Data Science For Everyone

Data Science Essentials for Beginners

What you'll learn

Basics of data science

Basics of machine learning

Basics of statistical inference

Basics of data-driven decision making

Requirements

None

Description

Welcome to this course, data science for everyone. In this series of lectures, I will provide you with essentials of data science.This course is targeted for managers who are not data scientist but need to manage data analytic projects. It is also targeted for managers who want to introduce data-driven management. So, the knowledge provided in this course is both theoretical and pragmatic, but not includes details of mathematics and coding. However, anyone who are beginners in data science are also welcome because this course can provide you with essentials for learning technical aspects of data science.You will learn: - Essentials concepts and theories for learning technical aspects of data science.- Pragmatic knowledge for interpreting data and results of data analytics.- Not includes mathematics details and coding.Target Audience:- Managers who are not data scientist but need to manage data analytic projects.- Managers who want to introduce data-driven management.- Anyone who are beginners in data scienceThis course covers the following topics. As you can see, the contents include fundamental concepts of data science, and basics of descriptive, diagnostic, and predictive analytics. This course also covers the very basics of deep learning. In the final two chapters, you can gain a basic but essential and robust understanding of artificial neural networks.I hope you enjoy this course.Contents:- Data Literacy and DIKW- Data-Driven Decision Making- Exploratory Data Analysis: Probability theory, Descriptive Statistics- Data Preprocessing- Data Visualization- Diagnostic Analytics: Hypothesis Testing (Theory and Methods)- Predictive Analytics: Machine Learning, Deep Learning

Overview

Section 1: Introduction

Lecture 1 Why Do We Need Data Literacy?

Lecture 2 What is Data Science?

Lecture 3 Data Science Workflow

Lecture 4 Data Type

Lecture 5 DIKW Pyramid

Section 2: Analytics for Decision Making

Lecture 6 Data-Driven Decision Making

Lecture 7 Business Analytics

Lecture 8 Machine Learning

Section 3: Exploratory Data Analysis Part 1

Lecture 9 What is EDA?

Lecture 10 Stevens' Typology

Lecture 11 Univariate Analysis

Lecture 12 Multivariate Analysis

Section 4: Exploratory Data Analysis Part 2

Lecture 13 Probability Basics

Lecture 14 Conditional Probability

Lecture 15 Bayes Theorem

Section 5: Data Preprocessing

Lecture 16 Data Cleaning

Lecture 17 Handling Missing Data

Lecture 18 Data Transformation

Lecture 19 Data Reduction

Section 6: Data Visualization

Lecture 20 Data Visualization for Univariate Analysis Part 1

Lecture 21 Data Visualization for Univariate Analysis Part 2

Lecture 22 Data Visualization for Univariate Analysis Part 3

Lecture 23 Data Visualization for Bivariate Analysis

Lecture 24 Data Visualization for Higher Dimensions

Section 7: Diagnostic Analytics Part 1

Lecture 25 Statistical Hypothesis Testing

Lecture 26 Probability Distribution

Lecture 27 Law of Large Numbers and Central Limit Theorem

Lecture 28 Hypothesis Testing Part 1

Lecture 29 Hypothesis Testing Part 2

Section 8: Diagnostic Analytics Part 2

Lecture 30 t-test

Lecture 31 Two-sample t-test

Lecture 32 Chi-Squared Test

Section 9: Diagnostic Analytics Part 3

Lecture 33 Correlation

Lecture 34 Regression

Lecture 35 Hypothesis Testing by Correlation and Hypothesis

Lecture 36 Multiple Regression Analysis

Section 10: Predictive Analytics Part 1

Lecture 37 Types of Machine Learning

Lecture 38 Regression

Lecture 39 Performance Metrics of Regression Models Part 1

Lecture 40 Performance Metrics of Regression Models Part 2

Section 11: Predictive Analytics Part 2

Lecture 41 What is Classification?

Lecture 42 Logistic Regression

Lecture 43 Decision Tree

Lecture 44 Ensemble Learning

Lecture 45 Performance Metrics of Classification Models Part 1

Lecture 46 Performance Metrics of Classification Models Part 2

Section 12: Cluster Analysis

Lecture 47 What is Clustering?

Lecture 48 Distance-Based Clustering

Lecture 49 K-Means Clustering

Lecture 50 Example: Customer Segmentation

Section 13: Deep Learning Part 1

Lecture 51 What is Deep Learning?

Lecture 52 Perceptron

Lecture 53 Multilayer Perceptron

Lecture 54 Artiricial Neural Network

Section 14: Deep Learning Part 2

Lecture 55 How to Train a Neural Network

Lecture 56 Optimization

Lecture 57 Regularization Part 1

Lecture 58 Reguralization Part 2

Anyone who want to learn data science,Managers who implement data-driven management