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

20:52
Python Basic 2

05:59
Python Basic 3

19:26
Python Basic 4

12:49

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
In the previous module, you learned how to work with numerical data using NumPy. In this module, you will learn Pandas, one of the most widely used Python libraries for data analysis and manipulation. Pandas helps Data Scientists organize, clean, filter, and analyze data efficiently. It allows you to work with datasets in a tabular format, similar to Microsoft Excel, making it easier to extract meaningful insights. By the end of this module, you will be able to import datasets, manipulate data, handle missing values, and perform basic exploratory data analysis (EDA).

Module 5: Data Visualization

Module 6: Statistics
Statistics is the foundation of Data Science and Machine Learning. It helps us collect, organize, analyze, and interpret data to make informed decisions. Every Machine Learning algorithm relies on statistical concepts to identify patterns, make predictions, and evaluate model performance. Statistics is broadly divided into two branches: Descriptive Statistics – Summarizes and describes the features of a dataset. Inferential Statistics – Uses sample data to make predictions or draw conclusions about an entire population. By the end of this module, students will understand the essential statistical concepts required for data analysis and Machine Learning.

Module 7: Linear Algebra for Data Science

Module 8: Machine Learning Fundamentals

Lesson 1: Introduction to Machine Learning

10:40
Lesson 2: Logistic Regression

01:32
Lesson 3: Sigmoid Function and Binary Classification

01:25
Lesson 4: Model Evaluation, Advantages, Limitations & Implementation of Logistic Regression

02:45
Lesson 5: Decision Tree Algorithm

02:52
Lesson 6: Decision Tree Concepts, Pruning, and Real-World Applications

06:49
Lesson 7: Decision Tree Visualization and Python Implementation

02:55
Lesson 8: Random Forest and Introduction to K-Means Clustering

21:44
Lesson 9: Time Series Analysis, ARIMA, and SARIMA

15:40

Module 9: Regression

Module 10: Classification

A course by

SKILLsFLICK

Student Ratings & Reviews

4.8

Total 4 Ratings

3 Ratings

1 Rating

0 Rating

Khushi Kumari

4 days ago

Useful resources for beginners

Shashank Sharma

11 months ago

Totally worth it !!

goohf

11 months ago

Thank you SKILLsFLick

Govind singh

11 months ago

It was the best Experience

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

Lesson 1: Introduction To Pandas

Lesson 2: Data analysis with Pandas

Module 5: Data Visualization

Matplotlib Introduction

Matplotlib Part 2

Python Visualization

Seaborn

Lesson 1: Descriptive Statistics – Part 1

Lesson 2: Descriptive stats- Part 2

Lesson 3: Descriptive stats Part-3

Lesson 4: Inferential Statistics – Part 1

Lesson 5: Inferential Statistics – Part 2

Lesson 6: Inferential Statistics – Part 3

Module 7: Linear Algebra for Data Science

Lesson 1: Introduction to Linear Algebra and Matrix Basics

Lesson 2: Matrix Operations, Rank, and Matrix Factorization

Lesson 3: Matrix Decomposition, Eigenvalues, and Eigenvectors

Module 8: Machine Learning Fundamentals

Lesson 1: Introduction to Machine Learning

Lesson 2: Logistic Regression

Lesson 3: Sigmoid Function and Binary Classification

Lesson 4: Model Evaluation, Advantages, Limitations & Implementation of Logistic Regression

Lesson 5: Decision Tree Algorithm

Lesson 6: Decision Tree Concepts, Pruning, and Real-World Applications

Lesson 7: Decision Tree Visualization and Python Implementation

Lesson 8: Random Forest and Introduction to K-Means Clustering

Lesson 9: Time Series Analysis, ARIMA, and SARIMA

Module 9: Regression

Lesson 1: Linear Regression 1

Lesson 2: Data Preprocessing for Linear Regression

Lesson 3: Detecting and Removing Outliers Using the IQR Method

Lesson 4

Module 10: Classification

Tags

A course by

Student Ratings & Reviews

Course Includes:

Share On:

Courses You May Like

Sign in

Sign up