Data Science with Python Introduction

• Course Overview with Data Science

Environment Set-Up

• Environment Set-up and Installation –
• Set up Anaconda, Jupyter, Ipython and install Python.
• Set up an IDE – Option to choose from installing – PyCharm CE or Sublime or
• VIM or Emacs or VI

Jupyter Overview

• Jupyter Notebooks
• Optional: Virtual Environments

Python Crash Course

• Introduction to Python Crash Course
• Python Crash Course – Part 1 – Basics
• Python Crash Course – Part 2 – OOPS concepts
• Python Crash Course – Part 3 – Modules
• Python Crash Course – Part 4 – Final
• Python Crash Course Exercises – Overview
• Python Crash Course Exercises – Solutions

Python for Data Analysis-NumPy

• Introduction to Numpy
• Numpy Arrays
• Quick Note on Array Indexing
• Numpy Array Indexing and Operations
• Numpy Exercises Overview and Solutions

Python for Data Analysis-Pandas

• Introduction to Pandas
• Series
• Data Frames – Part 1 Introduction
• Data Frames – Part 2 Organizing
• Data Frames – Part 3 Set up
• Missing Data
• Group by
• Merging Joining and Concatenating
• Operations
• Data Input and Output

Python for Data Analysis-Pandas Exercises

• Salaries Exercise Overview
• Note on SF Salary Exercise
• SF Salaries Solutions
• E-commerce Purchases Exercise Overview
• E-commerce Purchases Exercise Solutions

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

Python for Data Visualization-Pandas Built-in Data Visualization

• Pandas Built-in Data Visualization
• Pandas Data Visualization Exercise
• Pandas Data Visualization Exercise- Solutions

Python for Data Visualization-Plotly and Cufflinks

• Introduction to Plotly and Cufflinks
• Plotly and Cufflinks

Python for Data Visualization-Geographical Plotting

• Introduction to Geographical Plotting
• Choropleth Maps – Part 1 – USA
• Choropleth Maps – Part 2 – World
• Choropleth Exercises
• Choropleth Exercises – 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

Neural Nests and Deep Learning

• Neural Network Theory
• Welcome to the Deep Learning Section!
• What is TensorFlow?
• Changes with TensorFlow
• TensorFlow Installation
• TensorFlow Basics
• MNIST with Multi-Layer Perceptron
• TensorFlow with ContribLearn
• Tensorflow Project Exercise Overview
• Tensorflow Project Exercise – Solutions

Generative AI

• Introduction to Generative AI
• Generative Models (e.g., Generative Adversarial Networks, Variational Autoencoders)
• Probability and Distribution Modeling
• Sampling Techniques
• Data Preprocessing for Generative Models
• Loss Functions for Generative Models
• Training and Optimization Methods
• Conditional Generation
• Text Generation
• Image Generation
• Video Generation
• Music and Audio Generation
• Style Transfer and Domain Translation
• Evaluation Metrics for Generative Models
• Challenges and Limitations of Generative AI
• Ethical Considerations in Generative AI
• Transfer Learning for Generative Models
• Interpretability and Explainability in Generative AI
• Advanced Techniques in Generative AI (e.g., Deep Reinforcement Learning, Attention Mechanisms)
• Applications of Generative AI (e.g., Creative Content Generation, Data Augmentation, Anomaly Detection)