ND111 - Data Science II - Nanodegree Foundations

Tags

• Author : AH Uyekita
• Dedication : 10 hours/week (suggested)
• Start : 14/12/2018
• End (Planned): 28/12/2018
• Title : Data Science II - Foundations Nanodegree Program
  • COD : ND111
  • Certificate: KE5NTMFH

Related Courses

• ND110 - Data Science I - Nanodegree Foundations

Bookdown and Projects

• Bookdown 📖
• Project 01 🚀
• Project 02 🚀
• Project 03 🚀
• Project 04 🚀

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Objectives

I want to finish this course in two weeks. It includes the Optional videos and chapters.

Syllabus

• Chapter 01 - Welcome
  • Lesson 01 - Instructions
  • Lesson 02 - Tips
• Chapter 02 - SQL for Data Analysis
  • Lesson 01 - Basic SQL
  • Lesson 02 - SQL Joins
  • Lesson 03 - SQL Aggregations
  • Lesson 04 - (Optional) SQL Subqueries & Temporary Tables (Advanced)
  • Lesson 05 - (Optional) SQL Data Cleaning (Advanced)
  • Project 01 - Query a Digital Music Store Database
• Chapter 03 - Data Wrangling
  • Lesson 01 - Introduction to Data Wrangling
  • Lesson 02 - Gathering
  • Lesson 03 - Assessing Data
  • Lesson 04 - Cleaning Data
  • Project 02 - Wrangle and Analyze Data
• Chapter 04 - Advanced Statistics
  • Lesson 01 - Descriptive Statistics - Part 1
  • Lesson 02 - Descriptive Statistics - Part 2
  • Lesson 03 - Admissions Case Study
  • Lesson 04 - Probability
  • Lesson 05 - Binomial Distribution
  • Lesson 06 - Conditional Probability
  • Lesson 07 - Bayes Rule
  • Lesson 08 - Python Probability Practice
  • Lesson 09 - Normal Distribution Theory
  • Lesson 10 - Sampling Distributions and the Central Limit Theorem
  • Lesson 11 - Confidence Intervals
  • Lesson 12 - Hypothesis Testing
  • Lesson 13 - Case Study: A/B Tests
  • Lesson 14 - Regression
  • Lesson 15 - Multiple Linear Regression
  • Lesson 16 - Logistic Regression
  • Project 03 - Analyze A/B Test Results
• Chapter 05 - Intro to Machine Learning
  • Lesson 01 - Welcome to Machine Learning
  • Lesson 02 - Naive Bayes
  • Lesson 03 - SVM
  • Lesson 04 - Decision Trees
  • Lesson 05 - Choose Your Own Algorithm
  • Lesson 06 - Datasets and Questions
  • Lesson 07 - Regressions
  • Lesson 08 - Outliers
  • Lesson 09 - Clustering
  • Lesson 10 - Feature Scaling
  • Lesson 11 - Text Learning
  • Lesson 12 - Feature Selection
  • Lesson 13 - PCA
  • Lesson 14 - Validation
  • Lesson 15 - Evaluation Metrics
  • Lesson 16 - Tying It All Together
  • Project 04 - Identify Fraud from Enron Email
• Chapter 06 - (Optional) Data Visualization
  • Lesson 01 - Introduction to Data Visualization
  • Lesson 02 - Design
  • Lesson 03 - Data Visualization in Tableau
  • Lesson 04 - Making Dashboard & Stories in Tableau

Repository Structure

This is the structure of this repository, each course's chapters (or parts) will be stored in different folders.

ND111_data_science_foundation_02
|
+--  01-Chapter_01
|           |
|           +--  README.md                       # General information
|
+--  02-Chapter_02
|           |
|           +--  README.md                       # General information
|           +--  00-Project_01                   # Project 01
|           +--  01-Lesson_01                    # Files from Lesson 01
|           |        +--  README.md              # Notes from Lesson 01 from Chapter 02
|           +--  02-Lesson_02                    # Files from Lesson 02
|           |        +--  README.md              # Notes from Lesson 02 from Chapter 02
|           .
|
+--  03-Chapter_03
|           |
|           +--  README.md                       # General information
|           +--  00-Project_02                   # Project 02
|           +--  01-Lesson_01                    # Files from Lesson 01
|           |        +--  README.md              # Notes from Lesson 01 from Chapter 02
|           +--  02-Lesson_02                    # Files from Lesson 02
|           |        +--  README.md              # Notes from Lesson 02 from Chapter 02
|           .

Best practice

• Add all deliverables in the GitKraken Glo;
• Take notes using the Markdown.