Data Science with Python Complete Course
Categories: Data Science, Development

About Course
Buy premium plans to access our amazing courses. Learn More.
What Will You Learn?
- Perform high-level mathematical and technical computing using the NumPy and SciPy packages and data analysis with the Pandas package
- Master the essential concepts of Python programming, including data types, tuples, lists, dicts, basic operators, and functions.
- Gain an in-depth understanding of Data Science processes: data wrangling, data exploration, data visualization, hypothesis building, and testing
- Apply knowledge and actionable insights from data across a broad range of application domains.
Course Content
Introduction
Basic Maths Required for Data Science
-
00:00
-
00:00
-
Types of Statistics
00:00 -
00:00
-
Measures of Shapes
00:00 -
Plots Visualisation
00:00 -
Inferential Statistics
00:00 -
Probability
00:00 -
Conditional Probability
00:00 -
Random Variables
00:00 -
Normal Probability Distribution
00:00 -
Central Limit Theorem
00:00 -
Hypothesis Testing for Decision Making
00:00
python for Data Science
-
00:00
-
00:00
-
Python Basics
00:00 -
Identifiers in Python
00:00 -
Comments in Python
00:00 -
Python Indentation
00:00 -
Python Statements
00:00 -
Variables in Python
00:00 -
Data Types & Related Stuffs in Python
00:00 -
Conversion of Data Types in Python
00:00 -
Python I/O functions
00:00 -
Output Formatting
00:00 -
User Input in Python
00:00 -
Operators in Python
00:00 -
Control Flow in Python
00:00 -
Functions in Python
00:00 -
Types of Functions in Python
00:00 -
Recursive Functions in Python
00:00 -
Argument in a Function
00:00 -
Lambda or Anonymous Functions in Python
00:00
Advance Python
-
Advance Programming in Python
00:00 -
Advance Programming in Python: Part 2
00:00 -
Data Visualisations
00:00 -
Bivariate Plotting
-
Multivariate Plotting
00:00
Let’s Dig Deeper
-
EDA
00:00 -
EDA on Mc’donalds Data Set
00:00 -
Exploratory Data Analysis
00:00
Let’s explore Machine Learning
-
Introduction: Machine Learning
00:00 -
Unsupervised Learning
00:00 -
Reinforement Learning
00:00
Module Seven
-
Linear Regression
00:00 -
How to use Linear Regression
00:00 -
Logistic Regression
00:00 -
Logistic Regression on Titanic Data Set
00:00 -
Decision Tree
00:00 -
Algorithms used in Decision Treee
00:00 -
Gini Index
00:00 -
Issues with Decision Tree
00:00 -
Applications of Decision Tree
00:00 -
Working on Titanic Data Set
00:00 -
Random Forest
00:00 -
Types of Random Forest
00:00 -
Why Random Forest
00:00 -
Application of Random Forest
00:00 -
Random Forest Implementation on Titanic Data Set
00:00 -
Model Evaluation Technique
00:00 -
Concept of R-Squared
00:00 -
Linear Regression
00:00 -
Classification
00:00 -
Confusion Matrix
00:00 -
Recall / Sensitivity / True Rate of Positive
00:00 -
FB score
00:00 -
AUC/ ROC curve
00:00 -
Model Evaluation recall Curve
00:00
Module Eight
-
Data Analysis using R
00:00 -
Data Analysis using R: part 2
00:00 -
All about R Language
00:00
Featured Topics
-
Big Data
00:00 -
Intro to Hadoop
00:00 -
Intro to Tableu
00:00 -
Intro to Business Analytics
00:00
Project: Telecom Churn Production
-
Project: Part 1: Let’s get our system ready
00:00 -
Project: part 2
00:00 -
Project: Part 3
00:00 -
Project: part 4
00:00 -
Project: Let’s Finalise it
00:00
Course Resources
-
Additional Resources : Downloadable