From data science methodology to an introduction to data science in Python, to essential math for data science
About This Video
In Detail
The opening part …
From data science methodology to an introduction to data science in Python, to essential math for data science
About This Video
In Detail
The opening part of Data Science 101 examines some frequently asked questions.
Following that, we will explore data science methodology with a case study. You will see the typical data science steps and techniques utilized by data professionals. Next, you will build a simple chatbot so you can get a clear sense of what is involved.
The next part is an introduction to data science in Python. You will have an opportunity to master Python for data science as each section is followed by an assignment to practice your skills. By the end of the section, you will understand Python fundamentals, decision and looping structures, Python functions, how to work with nested data, and list comprehension. Finally, we will wrap up the two most popular libraries for data science—NumPy and Pandas.
The last part delves into essential math for data science. You will get the hang of linear algebra along with probability and statistics. Our goal for the linear algebra part is to introduce all necessary concepts and intuition for an in-depth understanding of an often-utilized technique for data fitting called least squares. We will spend a lot of time on probability, both classical and Bayesian, as reasoning about problems is a much more difficult aspect than simply running statistics.
By the end of this course, you will understand data science methodology and how to use essential math in your real projects.
Audience
This course is designed for people who are new to data science or who are interested in pursuing a career in data science, as well as those who wish to obtain a broad overview before diving into specialized data science topics.
This course will also benefit students who want to master the fundamental arithmetic for data science or obtain an introduction to data science in Python.
You need not have any prior experience in data science to take up this course.
Chapter 1 : Introduction to Data Science 101
Matching Activity - Match the Project to the Data Role
Introduction to Data Science
What a Data Scientist Does
Big Data
Data Mining
Machine Learning Versus Deep Learning
Advice to Data Scientists
Chapter 2 : Best Language for Data Science
What IS the Best Language for Data Science?
Python
SAS (Statistical Analysis System)
R
SQL
Chapter 3 : Data Science Methodology
Data Science Methodology/Process Introduction
Business Understanding
Data Understanding
Data Prep
Modelling
Evaluation
Deployment
Chapter 4 : Data Science Through Chatbot
Purpose of Chatbot Section
What is a Chatbot?
Signing Up for Watson Assistant
Creating a Name - Healthcare Service Chatbot
Intents
Entities
Suggestions for More Learning
Section Recap: Natural Language Processing, Machine Learning, and Use Cases
Chapter 5 : Libraries, APIs, Datasets
Libraries
APIs
Datasets
Chapter 6 : GitHub
Introduction to GitHub
Create a Repository
Create a Branch and Commit Changes
Pull Request and Merging Pull Request
Chapter 7 : Installation / Jupyter / Comments (Windows and MacOS/Jupyter Notebook)
Windows - Download Anaconda Distribution (Includes Python!)
Windows - Install Anaconda Distribution
Windows - Setting Up Environment
Windows - Opening Jupyter Notebook
MacOS - Anaconda Download and Install
MacOS - Conda Environment
MacOS - Jupyter Notebook
Jupyter Notebook Interface and Shortcuts
Chapter 8 : Introduction to Data Science in Python - Python Fundamentals
How to Use Markdown Cells (Adding Headers, Links, and Images)
Comments - Inline and Block Comments
Python Indentation
Writing Single and Multiple Lines of Code
Understanding Variables
Main Data Types and Creating Them (Integer, Float, String, List, Dictionary)
Lists - How to Use
Dictionaries - How to Use
Creating a Tuple
Tuple - How to Use
Creating a Set
Set - How to Use
Operators
Chapter 9 : Introduction to Data Science in Python - Decision and Looping Structures
Introducing Decision and Looping Structures
If Statement
Else Statement
Elif
For Loop
While Loop
Break and Continue Statements
Chapter 10 : Introduction to Data Science in Python - Python Functions
Introducing Functions
Functions - General Syntax
+1 Function
Fav Band Function
Celsius to Fahrenheit Function
Optional Return Statement (and Comparing It to Print Statement)
Defining a Function Versus Calling a Function
Practical/Real World Example: Function to Get Reddit Data
Lambda Introduction (Anonymous Functions)
Formal Function Versus Lambda for Splitting Strings
Chapter 11 : Introduction to Data Science - Nested Data, Iteration, and List Comprehension
Introducing you to Nested Data and Iteration
Simple Nested Example
Double Indexing
Assigning Values
List of Dicts and Dicts of Dicts Example
Nested Iteration - Iterating Through List of Lists
Defining List Comprehension and Syntax
List Comprehension - Simple Examples
List Comp as an Alternative to Loops
Practical/Real World Example - Using Common Mathematical Notation
Practical/Real World Example - Creating a Constrained ID
Activity: Building Intuition (Loops, Nested Data, Iteration, and List Comp)
Chapter 12 : Introduction to Data Science in Python - Learn NumPy
Introducing NumPy
Creating Our First NumPy Array
Shaping an Array (When You Know the Shape You Want)
Creating a Sequence of Integers and Floats
Element-Wise Operations
A Range with a Shape (Arrange Function with Reshape Function)
NumPy Indexing
NumPy Slicing
Indexing and Slicing with Breast Cancer Wisconsin Dataset
Delete Elements
Append
Insert Elements
Reshape -1 Feature
Flatten
Transpose
Concatenate
Splitting
Aggregate/Statistical Functions
Chapter 13 : Introduction to Data Science in Python - Pandas
Introducing Pandas
For SAS Programmers: Analogous Terms in Pandas (Python)
Using Series as Input into DataFrame
Comparing Series and DataFrame
Importing TSLA Dataset
Index-Based Selection (iloc)
Label-Based Selection (loc)
Conditional Selection
Summary Functions
Grouping (groupby)
Sorting
Checking Data Types and Converting
Dealing with Missing Values
Dropping Columns/Variables and Records/Rows
Renaming Columns/Variables and Records/Rows
Concat Function + Pop Quiz
Real-World Activity: Add New Columns and Predict Stock Movement
Chapter 14 : Introduction to Data Science in Python - Python Activity Solutions
Solution - Fill in Activity - Fundamentals
Solution - Fill in Activity - Looping and Functions
Solution - Fill in Activity - Nested and List Comprehension
Solution - Fill in Activity - NumPy
Chapter 15 : Essential Math for Data Science - Linear Algebra Made Easy
Linear Equation Definition
Forms of a Linear Equation
Systems of Linear Equations
Line and Plane
Aij Notation
System of Equations as a Matrix
System in Corresponding Forms
Row Echelon Form (Gaussian Elimination)
Reduced Row Echelon Form
Row Operations Rules
Row Operations Example (REF)
Visualizing Ax=b
General Formula - Matrix Vector Multiplication
Tips for Row Operations
Chapter 16 : Essential Math for Data Science - Mathematical Structures
Mathematical Structures
Abelian Groups and Fields
Vector Spaces 1
Vector Spaces - Concrete Example
Subspaces
Linear Combinations and Span
Is It in the Span?
Linear Independence
A Basis for a Vector Space
Dim of C(A) and N(A)
The Dimension of a Vector Space
Linear Maps
The Four Fundamental Subspaces
Adding Geometry to Vector Spaces
Orthogonal Projection - How to Derive Projection and Check for Orthogonality
Least Squares
Least Squares Through Pseudoinverse - with Python and SAS code
Chapter 17 : Essential Math for Data Science - Introduction to Probability
Probability Models and Axioms
Simple Counting
Discrete Example
Conditional Bayes
Conditional Example 1
Conditional Healthcare (Cancer) Example 2
Independence of Events (What It Means and Does Not Mean)
Permutations and Combinations
Chapter 18 : Essential Math for Data Science - Random Variables and Multiple Variables
Random Variables
Probability Mass Function and Discrete R.V.s
Expectation and Variance for Discrete Random Variables
Joint PMFs (Multiple Discrete Variables)
Continuous Random Variables
Continuous Random Variables and Probability Density Function
Continuous R.V. Example
Joint PDF Example - Banking
Cumulative Distribution Function (CDF)
Covariance, Correlation, and More on Variance
Law of Large Numbers (LLN)
Central Limit Theorem (CLT)
Chapter 19 : Essential Math for Data Science - Statistical Inference
Statistical Inference
Bayesian Estimator
Example - Bayesian Estimator
Mean Squared Error = Variance. Why?