JDS Academy - course material #2

JDS Academy - course material #2

Supplementary material: Cheat Sheets

• SQL,
• Python,
• Bash

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Additional JDS course material:
table of content of A/B testing.

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WEEK 0

Installation of a Linux webserver  on Linode cloud + I made a Linux workstation, both with Ubuntu.

WEEK 1

# Module 1: Introduction to Data Science

• What is Data Science? Case study.
• Clarification of AI, ML, big data, deep learning concepts.

# Module 2: How to become a Data Scientist

• Soft skills, mindset, time commitment, roadmap, learning curve.

# Module 3: SQL Introduction - European accidents dataset

• Introduction to SQL Workbench and pgAdmin, installation.
• SQL server configuration
• Importing data into SQL, data analysis.

# Module 4: SQL Basics + Simple Queries

• Basic SQL exercises.

# Module 5: SQL WHERE with Multiple Filter Conditions - European accidents dataset

• Advanced filtering techniques.

# Module 6: SQL WHERE + ORDER BY - European accidents dataset

• Combination of sorting and filtering.

# Module 7: SQL Functions (COUNT, SUM, AVG, MIN, MAX) - European accidents dataset

• Basic Aggregation Functions.

WEEK 2

# Module 1: SQL Functions + GROUP BY

• Grouping and Aggregation.

# Module 2: SQL Table Joining (JOIN)

• Table Joining Techniques.

# Module 3: SQL Subqueries + HAVING

• Nested Queries and Conditional Filtering.

# Module 4: Extra Task (Case Study) - Mobile App User and Business Data Analysis

• Data-Driven Tasks.

# Module 5: Data Analysis Methodologies

# Vault: SQL Exercises

• Interview-Preparing SQL Tasks.
• A/B test data, 
• Solar panel factory production data analysis, 
• Travel blog User and business analysis.

WEEK 3

# 0. Module: Setting up your own server

• Installing and configuring a database server.
  Note: I have done on the 0th week. Linode webserver + Linux workstation, Ubuntu

# 1. Module: Bash Basics

• Basics of Bash commands and scripts. ETL with bash

# 2. Module: Bash Basics continued

• Additional practical tasks. ETL with bash

# 3. Module: Scripts and automation in Bash

• Automation techniques.

# 4. Module: Data collection

WEEK 4

# 1. Module: Python Introduction, Variables, Data structures

• Jupyter Notebook management, variables and structures.

# 2. Module: Python Functions, If Branches, For Loops

• Basic Python programming techniques.

# Module 3: Python Practice

• Various python practice tasks, basic operations/logical functions.

# Module 4: Statistics

WEEK 5

# Module 1: Python + Analytics: Pandas basics

• Data Management with Pandas.

# Module 2: Pandas GroupBy, Functions, Sorting

• Advanced data management techniques.

# Module 3: Data Visualization with Python + Pandas

• Creating graphs and visualizations.

# Module 4: Predictive Analytics

WEEK 6

# Module 1: Machine Learning Examples in Python

• Linear and Polynomial Regression, 
• Random Forest, 
• Deep Learning.

# Module 2: Data Presentation

JDS Course material (table of content) of A/B testing.