Data Science with Python: Learn Data Analytics Through Practical Projects

Building a Strong Foundation in Data Science

Data Science combines programming, statistics, data analysis, and machine learning to transform raw data into meaningful insights. Organizations across industries rely on data professionals to analyze information, identify trends, and support informed decision-making.

This Data Science with Python course introduces learners to the complete data science workflow, starting with Python programming and progressing through SQL, statistics, visualization, Power BI, machine learning, and artificial intelligence fundamentals. The course follows a structured learning path supported by practical exercises and project-based learning.

Students gradually develop the knowledge required to understand data, build predictive models, and create meaningful visual reports using industry-relevant tools.

Module 1: Python Programming

• Python Basics

• Variables & Data Types

• Operators

• Conditional Statements

• Loops

• Functions

• OOP Concepts

• File Handling

• Exception Handling

• Modules & Packages

Mini Project

Module 2: SQL for Data Analytics

• Database Concepts

• CRUD Operations

• Filtering & Sorting

• Aggregate Functions

• Joins

• Subqueries

• Views

• Stored Procedures

• Window Functions

• CTE

Real-Time SQL Project

Module 3: Advanced Excel

• Data Cleaning

• Lookup Functions

• Pivot Tables

• Charts

• Conditional Formatting

• Dashboard Creation

• Data Validation

Business Dashboard Project

Module 4: Statistics

• Mean, Median, Mode

• Standard Deviation

• Variance

• Probability

• Distribution

• Hypothesis Testing

• Correlation

• Regression

Module 5: Python Libraries

• NumPy

• Pandas

• Matplotlib

• Seaborn

• Plotly

Data Analysis Project

Module 6: Data Visualization

• Charts

• Dashboards

• Storytelling

• KPI Analysis

• Business Reports

Module 7: Power BI

• Power Query

• Data Modeling

• DAX

• Relationships

• KPIs

• Interactive Dashboards

• Publishing Reports

Industry Dashboard Project

Module 8: Machine Learning

• ML Workflow

• Data Preprocessing

• Feature Engineering

• Supervised Learning

• Unsupervised Learning

• Model Evaluation

• Hyperparameter Tuning

Algorithms

• Linear Regression

• Logistic Regression

• Decision Tree

• Random Forest

• KNN

• Naive Bayes

• SVM

• K-Means Clustering

Machine Learning Project

Module 9: Artificial Intelligence Basics

• Introduction to AI

• Generative AI

• Prompt Engineering

• ChatGPT for Data Science

• AI Productivity Tools

Module 10: Real-Time Capstone Projects

• Sales Dashboard

• HR Analytics

• Financial Analysis

• Customer Churn Prediction

• IPL Data Analysis

• Healthcare Analytics

FAQs

1. What skills are covered in a Data Science with Python course?

The course covers Python programming, SQL, statistics, data analysis, visualization, Power BI, machine learning, artificial intelligence basics, and practical data science projects.

2. Is Python necessary for learning Data Science?

Yes. Python is one of the most commonly used programming languages for data analysis, machine learning, automation, and artificial intelligence applications.

3. What projects can students build during a Data Science course?

Students can work on projects such as sales dashboards, customer churn prediction, healthcare analytics, financial analysis, HR analytics, and machine learning models to apply their knowledge in practical scenarios.