Data Science Course in Nehru Place

This module introduces the basic concepts, scope, and real-world applications of data science across industries.
Learners understand how raw data is collected, cleaned, processed, and transformed into meaningful insights.
The module strengthens analytical thinking and structured problem-solving skills.
It also provides clarity on various data science career paths and job roles.

This module focuses on Python programming essential for data science and analytics tasks.
Learners work with data structures, conditional logic, loops, functions, and file handling.
Popular Python libraries used for data analysis are covered with practical examples.
Hands-on coding exercises help build confidence and problem-solving efficiency.

This module covers core statistical concepts required for data-driven decision-making.
Topics include probability, distributions, hypothesis testing, correlation, and regression basics.
Learners understand how statistics supports data interpretation and predictive modeling.
Real-world examples help bridge the gap between theory and application.

This module focuses on exploring datasets to identify trends, patterns, and anomalies.
Learners create visualizations such as charts, graphs, and dashboards to communicate insights.
Exploratory data analysis techniques improve data understanding and accuracy.
The module enhances data storytelling and presentation skills.

This module introduces machine learning workflows and practical implementations.
Learners explore supervised and unsupervised learning techniques.
Topics include regression, classification, clustering, and model evaluation methods.
Hands-on projects demonstrate how machine learning is applied in real business scenarios.

This module trains learners to manage and analyze structured data using SQL.
Learners write queries involving joins, subqueries, aggregations, and filtering.
Understanding relational databases improves data extraction and reporting efficiency.
The module prepares learners for real-world business data environments.

This module introduces learners to big data concepts and scalable data processing.
Topics include handling large datasets and understanding distributed systems.
Learners gain exposure to modern data processing frameworks and tools.
This module helps prepare learners for enterprise-level data challenges.

This module explains how machine learning models are deployed into production systems.
Learners understand model validation, versioning, and performance tracking.
The importance of maintaining accuracy and reliability over time is emphasized.
This module connects data science development with real-world business usage.

This module focuses on applying data insights to solve business problems.
Learners understand how data supports strategic planning and operational decisions.
Real-life business scenarios and KPIs are analyzed using data-driven methods.

This module emphasizes practical learning through real-world problem-solving.
Learners work on industry-aligned capstone projects from end to end.
Projects help build a strong professional portfolio for job applications.
Case studies strengthen analytical reasoning and decision-making abilities.