★★★★★
"The structure, the practice exams, the instructor — all top tier. Passed first try."
AS
Aarti SharmaSenior Project Manager · TCS
— The journey
Course Outline
What the programme covers, module by module.
Module 1: Introduction to Data Science
- Data science fundamentals
- Data science lifecycle
- Types of data
- Structured and unstructured data
- Data-driven decision-making
- Industry applications
Module 2: Data Science Tools & Environment
- Python ecosystem
- Jupyter Notebook
- Development environments
- Data science libraries
- Notebook workflows
- Environment setup
Module 3: Python Fundamentals
- Variables and data types
- Operators
- Conditional statements
- Loops
- Functions
- Python syntax
Module 4: Python Data Structures
- Lists
- Tuples
- Dictionaries
- Sets
- Indexing and slicing
- Data manipulation
Module 5: NumPy Fundamentals
- NumPy arrays
- Array operations
- Indexing
- Mathematical operations
- Aggregations
- Numerical computing
Module 6: Data Analysis with Pandas
- Series and DataFrames
- Importing datasets
- Selecting data
- Filtering data
- Sorting data
- Data aggregation
Module 7: Data Cleaning & Preparation
- Missing values
- Duplicate records
- Data types
- Outlier handling
- Data transformation
- Data validation
Module 8: Exploratory Data Analysis
- Data exploration
- Descriptive analysis
- Distribution analysis
- Pattern identification
- Correlation analysis
- Insight generation
Module 9: Statistics for Data Science
- Descriptive statistics
- Mean, median, and mode
- Variance
- Standard deviation
- Probability fundamentals
- Statistical distributions
Module 10: Data Visualization
- Visualization principles
- Matplotlib fundamentals
- Charts and plots
- Distribution visualization
- Relationship visualization
- Data storytelling
Module 11: SQL Fundamentals for Data Science
- Relational databases
- SELECT statements
- Filtering
- Sorting
- Aggregations
- Basic joins
Module 12: Introduction to Machine Learning
- Machine learning concepts
- Supervised learning
- Unsupervised learning
- Features and targets
- Training data
- Model workflow
Module 13: Regression & Classification Fundamentals
- Regression concepts
- Classification concepts
- Linear regression
- Logistic regression
- Prediction
- Practical applications
Module 14: Model Evaluation Fundamentals
- Training and testing data
- Model performance
- Accuracy
- Precision and recall
- Error metrics
- Overfitting concepts
Module 15: Practical Data Science Project
- Dataset selection
- Data preparation
- Exploratory analysis
- Data visualization
- Basic model development
- Insight presentation
— 01.2 · Is it right for you?
Who it's for & what's included
Pick a delivery method to see exactly who it suits and everything you receive.
Who it's for
Classroom
Best for learners who want face-to-face tuition and to network with peers in person.
What's included
Everything you get
- ✓ Live instructor on-site
- ✓ Printed workbook & materials
- ✓ Group exercises & case studies
Who it's for
Online Instructor-Led
Best for learners who want a live instructor and a fixed schedule, without the travel.
What's included
Everything you get
- ✓ Live instructor via video call
- ✓ Digital workbook & resources
- ✓ Session recordings
Who it's for
Self-Paced
Best for self-motivated learners who need maximum flexibility around work and life.
What's included
Everything you get
- ✓ On-demand video lessons
- ✓ Interactive quizzes
- ✓ 24/7 access on any device