Data Science Bootcamp Udemy Course

I attended a Data Science Bootcamp Course from Udemy online and these are the notes of the course.

• Introduction

• Probability

  • Combinatorics

  • Bayesian Inference

  • Distributions

  • Probability in Other Fields

• Statistics

  • Descriptive Statistics

  • Practical Example: Descriptive Statistics

  • Inferential Statistics Fundamentals

  • Inferential Statistics: Confidence Intervals

  • Practical Example: Inferential Statistics

  • Hypothesis Testing

  • Practical Example: Hypothesis Testing

• Introduction to Python

  • Variables and Data Types

  • Basic Python Syntax

  • Other Python Operators

  • Conditional Statements

  • Python Functions

  • Sequences

  • Iterations

  • Advanced Python Tools

• Advanced Statistical Methods in Python

  • Linear regression with StatsModels

  • Multiple Linear Regression with StatsModels

  • Linear Regression with sklearn

  • Practical Example: Linear Regression

  • Logistic Regression

  • Cluster Analysis

  • K-Means Clustering

  • Other Types of Clustering

• Mathematics

• Deep Learning

  • Introduction to Neural Networks

  • How to Build a Neural Network from Scratch with NumPy

  • TensorFlow 2.0: Introduction

  • Digging Deeper into NNs: Introducing Deep Neural Networks

  • Overfitting

  • Initialization

  • Digging into Gradient Descent and Learning Rate Schedules

  • Preprocessing

  • Classifying on the MNIST Dataset

  • Business Case Example

  • Conclusion

• Case Study

  • Case Study - What is Next in the Course?

  • Case Study - Preprocessing the Absenteeism Data

  • Case Study - Applying Machine Learning to Create the absenteeism Module

  • Case Study - Loading the absenteeism Module

  • Case Study - Analyzing the Predicted Outputs in Tableau

• Appendix

  • Deep Learning - TensorFlow 1: Introduction

  • Deep Learning - TensorFlow 1: Classifying on the MNIST Dataset

  • Deep Learning - TensorFlow 1: Business Case

  • Software Integration

Introduction

Please find the Big picture of Data science here in :

Big picture of the data science term definitions

Some important things to keep in mind:

• Analytics vs Analysis Term Analytics relates to the future events. It includes exploring of patterns and prediction. Term analysis relates to the data from past events(existing data). It explains how and when.

• Business intelligence Process of analysing and reporting historical business data. Aims to explain past events using business data.

• Machine learning The ability of machines to predict outcomes without being explicitly programmed. Creating and implementing algorithms that let machines receive data and use this data to: 1. Make predictions, 2. Analyse Patterns, 3. Give recommendations.

• Business intelligence Simulating human knowledge and decision making with computers.

Overview of Course

Probability

Combinatorics

Permutations (Arrange)(Used for arrangement of objects & order is relevant): Arrange entire set of elements in the sample space. Variations (Pick and arrange) (Used for arrangement of objects & order is relevant): Arrange only a few elements from the set of elements in the sample space. Combination (Pick)(order is irrelevant)

No repetition

Repetition

Bayesian Inference

Distributions

Probability in Other Fields

Statistics

Descriptive Statistics

Practical Example: Descriptive Statistics

Inferential Statistics Fundamentals

Inferential Statistics: Confidence Intervals

Practical Example: Inferential Statistics

Hypothesis Testing

Practical Example: Hypothesis Testing

Introduction to Python

Variables and Data Types

Basic Python Syntax

Other Python Operators

Conditional Statements

Python Functions

Sequences

Iterations

Advanced Python Tools

Advanced Statistical Methods in Python

Linear regression with StatsModels

Multiple Linear Regression with StatsModels

Linear Regression with sklearn

Practical Example: Linear Regression

Logistic Regression

Cluster Analysis

K-Means Clustering

Other Types of Clustering

Mathematics

Deep Learning

Introduction to Neural Networks

How to Build a Neural Network from Scratch with NumPy

TensorFlow 2.0: Introduction

Digging Deeper into NNs: Introducing Deep Neural Networks

Overfitting

Initialization

Digging into Gradient Descent and Learning Rate Schedules

Preprocessing

Classifying on the MNIST Dataset

Business Case Example

Conclusion

Case Study

Case Study - What is Next in the Course?

Case Study - Preprocessing the Absenteeism Data

Case Study - Applying Machine Learning to Create the absenteeism Module

Case Study - Loading the absenteeism Module

Case Study - Analyzing the Predicted Outputs in Tableau

Appendix

Deep Learning - TensorFlow 1: Introduction

Deep Learning - TensorFlow 1: Classifying on the MNIST Dataset

Deep Learning - TensorFlow 1: Business Case

Software Integration