This course provides a complete introduction to the world of Data Science, guiding learners from foundational concepts to practical data analysis and visualization. You’ll gain hands-on experience in Python programming, data manipulation, SQL querying, and statistical analysis — empowering you to make sense of complex datasets and communicate insights effectively.

Master Python for Data Analysis using libraries such as Pandas and NumPy.
Write and execute SQL queries to extract and manage data from relational databases.
Visualize and tell stories with data using tools like Matplotlib and Seaborn.
Apply core statistical and probabilistic concepts to analyze trends and patterns.
Clean, process, and transform raw data into actionable insights through hands-on exercises.
Complete a capstone project demonstrating your full data analysis workflow.

Course Content

Module 1: Introduction to Python

Python Basic 1

20:51
Python Basic 2

05:59
Python Basic 3

19:26
Python Basic 4

00:00

Module 2: Object Oriented Programming(OOPs)
Object-Oriented Programming (OOP) is a programming paradigm based on the concept of objects, which combine data and behavior into a single unit. It focuses on four main principles — Encapsulation, Abstraction, Inheritance, and Polymorphism — to make code more modular, reusable, and easier to maintain. OOP helps model real-world entities, improving code structure and flexibility.

Module 3: Numpy

Module 4: Pandas
Pandas is a powerful Python library used for data analysis and manipulation. It provides easy-to-use data structures like Series (1D) and DataFrame (2D) for handling structured data efficiently. Key features include: Data Handling: Import and export data from formats like CSV, Excel, SQL, and JSON. Data Cleaning: Handle missing data, duplicates, and perform filtering or transformation. Data Analysis: Perform operations like grouping, aggregation, sorting, and merging. Data Visualization: Integrates easily with libraries like Matplotlib for visual analysis. In short, Pandas simplifies working with large datasets, making data cleaning, analysis, and visualization faster and more intuitive.

Module 5: Data Visualization

Module 6: Statistics

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SKILLsFLICK

Student Ratings & Reviews

5.0

Total 3 Ratings

3 Ratings

0 Rating

Shashank Sharma

5 months ago

Totally worth it !!

goohf

5 months ago

Thank you SKILLsFLick

Govind singh

5 months ago

It was the best Experience

What Will You Learn?

Material Includes

Requirements

Audience

Course Content

Module 1: Introduction to Python

Python Basic 1

Python Basic 2

Python Basic 3

Python Basic 4

Introduction to OOPs

Inheritance and Polymorphism

Encapsulation and Abstraction

Module 3: Numpy

Numpy methods

Random Library

Pandas Method

Data analysis with Pandas

Module 5: Data Visualization

Matplotlib Introduction

Matplotlib Part 2

Python Visualization

Seaborn

Module 6: Statistics

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Data Science Training

What Will You Learn?

Material Includes

Requirements

Audience

Course Content

Module 1: Introduction to Python

Python Basic 1

Python Basic 2

Python Basic 3

Python Basic 4

Introduction to OOPs

Inheritance and Polymorphism

Encapsulation and Abstraction

Module 3: Numpy

Numpy methods

Random Library

Pandas Method

Data analysis with Pandas

Module 5: Data Visualization

Matplotlib Introduction

Matplotlib Part 2

Python Visualization

Seaborn

Module 6: Statistics

Tags

A course by

Student Ratings & Reviews

Course Includes:

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