The training is for Professional Development purposes, and the certificate obtained does not replace the MYK authorization certificate in training where the MYK authorization certificate is required.

Course Scope: This course covers the skills you need to become a BI Analyst (Statistics, Database theory, SQL, Tableau, Python).

Requirements:

You will need to install MySQL, Tableau Public, and Anaconda. We will guide you step by step on how to do this. Microsoft Excel 2003, 2010, 2013, 2016, or 365

Training Program:

DAY 1

Introduction

• Introduction to Data and Data Science
• Different Data Science Areas
• Introduction to Business Analytics, Data Analytics, and Data Science

DAY 2

Business Analytics

• Business Analytics
• Data Analysis
• Data Science
• Introduction to Business Intelligence, Machine Learning, and Artificial Intelligence

DAY 3

Data and Data Science

• Traditional Data
• Big Data
• Traditional Data Science
• Machine Learning
• Data Science Areas and Discrete Lines

DAY 4

Common Data Science Techniques

• Traditional Data
• Real-Life Data Examples
• Big Data
• Big Data Technologies
• Traditional Methods
• Machine Learning Techniques

DAY 5

Programming Languages and Common Data Science Tools

• Data Science Career Paths
• Statistics - Population and Sample
• Descriptive Statistics

DAY 6

Basics of Inferential Statistics

• Distribution
• What is Distribution?
• Normal Distribution
• Standard Normal Distribution
• Central Limit Theorem
• Standard Error
• Estimators and Predictions
• Inferential Statistics: Confidence Intervals
• What are Confidence Intervals?
• Confidence Intervals; Known Population Variance; Z-score
• Confidence Interval Explanations
• Student's T Distribution
• Confidence Intervals; Population Variance Unknown; T-score
• Margin of Error
• Confidence Intervals. Two meanings. Dependent
• Confidence Intervals. Two meanings. Independent samples

DAY 7

Statistics - Hypothesis Testing

• Null and Alternative Hypothesis
• More about Null and Alternative Hypothesis
• Null and Alternative Hypothesis
• Rejection Region and Significance Level
• Type I Error and Type II Error
• Test the Mean. Known Population Variance
• P-value
• Test the Mean. Unknown Population Variance
• Test the Mean. Dependent Samples
• Examples

DAY 8

Relational Database Theory and Introduction to SQL

• Why should I use SQL?
• Why should I use MySQL?
• Introduction to Databases
• Basics of Relational Databases
• Comparing Databases and Spreadsheets
• Key Database Terminologies
• Relational Schema Concepts: Primary Key
• Relational Schema Concepts: Foreign Key
• Relational Schema Concepts: Unique Key and Null Values
• Relational Schema Concepts: Relationships Between Tables
• SQL
• Installing MySQL
• Creating a Database
• Creating a Table
• Select, Delete, Import, Update Queries
• Functions
• Joins
• Subqueries
• Stored Routines
• Common Table Expressions (CTEs)

DAY 9

Table

• Introduction to Tables
• Loading
• Functionalities
• Combining SQL and Tableau

DAY 10

Python

• Python Syntax and Variables
• Python Libraries
• Operators
• Conditional Statements
• Functions

DAY 11

Python Arrays

• Lists
• Methods
• Tuples
• Dictionaries