DATA SCIENCE USING PYTHON TRAINING

Course Overview

• Overview of Data science
• What is Data Science
• Different Sectors Using Data Science

Data Analytics Overview

• Exploratory Data Analysis(EDA)
• EDA-Quantitative Technique
• EDA - Graphical Technique
• Data Analytics Conclusion or Predictions
• Data Types for Plotting
• Data Types and Plotting

Mathematical Computing with Python(NumPy)

• Introduction to Numpy
• Activity-Sequence
• Creating and Printing an ndarray
• Class and Attributes of ndarray
• Basic Operations
• Activity – Slicing
• Copy and Views
• Mathematical Functions of Numpy
• Advance Slicing
• Transpose and arance
• Searching

Scientific Computing with Python(SciPy)

• Introduction of SciPy
• SciPy Sub Package - Integration and Optimization
• SciPy sub package
• Demo - Calculate Eigenvalues and Eigenvector
• Demo - Calculate Eigenvalues and Eigenvector

Data Manipulation with Pandas

• Introduction of Pandas
• Data Types in Pandas
• Understanding Series
• Understanding DataFrame
• View and Select Data Demo
• Missing Values
• Data Operations
• File Read and Write Support
• Pandas Sql Operation

Python for Data Visualization - Matplotlib

• Introduction to Matplotlib
• Matplotlib Part 1 Set up
• Matplotlib Part 2 Plot
• Matplotlib Part 3 Next steps
• Matplotlib Exercises Overview
• Matplotlib Exercises – Solutions

Python for Data Visualization - Seaborn

• Introduction to Seaborn
• Distribution Plots
• Categorical Plots
• Matrix Plots
• Regression Plots
• Grids
• Style and Color
• Seaborn Exercise Overview
• Seaborn Exercise Solutions

Introduction to Machine Learning

• Link for ISLR
• Introduction to Machine Learning
• Machine Learning with Python

Linear Regression

• Linear Regression Theory
• Model selection Updates for SciKit Learn
• Linear Regression with Python – Part 1 Introduction
• Linear Regression with Python – Part 2 Deep Dive
• Linear Regression Project Overview and Project Solution
• Logistic Regression
• Logistic Regression Theory – Introduction
• Logistic Regression with Python – Part 1 – Logistics
• Logistic Regression with Python – Part 2 – Regression
• Logistic Regression with Python – Part 3 – Conclusion
• Logistic Regression Project Overview and Project Solutions

K Nearest Neighbours

• KNN Theory
• KNN with Python
• KNN Project Overview and Project Solutions

Decision Trees and Random Forests

• Introduction to Tree Methods
• Decision Trees and Random Forest with Python
• Decision Trees and Random Forest Project Overview
• Decision Trees and Random Forest Solutions Part 1
• Decision Trees and Random Forest Solutions Part 2

Support Vector Machines

• SVM Theory
• Support Vector Machines with Python
• SVM Project Overview
• SVM Project Solutions

K Means Clustering

• K Means Algorithm Theory
• K Means with Python
• K Means Project Overview
• K Means Project Solutions

Principal Component Analysis

• Principal Component Analysis
• PCA with Python

Recommender Systems

• Recommender Systems
• Recommender Systems with Python – Part 1 The Foundation
• Recommender Systems with Python – Part 2 Deep Dive

Natural Language Processing

• Natural Language Processing Theory
• NLP with Python
• NLP Project Overview
• NLP Project Solutions

Big Data and Spark with Python

• Big Data Overview
• Spark Overview
• Local Spark Set-Up
• AWS Account Set-Up
• Quick Note on AWS Security
• EC2 Instance Set-Up
• SSH with Mac or Linux
• PySpark Setup
• Lambda Expressions Review
• Introduction to Spark and Python
• RDD Transformations and Actions