Python for Data Science
Learn Python for data analysis and machine learning
Starting from
- Key Highlights
- Data analysis
- Hands-on projects
- Interactive coding challenges
- Community support
- Certificate of completion
Syllabus
- Environment Setup, Variables & Data Types, Operators, Conditional Statements, Loops, Functions, Data Structures, File Handling, Exception Handling
This section builds core programming skills required for data science. Students learn structured coding practices, problem-solving techniques, and how to handle data efficiently using Python fundamentals.
- Array Processing, DataFrame Operations, Data Cleaning, Handling Missing Values, Data Transformation, Feature Engineering Basics, Exploratory Analysis Techniques
Learners develop the ability to transform raw data into structured insights. The focus is on cleaning, organizing, analyzing, and interpreting datasets to uncover patterns and trends.
- Descriptive Statistics, Probability Theory, Random Variables, Sampling Techniques, Hypothesis Testing, Confidence Intervals, Correlation Analysis
This module establishes the mathematical backbone for analytical reasoning. Students understand uncertainty, statistical inference, and how to validate data-driven conclusions.
- Visualization Principles, Distribution Plots, Comparative Charts, Time-Series Analysis, Dashboard Fundamentals, KPI Reporting, Insight Communication
Students learn to convert analytical results into impactful visual narratives that support business decision-making and stakeholder communication.
- Regression Models, Classification Algorithms, Model Training Workflow, Feature Selection, Evaluation Metrics, Cross-Validation, Overfitting & Regularization
This section introduces predictive modeling techniques used to solve real-world problems. Learners build and evaluate models using structured machine learning workflows.
- Clustering Techniques, Dimensionality Reduction, Feature Scaling, Hyperparameter Tuning, Pipeline Development, Model Performance Improvement
Students explore pattern discovery techniques and optimization strategies that enhance model accuracy and robustness for complex datasets.
- Database Fundamentals, Query Writing, Filtering & Aggregation, JOIN Operations, Subqueries, Window Functions, Analytical SQL Use Cases
This module equips learners with database querying skills necessary to extract, manipulate, and analyze structured business data efficiently.
- Application Integration, Model Deployment, Version Control, Portfolio Development, Resume Building, Interview Preparation, Capstone Project
The final section prepares students to deploy machine learning solutions, showcase projects professionally, and transition confidently into industry roles.