Types of Probability Distributions 0:05 Min
- Characteristics of Discrete Distributions 0:02 Min
- Characteristics of Continuous Distributions 0:03 Min
- Probability Distribution 0:15 Min
- Probability in Statistics 0:04 Min
- Probability in Data Science 0:03 Min
- Probability in Finance 0:02 Min
- Probability in other fields 0:15 Min
- Population and Sample 0:03 Min
- Statistics 0:15 Min
- Types of data 0:04 Min
- Descriptive statistics 0:15 Min
- Introduction to Programming 0:02 Min
- Why Python? 0:02 Min
- Why Jupyter? 0:02 Min
- Installing Python and Jupyter 0:03 Min
- Introduction to Python 0:15 Min
- Variables 0:02 Min
- Numbers and Boolean Values in Python 0:03 Min
- Python String 0:03 Min
- Python - Variables and Datatype 0:15 Min
- Using Arithmetic Operators in Python 0:02 Min
- The Double Equality Sign 0:03 Min
- How to Reassign Values 0:01 Min
- Structuring with Indentation 0:01 Min
- Python - Basic Python Syntax 0:15 Min
- Comparison Operators 0:02 Min
- Logical and Identity Operators 0:02 Min
- Python - Other Python Operators 0:15 Min
- The IF Statement 0:01 Min
- The ELSE Statement 0:02 Min
- The ELIF Statement 0:02 Min
- A Note on Boolean Values 0:02 Min
- Python - Conditional Statements 0:15 Min
- Defining a Function in Python 0:01 Min
- How to Create a Function with a Parameter 0:01 Min
- How to Use a Function within a Function 0:01 Min
- Built-in Functions in Python 0:07 Min
- Python - Python Functions 0:15 Min
- Lists 0:02 Min
- Using Methods 0:01 Min
- Tuples 0:02 Min
- Dictionaries 0:01 Min
- Python - Sequences 0:15 Min
- For Loops 0:01 Min
- While Loops and Incrementing 0:02 Min
- Lists with the range() Function 0:03 Min
- How to Iterate over Dictionaries 0:03 Min
- Python - Iterations 0:15 Min
- Object-Oriented Programming 0:05 Min
- Packages and Modules 0:03 Min
- Understanding Python's Standard Library 0:02 Min
- Python-Advanced Python Tools 0:15 Min
- Introduction to Regression Analysis 0:04 Min
- Advanced Statistical Methods 0:14 Min
- Python Packages Installation 0:02 Min
- Advanced Statistical Methods - Linear Regression With StatsModels 0:15 Min
- Multiple Linear Regression 0:02 Min
- Adjusted R-Squared 0:03 Min
- Advanced Statistical Methods - Multiple Linear Regression With StatsModels 0:15 Min
- What is sklearn and How is it Different from Other Packages 0:03 Min
- Simple Linear Regression with sklearn 0:01 Min
- Simple Linear Regression with sklearn - A Statsmodel - like summary table 0:03 Min
- Feature Selection (F-regression) 0:02 Min
- Advanced Statistical Methods - Linear Regression with sklearn 0:15 Min
- Introduction to Logistic Regression 0:02 Min
- Advanced Statistical Methods : Logistic Regression 0:15 Min
- What is a Matrix? 0:01 Min
- Scalars and Vectors 0:01 Min
- What is a Tensor? 0:02 Min
- Addition and Subtraction of Matrices 0:03 Min
- Mathematics 0:15 Min
- What is Deep Learning? 0:02 Min
- Deep Learning Frameworks 0:02 Min
- Introduction to Neural Networks 0:01 Min
- Deep Learning 0:15 Min
- Understanding Key Concepts in Software Integration 0:05 Min
- Data Connectivity, APIs, and Endpoints 0:05 Min
- Taking a Closer Look at APIs 0:08 Min
- Software Integration 0:05 Min
- Software Integration 0:15 Min
- Using the .format() Method 0:02 Min
- Iterating Over Range Objects 0:03 Min
- Introduction to Nested For Loops 0:03 Min
- Anonymous (Lambda) Functions 0:04 Min
- Appendix - Additional Python Tools 0:15 Min
- Introduction to pandas Series 0:02 Min
- Working with Methods in Python 0:02 Min
- Using .unique() and .nunique() 0:01 Min
- Using .sort_values() 0:02 Min
- Appendix : Pandas Fundamentals 0:15 Min
Learn With GreyLearn
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Hands-on projects and real-world case studies.
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Training in Python, statistics, machine learning, and data visualization.
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Practical exercises using industry-standard tools and technologies.
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Career-focused learning designed for beginners and professionals.
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Project-based approach to strengthen practical understanding
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Access to updated learning materials and industry-relevant content.
What Learners Say
dhruv kapoor
The planner made weekly progress steady and motivating i document assumptions limits and next tests in plain words feature work became structured and less guess
Madhura S
I can brief leaders with facts limits and next actions i explain tradeoffs and choose a model for the real need i practice honest comparison
Biswabismay Pradhan
I practice honest comparison with clear acceptance rules i treat data ethics and consent as part of the work the planner made weekly progress steady
Anjali Jadhav
I learned to handle class imbalance with measured steps the method fits dashboards products and research tasks the method made statistics feel approachable and helpful
Krushna Jathar
The pipeline keeps steps reproducible and easy to review the process kept work ethical transparent and effective i treat data ethics and consent as part
Anamika Singh
I learned to explain findings in language teams understand i now connect insight to decisions the team can execute i left with a template i
Frequently Asked Questions
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