Page Index - sj50179/IBM-Data-Science-Professional-Certificate GitHub Wiki

155 page(s) in this GitHub Wiki:

• Home
• Welcome to my study note wiki for theIBM Data Science Professional Certificate
• My Certificate URL
• 1. What is Data Science?
• 2. Tools for Data Science
• 3. Data Science Methodology
• 4. Python for Data Science, AI & Development
• 5. Python Project for Data Science
• 6a. Data Engineering Foundations Specialization - Introduction to Relational Databases (RDBMS)
• 6. Databases and SQL for Data Science with Python
• 7. Data Analysis with Python
• 8. Data Visualization with Python
• 9. Machine Learning with Python
• 10. Applied Data Science Capstone
• 1.1.1.Defining Data Science
• 1.1.1.Quiz
• 1.1.2.Quiz
• 1.1.2.What Do Data Scientists Do?
• 1.2.1.Big Data and Data Mining
• 1.2.1.Quiz
• 1.2.2.Deep Learning and Machine Learning
• 1.2.2.Quiz
• 1.3.1.Data Science in Business
• 1.3.1.Quiz
• 1.3.1.Reading : The Final Deliverable
• 1.3.2.Careers and Recruiting in Data Science
• 1.3.3.Quiz
• 1.3.3.The Report Structure
• 10.1.Introduction
• 10.2.Exploratory Data Analysis (EDA)
• 10.3.Interactive Visual Analytics and Dashboard
• 10.4.Predictive Analysis (Classification)
• 10.5.Present Your Data Driven Insights
• 2.1.2.1.Data Science Tools_Open Source Tools
• 2.1.2.2.Data Science Tools_Commercial Tools
• 2.1.2.3.Data Science Tools_Cloud Based Tools
• 2.1.2.Quiz
• 2.1.3.1.Libraries for Data Science
• 2.1.3.2.Application Programming Interfaces
• 2.1.3.3.Data Sets & Sharing Enterprise Data
• 2.1.3.4.Machine Learning Models
• 2.1.3.5.The Model Asset Exchange
• 2.1.3.Quiz
• 2.1.Graded Quiz
• 2.2.1.Jupyter Notebook and Jupyter Lab
• 2.2.1.Quiz
• 2.2.Graded Quiz
• 2.3.Graded Quiz
• 3.1.2.Lab : From Requirements to Collection
• 3.1.2.Quiz_From Requirements to Collection
• 3.1.From Problem to Approach and From Requirements to Collection
• 3.1.Lab_From Problem to Approach
• 3.1.Quiz_From Problem to Approach
• 3.2.1.Lab : From Understanding to Preparation
• 3.2.1.Quiz_From Understanding to Preparation
• 3.2.2.Lab : From Modeling to Evaluation
• 3.2.2.Quiz_From Modeling to Evaluation
• 3.2.From Understanding to Preparation
• 3.3.From Deployment to Feedback
• 3.3.Quiz_From Deployment to Feedback
• 4.1.1.Quiz Expressions&Variables
• 4.1.2.Quiz String Operations
• 4.1.Python Basics
• 4.2.1.Graded Quiz Lists&Tuples
• 4.2.1.Quiz
• 4.2.2.Graded Quiz Dictionaries
• 4.2.3.Graded Quiz Sets
• 4.2.Python Data Structures
• 4.3.1.Graded Quiz Conditions and Branching
• 4.3.2.Graded Quiz Loops
• 4.3.3.Graded Quiz Functions
• 4.3.4.Graded Quiz Exception Handling
• 4.3.5.Graded Quiz: Objects and Classes
• 4.3.Python Programming Fundamentals
• 4.4.1.PracticeQuiz
• 4.4.2.Graded Quiz Reading and Writing files with Open
• 4.4.3.Practice Quiz Pandas
• 4.4.4.Graded Quiz: Pandas
• 4.4.5.Practice Quiz Numpy
• 4.4.6.Graded Quiz Numpy in Python
• 4.4.Working with Data in Python
• 4.5.1.Simple APIs
• 4.5.2.REST APIs & HTTP Requests
• 4.5.3.Webscraping
• 4.5.4.Working with different file formats
• 4.5.5.Practice Quiz & Graded Quiz: REST APIs, Webscraping, and Working with Files
• 4.5.APIs, and Data Collection
• 5.1.Crowdsourcing Short squeeze Dashboard
• 5.2.Peer graded Assignment Analyzing Historical Stock Revenue Data and Building a Dashboard
• 6.1.1.Basic SQL
• 6.1.Getting Started with SQL
• 6.2.Introduction to Relational Databases and Tables
• 6.3.Intermediate SQL
• 6.4.Accessing Databases using Python
• 6.5.Course Assignment
• 6.6.Bonus Module: Advanced SQL for Data Engineer
• 7.1.1.Importing Datasets
• 7.1.2.Lab 1: Importing Datasets
• 7.1.Importing Datasets
• 7.2.1.Data Wrangling
• 7.2.Data Wrangling
• 7.3.1.Exploratory Data Analysis
• 7.3.Exploratory Data Analysis
• 7.4.1.Model Development
• 7.4.Model Development
• 7.5.1.Model Evaluation and Refinement
• 7.5.2.Lab 5: Model Evaluation and Refinement
• 7.5.Model Evaluation
• 7.6.1.Final Assignment
• 7.6.2.Final Exam
• 7.6.Final Assignment
• 8.1.1.Introduction to Data Visualization
• 8.1.Introduction to Data Visualization Tools
• 8.2.1.Visualization Tools
• 8.2.Basic and Specialized Visualization Tools
• 8.3.1.Advanced Visualization Tools & Visualizing Geospatial Data
• 8.3.Advanced Visualizations and Geospatial Data
• 8.4.1.Creating Dashboards with Plotly and Dash
• 8.4.3.Dash basics: HTML and core components
• 8.4.4.Add interactivity: user input and callbacks
• 8.4.5.Flight Delay Time Statistics Dashboard
• 8.4.Creating Dashboards with Plotly and Dash
• 8.5.Final Project & Exam
• 9.1.Introduction to Machine Learning
• 9.2.1.Linear Regression
• 9.2.2.Non Linear Regression
• 9.2.Regression
• 9.3.1.K Nearest Neighbors
• 9.3.2.Decision Trees
• 9.3.3.Logistic Regression
• 9.3.4.Support Vector Machine
• 9.3.Classification
• 9.4.1.k Means Clustering
• 9.4.2.Hierarchical Clustering
• 9.4.3.Density based Clustering
• 9.4.Clustering
• 9.5.1.Content based Recommendation Engines
• 9.5.2.Collaborative Filtering
• 9.5.Recommender Systems
• 9.6.1.Final Exam
• 9.6.Final Project
• DEFS.4.0.Course Introduction
• DEFS.4.1.1.Fundamental Relational Database Concepts
• DEFS.4.1.2.Introducing Relational Database Products
• DEFS.4.1.Relational Database Concepts
• DEFS.4.2.1.Creating Tables and Loading Data
• DEFS.4.2.2.Designing Keys, Indexes, and Constraints
• DEFS.4.2.Using Relational Databases
• DEFS.4.3.1.MySQL
• DEFS.4.3.2.PostgreSQL
• DEFS.4.3.MySQL and PostgreSQL
• DEFS.4.4.1.Assignment
• DEFS.4.4.2.Final Quiz
• DEFS.4.4.Course Assignment
• IBM Data Science Professional Certificate