Part 1
Introduction to Data Science Methods
Numpy and Pandas for Data Science

Part 2
Introduction to Linear Algebra
Introduction to Statistics

Part 3
Introduction to Modeling
Hyperparameters

Part 4
Model Validation
Feature Engineering

Part 5
Naive Bayes Classification
Gradient Descent

Part 6
Linear Regressions
Logistic Regression

Part 7
Support Vector Machines

Part 8
Decision Trees and Random Forests

Part 9
k-Means Clustering
Principal Component Analysis

Part 10
Current Applications of Data Science and Machine Learning

Part 1
Installation
Variables
Expressions
Statements

Part 2
Conditional Execution (if, elif, else)

Part 3
Loops
Nested Loops

Part 4
Functions
Strings

Part 5
Lists
Tuples

Part 6
Dictionaries
Files

Part 7
Pandas

Part 8
Numpy
Matplotlib

Part 1 – Understanding SQL
What is Data?
What is Database?
What is SQL?
Relational Databases
Data Types
SMSS

Part 2 – Gathering Data
Select * From
One Column
Multi Columns
All Columns
One Row
Limited Rows
Alias

Part 3 – Ordering
ASC
DESC
Multi Order

Part 4 – Filtering
WHERE
Wildcards
Between
IN
Like
Exists
is NULL
NOT
1=0

Part 5 – Manipulation
Calculations
CONCAT
Functions
Dates
Numeric
CAST
CONVERT

Part 6 – Aggregate
Functions
DISTINCT
Grouping

Part 7 – Subquery
FROM
WHERE
SELECT

Part 8 – Joins
Why Joins?
INNER
LEFT
RIGHT
FULL
CROSS
UNION
UNION ALL

Part 9 – DML
Temp
Create
INTO
INSERT INTO
UPDATE
DELETE
ALTER

Part 10 – Extras
IF ELSE
Case When
While
IDENTITY
RANK
Datefirst
Parameters

Part 11 – Misc
Primary Key
Foreign Key
INDEX
Views
SP
Performance

Learning Mathematical Programming
Part 1 – The big picture
Part 2 – Introduction to Linear programming
Part 3 – Beyond simple LP
Part 4 – Modeling practice

Optimization Modeling with IBM ILOG OPL
Part 1 – Introduction to optimization with IBM ILOG OPL
Part 2 – Working with the OPL language
Part 3 – Working with IBM ILOG Script: basic tasks
Part 4 – Solving simple MP problems
Part 5 – Linking to spreadsheets and databases with OPL
Part 6 – Integer and mixed integer programming