Data Science 101: Methodology, Python, and Essential Math

Course Curriculum

Chapter 1: Intro to Data Science 101

Lecture 1: Matching Activity – Match the Project to the Data Role

Lecture 2: Intro to Data Science

Lecture 3: What a Data Scientist Does?

Lecture 4: Big Data

Lecture 5: Data Mining

Lecture 6: Machine Learning vs. Deep Learning

Lecture 7: Advice to Data Scientists

Chapter 2: Best Language for Data Science?

Lecture 1: What IS the best language for Data Science?

Lecture 2: Python

Lecture 3: SAS

Lecture 4: R

Lecture 5: SQL

Chapter 3: Data Science Methodology

Lecture 1: Data Science Methodology/Process Intro

Lecture 2: Business Understanding

Lecture 3: Data Understanding

Lecture 4: Data Prep

Lecture 5: Modeling

Lecture 6: Evaluation

Lecture 7: Deployment

Chapter 4: Data Science Via Chatbot

Lecture 1: Purpose of Chatbot Section

Lecture 2: What is a Chatbot?

Lecture 3: Signing up for Watson Assistant

Lecture 4: Creating a name – Healthcare Service Chatbot

Lecture 5: Intents

Lecture 6: Entities

Lecture 7: Suggestions for More Learning

Lecture 8: Section Recap: Natural Language Processing , Machine Learning, and Use Cases

Chapter 5: Libraries, API's, Datasets

Lecture 1: Libraries

Lecture 2: API's

Lecture 3: Datasets

Chapter 6: Github

Lecture 1: Intro to Github

Lecture 2: Create a Repository

Lecture 3: Creating Branch and Commit Changes

Lecture 4: Pull Request and Merging Pull Request

Chapter 7: Introduction to Data Science in Python

Lecture 1: Welcome to the Python for Data Science and Machine Learning Section

Lecture 2: Options/Features When Watching Videos

Lecture 3: Resources (Data-sets and Notebooks)

Chapter 8: Installation/Jupyter/Comments (Windows and MacOS/Jupyter Notebook)

Lecture 1: Windows – Download Anaconda Distribution (includes Python!)

Lecture 2: Windows – Install Anaconda Distribution

Lecture 3: Windows – Setting Up Environment

Lecture 4: Windows – Opening Jupyter Notebook

Lecture 5: MacOS – Anaconda Download and Install

Lecture 6: MacOS – Conda Environment

Lecture 7: MacOS – Jupyter Notebook

Lecture 8: Jupyter Notebook Interface and Shortcuts

Chapter 9: Introduction to Data Science in Python – Python Fundamentals

Lecture 1: How to Use Markdown Cells (Adding Headers, Links, and Images)

Lecture 2: Comments – Inline and Block Comments

Lecture 3: Python Indentation

Lecture 4: Writing Single and Multiple Lines of Code

Lecture 5: Understanding Variables

Lecture 6: Main Data Types and Creating Them (Integer, Float, String, List, Dictionary)

Lecture 7: Lists – How To Use

Lecture 8: Dictionaries – How To Use

Lecture 9: Creating A Tuple

Lecture 10: Tuple – How To Use

Lecture 11: Creating a Set

Lecture 12: Set – How To Use

Lecture 13: Operators

Lecture 14: Fill in Activity 1 – Fundamentals

Chapter 10: Introduction to Data Science in Python – Decision and Looping Structures

Lecture 1: Introducing Decision and Looping Structures

Lecture 2: If statement

Lecture 3: Else Statement

Lecture 4: Elif

Lecture 5: For Loop

Lecture 6: While Loop

Lecture 7: Break and Continue Statements

Chapter 11: Introduction to Data Science in Python – Python Functions

Lecture 1: Introducing Functions

Lecture 2: Functions – General Syntax

Lecture 3: +1 Function

Lecture 4: Fav Band Function

Lecture 5: Celsius to Fahrenheit Function

Lecture 6: Optional Return Statement (and comparing it to Print Statement)

Lecture 7: Defining a Function vs. Calling a Function (including different ways to call)

Lecture 8: Practical/Real World Example: Function to Get Reddit Data

Lecture 9: Lambda Intro (Anonymous Functions)

Lecture 10: Formal Function vs. Lambda for splitting strings

Lecture 11: Fill in Activity 2 – Looping and Functions

Chapter 12: Introduction to Data Science – Nested Data, Iteration and List Comprehension

Lecture 1: Introducing you to Nested Data and Iteration

Lecture 2: Simple Nested Example

Lecture 3: Double Indexing

Lecture 4: Assigning Values

Lecture 5: List of Dicts and Dicts of Dicts Example

Lecture 6: Nested Iteration – Iterating through List of Lists

Lecture 7: Defining List Comprehension and Syntax