Science Academy

A message from the instructor!

Tips to successful in the filed of Data Science

Course Introduction

Setup jupyter notebook and environment - part 1

Setup jupyter notebook and environment - part 2

Environment setup - Other Options/suggestions

Please download the course material

Please read.

Possible update in the course.

Python datatypes - Part 1

Python datatypes - Part 2

Comparisons Operators, if, else, elif statement

Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 1)

Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 2)

Python Essentials Exercises Overview

Python Essentials Exercises Solutions

What is Numpy? A brief introduction.

NumPy Essentials - Indexing, slicing, broadcasting & boolean masking

NumPy Essentials - Arithmetic Operations & Universal Functions

NumPy Essentials Exercises Overview

NumPy Essentials Exercises Solutions

What is pandas? A brief introduction and installation instructions.

Pandas Introduction - little more!

TIP: Good to know - How to avoid unnecessary future warning to keep your notebook clean!

Pandas Essentials - Pandas Data Structures - Series

Pandas Essentials - Pandas Data Structures - DataFrame

Pandas Essentials - Hierarchical Indexing

Pandas Essentials - Handling Missing Data

Pandas Essentials - Data Wrangling - Combining, merging, joining

Pandas Essentials - Useful Methods and Operations

Pandas Essentials - Project 1 (Overview) Customer Purchases Data

Pandas Essentials - Project 1 (Solutions) Customer Purchases Data

Pandas Essentials - Project 2 (Overview) Chicago Payroll Data

Pandas Essentials - Project 2 (Solutions Part 2) Chicago Payroll Data

Pandas Essentials - Project 2 (Solutions Part 1) Chicago Payroll Data

Matplotlib Essentials (Part 1) - Basic Plotting & Object Oriented Approach

Matplotlib Essentials (Part 2) - Basic Plotting & Object Oriented Approach

Matplotlib Essentials (Part 3) - Basic Plotting & Object Oriented Approach

Matplotlib Essentials - Exercises Overview

Matplotlib Essentials - Exercises Solutions

Matplotlib Essentials (Optional) - Advance

Seaborn - Introduction (& Installation - if needed)

Seaborn - Distribution Plots

Seaborn - Categorical Plots (Part 1)

Seaborn - Categorical Plots (Part 2)

Seaborn - Matrix Plots

Seaborn - Regression Plots

Seaborn - Controlling Figure Aesthetics

Seaborn - Exercises Overview

Seaborn - Exercise Solutions

Python for Data Visualization using pandas

Pandas Data Visualization Exercises Overview

Panda Data Visualization Exercises Solutions

Plotly & Cufflinks - Interactive & Geographical Plotting (Part 1)

Plotly & Cufflinks - Interactive & Geographical Plotting (Part 2)

Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Overview)

Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Solutions)

Project 1 - Oil vs Banks Stock Price during recession (Overview)

Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 1)

Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 2)

Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 3)

Project 2 (Optional) - Emergency Calls from Montgomery County, PA (Overview)

Introduction to ML - What, Why and Types.....

Theory Lecture on Linear Regression Model, No Free Lunch, Bias Variance Tradeoff

TIP: How to avoid Future warning in your jupyter notebook!

scikit-learn - Linear Regression Model - Hands-on (Part 1)

scikit-learn - Linear Regression Model Hands-on (Part 2)

Good to know: Pickle it! -- How to save and load your trained Machine Learning model

scikit-learn - Linear Regression Model (Insurance Data Project Overview)

scikit-learn - Linear Regression Model (Insurance Data Project Solutions)

Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificity...etc.

Output of classification report in scikit-learn — A small change

scikit-learn - Logistic Regression Model - Hands-on (Part 1)

scikit-learn - Logistic Regression Model - Hands-on (Part 2)

scikit-learn - Logistic Regression Model - Hands-on (Part 3)

scikit-learn - Logistic Regression Model - Hands-on (Project Overview)

scikit-learn - Logistic Regression Model - Hands-on (Project Solutions)

Theory: K Nearest Neighbors, Curse of dimensionality ....

scikit-learn - K Nearest Neighbors - Hands-on

scikt-learn - K Nearest Neighbors (Project Overview)

scikit-learn - K Nearest Neighbors (Project Solutions)

Theory: D-Tree & Random Forests, splitting, Entropy, IG, Bootstrap, Bagging....

scikit-learn - Decision Tree and Random Forests (Project Overview)

scikit-learn - Decision Tree and Random Forests (Project Solutions)

Support Vector Machines (SVMs) - (Theory Lecture)

scikit-learn - Support Vector Machines - Hands-on (SVMs)

scikit-learn - Support Vector Machines (Project 1 Overview)

scikit-learn - Support Vector Machines (Project 1 Solutions)

scikit-learn - Support Vector Machines (Optional Project 2 - Overview)

scikit-learn - K Means Clustering - Hands-on

scikit-learn - K Means Clustering (Project Overview)

scikit-learn - K Means Clustering (Project Solutions)

Theory: Principal Component Analysis (PCA)

scikit-learn - Principal Component Analysis (PCA) - Hands-on

scikit-learn - Principal Component Analysis (PCA) - (Project Overview)

scikit-learn - Principal Component Analysis (PCA) - (Project Solutions)

Theory: Recommender Systems their Types and Importance

Python for Recommender Systems - Hands-on (Part 1)

Python for Recommender Systems - - Hands-on (Part 2)

NLTK - NLP-Challenges, Data Sources, Data Processing .....

NLTK - Feature Engineering and Text Preprocessing in Natural Language Processing

NLTK - BoW, TF-IDF, Machine Learning, Training & Evaluation, Naive Bayes ...

NLTK - NLP - Pipeline feature to assemble several steps for cross-validation...

Please read, it's important!

Congratulations and thanks you for doing the course!