# Data Science Bootcamp Udemy Course

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

* [Introduction](#introduction)
* [Probability](#probability)
  * [Combinatorics](#combinatorics)
  * [Bayesian Inference](#bayesian-inference)
  * [Distributions](#distributions)
  * [Probability in Other Fields](#probability-in-other-fields)
* [Statistics](#statistics)
  * [Descriptive Statistics](#descriptive-statistics)
  * [Practical Example: Descriptive Statistics](#practical-example-descriptive-statistics)
  * [Inferential Statistics Fundamentals](#inferential-statistics-fundamentals)
  * [Inferential Statistics: Confidence Intervals](#inferential-statistics-confidence-intervals)
  * [Practical Example: Inferential Statistics](#practical-example-inferential-statistics)
  * [Hypothesis Testing](#hypothesis-testing)
  * [Practical Example: Hypothesis Testing](#practical-example-hypothesis-testing)
* [Introduction to Python](#introduction-to-python)
  * [Variables and Data Types](#variables-and-data-types)
  * [Basic Python Syntax](#basic-python-syntax)
  * [Other Python Operators](#other-python-operators)
  * [Conditional Statements](#conditional-statements)
  * [Python Functions](#python-functions)
  * [Sequences](#sequences)
  * [Iterations](#iterations)
  * [Advanced Python Tools](#advanced-python-tools)
* [Advanced Statistical Methods in Python](#advanced-statistical-methods-in-python)
  * [Linear regression with StatsModels](#linear-regression-with-statsmodels)
  * [Multiple Linear Regression with StatsModels](#multiple-linear-regression-with-statsmodels)
  * [Linear Regression with sklearn](#linear-regression-with-sklearn)
  * [Practical Example: Linear Regression](#practical-example-linear-regression)
  * [Logistic Regression](#logistic-regression)
  * [Cluster Analysis](#cluster-analysis)
  * [K-Means Clustering](#k-means-clustering)
  * [Other Types of Clustering](#other-types-of-clustering)
* [Mathematics](#mathematics)
* [Deep Learning](#deep-learning)
  * [Introduction to Neural Networks](#introduction-to-neural-networks)
  * [How to Build a Neural Network from Scratch with NumPy](#how-to-build-a-neural-network-from-scratch-with-numpy)
  * [TensorFlow 2.0: Introduction](#tensorflow-20-introduction)
  * [Digging Deeper into NNs: Introducing Deep Neural Networks](#digging-deeper-into-nns-introducing-deep-neural-networks)
  * [Overfitting](#overfitting)
  * [Initialization](#initialization)
  * [Digging into Gradient Descent and Learning Rate Schedules](#digging-into-gradient-descent-and-learning-rate-schedules)
  * [Preprocessing](#preprocessing)
  * [Classifying on the MNIST Dataset](#classifying-on-the-mnist-dataset)
  * [Business Case Example](#business-case-example)
  * [Conclusion](#conclusion)
* [Case Study](#case-study)
  * [Case Study - What is Next in the Course?](#case-study---what-is-next-in-the-course)
  * [Case Study - Preprocessing the Absenteeism Data](#case-study---preprocessing-the-absenteeism-data)
  * [Case Study - Applying Machine Learning to Create the absenteeism Module](#case-study---applying-machine-learning-to-create-the-absenteeism-module)
  * [Case Study - Loading the absenteeism Module](#case-study---loading-the-absenteeism-module)
  * [Case Study - Analyzing the Predicted Outputs in Tableau](#case-study---analyzing-the-predicted-outputs-in-tableau)
* [Appendix](#appendix)
  * [Deep Learning - TensorFlow 1: Introduction](#deep-learning---tensorflow-1-introduction)
  * [Deep Learning - TensorFlow 1: Classifying on the MNIST Dataset](#deep-learning---tensorflow-1-classifying-on-the-mnist-dataset)
  * [Deep Learning - TensorFlow 1: Business Case](#deep-learning---tensorflow-1-business-case)
  * [Software Integration](#software-integration)

## Introduction

Please find the Big picture of Data science here in :

![Big picture of the data science term definitions](https://4024498018-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FaaCss5X0lrocT88qJJsY%2Fuploads%2Fgit-blob-ec2cf9dd7542080c83618422fa2f4d9bdde7744b%2FBigPicture.png?alt=media)

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](https://4024498018-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FaaCss5X0lrocT88qJJsY%2Fuploads%2Fgit-blob-84609a0d9b505d9c509c20c910dba8bdc50fd5dc%2FCourseOverview.png?alt=media)

## 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