Data Science Course in South Extension

This module introduces the foundational principles, scope, and importance of data science.
Learners understand how data is collected, processed, analyzed, and used for decision-making.
The module builds an analytical mindset and structured problem-solving approach.
It also explains different career roles and applications of data science across industries.

This module focuses on Python programming essentials required for data analysis tasks.
Learners work with data types, loops, functions, conditional logic, and file handling.
Key Python libraries for data manipulation and analysis are covered with examples.
Hands-on coding sessions strengthen programming confidence and logical thinking.

This module covers statistical and probabilistic concepts essential for data interpretation.
Topics include probability theory, data distributions, hypothesis testing, and correlation.
Learners understand how statistics supports analytical and predictive modeling.
Practical use cases help connect theory with real-world applications.

This module focuses on exploring and visualizing data to identify patterns and trends.
Learners create charts, graphs, and dashboards to communicate insights effectively.
Exploratory techniques help detect anomalies, relationships, and data behavior.
The module enhances data storytelling and presentation skills.

This module introduces core machine learning concepts and workflows.
Learners explore supervised and unsupervised learning algorithms.
Topics include regression, classification, clustering, and model evaluation.
Practical implementation helps learners understand real-world machine learning use cases

This module trains learners to work with structured data using SQL.
You will learn to write queries, joins, subqueries, and aggregation functions.
Understanding database systems improves data extraction and reporting efficiency.
The module prepares learners for handling business and enterprise databases.

This module introduces big data concepts and modern data processing techniques.
Learners understand how large datasets are stored, processed, and managed efficiently.
Exposure to scalable data systems prepares learners for enterprise data environments.
This module enhances readiness for advanced data roles.

This module explains how data science models are deployed into production systems.
Learners understand model validation, tuning, monitoring, and performance tracking.
The importance of maintaining model accuracy over time is emphasized.
This module bridges the gap between development and real-world implementation.

This module focuses on applying data insights to solve business problems.
Learners analyze KPIs, business metrics, and real-world scenarios.
The module improves communication between technical teams and stakeholders.
It strengthens strategic thinking and decision-making skills.

This module emphasizes practical learning through real-world projects.
Learners work on industry-aligned capstone projects from start to finish.
Projects help build a strong professional portfolio.
Case studies enhance analytical reasoning and problem-solving abilities.