Data Science in Python Course

This is a detailed Hands-on Data Science course with Python with Capstone Projects from various domains. This course is suitable for all levels of learners from beginners to advanced students.

4.9
5740+
₹31,150.00 ($382.34)
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Course Overview Highlights Curriculum Mentors

About Our Course:

Welcome to Complete Data Science Course With Python. This course includes important Data Science topics such as  Data Collection, Data Visualization, Data Preprocessing, Machine Learning.

In this course you will learn the basics of Python Programming required for Data Science and Important Python Libraries required for Data Science.


After completing these modules you will learn how to use your knowledge of Data Science in Projects. This course includes Capstone Projects including Building a Movie Recommendation System, Cancer Prediction, etc.

Data Science in Python Course Thumbnail
Data Science in Python Course
What you will learn ?
  • Data Collection
  • Data visualization & analysis
  • Data Preprocessing
  • Model selection
  • Python Data Type
  • Operators in Python
  • Tuple, Sets & Dictionary
  • Loops
  • Numpy & Pandas
  • Dataset standardization
  • Label encoding
  • AL vs ML vs DL
  • Data Science Projects
  • Lecture 2 Lectures
  • Lecture 11 Lectures
  • Overview of the steps involved
  • Understanding the problem statement
  • Data Collection
  • Data Visualization and Analysis
  • Data Preprocessing - Part 1
  • Data Preprocessing - Part 2
  • Model Selection
  • Evaluation
  • Presenting our Insights
  • Summary
  • Module 2 : Steps involved in a Data Science Project
  • Lecture 3 Lectures
  • Overview
  • Google Colaboratory - Getting Started
  • Google Colab - Basics
  • Lecture 10 Lectures
  • Python Basics - Synopsis
  • Python Basics
  • Basic Python Data Types
  • Basic Operators in Python
  • Special Data Types - list
  • Tuple, Set and Dictionary
  • If Statements
  • Loops
  • Module 4 : Python Basics
  • Assignment 1 - Python basics
  • Lecture 9 Lectures
  • Overview
  • Numpy Basics Part - 1
  • Numpy Basics Part - 2
  • Numpy Basics - Part 3
  • Pandas Basics - Part 1
  • Pandas Basics - Part 2
  • Other important Python Libraries
  • Module 5 : Numpy and Pandas Tutotial
  • Assignment 2 - Pandas and Numpy Tutorial
  • Lecture 7 Lectures
  • Data Collection - Overview
  • Getting the dataset - part 1
  • Getting the dataset - part 2
  • Importing Kaggle Dataset through API
  • Web Scrapping - Wikipedia Table
  • Summary
  • Assignment 3 - Data Collection
  • Lecture 7 Lectures
  • Data Visualization - Overview
  • Data Visualization - Example
  • Basic Plots - Matplotlib
  • Iris Dataset - Visualization
  • Titanic Dataset - Visualization
  • Module 7 : Data Visualization and Analysis
  • Assignment 4 - Data Visualization and Analysis
  • Lecture 8 Lectures
  • Data Preprocessing - Overview
  • Dataset Manipulation - Basics
  • Handling missing values - by Dropping
  • Handling missing values - by Imputation
  • Dataset Standardization
  • Label Encoding
  • Module 8: Data Pre-processing
  • Assignment 5 - Data pre-processing
  • Lecture 8 Lectures
  • Overview
  • AI vs ML vs DL
  • Machine Learning
  • Types of Machine Learning
  • Model Selection
  • 9.6. Logistic Regression model
  • Support Vector Machine model
  • Module 9 : Machine Learning - Basics
  • Lecture 10 Lectures
  • Data Science Projects - Overview
  • Breast Cancer Prediction - Overview
  • Breast Cancer Prediction - Logistic Regression - Part 1
  • Breast Cancer Prediction - Part 2
  • Iris Clustering
  • Spam mail prediction
  • Movie Recommendation System - Overview
  • Movie Recommendation System - Part 1
  • Movie Recommendation System - Part 2
  • Assignment 6 - Data Science projects
Our Mentors:
Siddhardhan S

Siddhardhan S

Data Science Expert

4.6 Rating

Sample Certificate:
What Siddhardhan S has to say:

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