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Data Science and Analytics

Build strong data analysis and machine learning capability through a structured program with practical case studies and project-based learning.

Duration: 9 months
Mode: online/offline
Language: Hindi/English
View Curriculum

Curriculum

Module 1: Python for Data Work

4 weeks

Topics Covered:

  • Python essentials
  • NumPy and Pandas
  • Data cleaning workflows
  • Notebook best practices

Projects:

  • Sales data cleanup project

Module 2: SQL for Analysis

4 weeks

Topics Covered:

  • Query fundamentals
  • Joins and aggregations
  • Window functions
  • Performance basics

Projects:

  • Retail KPI reporting

Module 3: Statistics for Analytics

4 weeks

Topics Covered:

  • Descriptive statistics
  • Probability and distributions
  • Hypothesis testing
  • A/B testing foundations

Projects:

  • Experiment analysis report

Module 4: Data Visualization

3 weeks

Topics Covered:

  • Matplotlib and Seaborn
  • Chart selection and storytelling
  • Dashboard structure
  • Presentation clarity

Projects:

  • Executive analytics dashboard

Module 5: Exploratory Data Analysis

3 weeks

Topics Covered:

  • Pattern discovery
  • Outlier handling
  • Feature understanding
  • Insight documentation

Projects:

  • Customer churn EDA

Module 6: Machine Learning Foundations

4 weeks

Topics Covered:

  • Regression
  • Classification
  • Model metrics
  • Cross-validation

Projects:

  • Lead scoring model

Module 7: Applied Analytics Case Studies

3 weeks

Topics Covered:

  • Marketing analytics
  • Product analytics
  • Operations analytics
  • Business recommendation writing

Projects:

  • Multi-domain analytics report

Module 8: Data Workflow and Collaboration

2 weeks

Topics Covered:

  • Version control for analysts
  • Reproducible workflows
  • Data documentation
  • Stakeholder communication

Projects:

  • Collaborative analytics notebook

Module 9: Capstone

3 weeks

Topics Covered:

  • Problem framing
  • Data pipeline design
  • Model and dashboard integration
  • Final presentation

Projects:

  • End-to-end data science capstone

Learning Objectives

  • ✓Collect, clean, and analyze structured data
  • ✓Create meaningful dashboards and business reports
  • ✓Apply statistical methods for decision support
  • ✓Build and evaluate baseline machine learning models
  • ✓Present technical findings to non-technical stakeholders

Program Overview

This program combines analytics rigor with practical machine learning so learners can solve business-facing problems using data. The focus is not just on model building, but on the full cycle: question framing, data preparation, analysis, visualization, and recommendation.

What You Will Build?

  • KPI dashboards for reporting and decision making
  • Exploratory analysis notebooks on real datasets
  • Baseline predictive models with clear evaluation
  • End-to-end capstone that combines analytics and ML

Frequently Asked Questions

Do I need strong mathematics to start?

No. Required statistics and analytical math are taught step by step with practical context.

Is this more analytics-focused or ML-focused?

It is balanced. You gain analytics depth first, then apply machine learning in practical use cases.

Will this help me build a portfolio?

Yes. The curriculum includes multiple case-study projects and a final capstone suitable for portfolio showcasing.

Tags

Data ScienceData AnalyticsPythonSQLMachine Learning

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