1. Intro to Data & Data Science
1.1 The different data science fields
1.2 The relationship between different data science fields
1.3 What is the purpose of each data science field
1.4 Common data science techniques
1.5 Common data science tools
1.6 Data science career paths
1.7 Dispelling common Misconceptions
2. Introduction to Microsoft Excel
2.1 Welcome! Course Introduction
2.2 Microsoft Excel - Useful Tools & Tips
2.3 Microsoft Excel - Beginner, Intermediate & Advanced Functions
2.4 Microsoft Excel - Building Professional Charts in Excel
2.5 Introduction to Pivot tables
2.6 A practical case study with Pivot Tables
3. Probability
3.1 The Basics of Probability
3.2 Combinatorics
3.3 Bayesian Inference
3.4 Probability Distributions
3.5 Probability in Other Fields
4. Statistics
4.1 Introduction
4.2 Descriptive Statistics Fundamentals
4.3 Practical Example - Descriptive Statistics
4.4 Inferential Statistics Fundamentals
4.5 Confidence Intervals
4.6 Practical Example - Confidence Intervals
4.7 Hypothesis testing
4.8. Practical Example - Hypothesis testing
5. Mathematics
6.1 Introduction to Linear Algebra
6. Tableau
6.1 Introduction to Tableau
6.2 Tableau - Tableau Functionalities
6.3 Tableau - The Tableau Exercise
7. PowerBl
7.1 What Does the Course Cover_
7.2 Power BI Overview
7.3 Power BI Setup
7.4 Connecting to Data Sources
7.5 Practical Task Three
7.6 Data Modelling
7.7 Practical Task Four
7.8 Creating Our First Data Visualization Re
7.9 Practical Task Five
7.10 Practical Task Six, Final Project
7.11 Bonus Lectures
8. SQL
8.1 Introduction to Databases, SQL, and MySQL
8.2 SQL Theory
8.3 Basic Database Terminology
8.4 Installing MySQL and Getting Acquainted with the Interface
8.5 First Steps in SQL
8.6 MySQL Constraints
8.7 SQL Best Practices
8.8 Loading the Data
8.9 SQL SELECT STATEMENT
8.10 SQL INSERT Statement
8.11 SQL UPDATE Statement
8.12 SQL DELETE Statement
8.13 MySQL Aggregate Functions
8.14 SQL JOINs
8.15 SQL Subqueries
8.16 SQL Self Join
8.17 SQL Views
8.18 Stored Routines
8.19 Advanced SQL Topics