Learn to Build Machine Learning Machine Using SAS + Python + SQL

Methodology

Duration : 4 - 6 months - Price : ₹5900.00 - Place : Delhi (110000)Course Curriculum

Course 1: Machine Learning with SAS Programming

Learning objectives - At the end of this course, you should be able to:

• Build programs using SAS language
• Perform data analysis using SAS language
• Visualize data using SAS language
• Perform statistical analysis using SAS language
• Apply machine learning algorithms using SAS language

A bird’s eye view of the topics to be discussed in the course is as under:

1. Introduction
• Introduction
• Installations and setup
• Getting the course material

2. Basic Concept of SAS program
• Structure of SAS program
• Processing of SAS program
• libraries (Permanent / Temporary)
• Referencing a file
• Data sets
• Variables and observations
• Intro to SAS Programming interface
• Coding exercise

3. Working with Data Steps
• Reading raw data using datalines
• Referencing library / data file
• Defining variables
• Reading external data using data step
• Reading data sets
• Coding exercise

4. Creating and Applying user-defined formats and informats
• Informats
• Formats
• Creating your own formats
• Formatting Datetime Data
• Using Yearcutoff
• Coding exercise

5. Understanding Data Step processing
• Program Data Vector (PDV)
• Compilation phase
• Execution phase
• Debugging a Data Step
• How data sets are read?
• Coding exercise

6. Data Manipulation
• Creating and modifying user defined variables
• Using conditional statements
• Subsetting Data
• Assigning length and labels
• Assigning values conditionally
• By-group processing
• Reading observations in the order of your choice
• Setting end of data set using END option
• Renaming variables
• Selecting variables
• Coding exercise

7. Combining or Merging Data
• One-to-one reading
• Concatenation
• Interleaving
• Match-merging
• Excluding Unmatched Data
• Coding exercise

8. Transforming Data with Functions
• What are functions?
• Converting data type using functions
• Character functions
• Numeric functions
• Datetime functions
• Nesting one or more functions
• Coding exercise

9. Frequently used procedures
• Print procedure
• Import and Export procedures
• Sort procedure
• Append procedure
• Datasets procedure
• Contents procedure
• Transpose procedure
• Tabulate procedure
• Report procedure
• Means procedure
• Summary procedure
• Freq procedure
• Coding exercise

10. Generating Data with DO loops
• Introduction to DO loops
• Working of DO loops
• Iterative reading of data
• Conditional execution of DO loops
• Coding exercise

11. Processing variables with arrays
• Understanding arrays
• Creating one-dimensional arrays
• Variable list in array elements
• Referencing element of an array
• Compilation and Execution
• Creating variables in an array statement
• Creating temporary array elements
• Coding exercise

12. PROC SQL
• Selecting Data
• Setting conditions
• Grouping Data
• Sorting Data
• Creating user-defined variables from existing variables
• Merging tables
• Using sub-queries
• Coding exercise

13. Introduction to Macros
• Introduction
• Macro variables
• Built-in macro variables
• Assigning values to macro variables
• Simple macros
• Tokens
• Using macro variable as a prefix
• Using macro variable to transfer a value between Data steps
• Coding exercise

14. Statistical Application
• Descriptive statistics
• Inferential statistics
• Correlation
• Simple Regression
• Multiple Regression
• Logistic Regression
• Cluster Analysis

15. Machine Learning Projects
• Regression
• Logistic Regression
• Time Series
• Random Forest
• Neural Network
 
Course 2: Introduction to SQL with MySQL

Learning objectives - At the end of this course, you should be able to:

• Create and modify databases and tables using MySQL
• Extract data from the tables as per your requirement
• Create new variables using existing variables
• Use conditions and functions while extracting data
• Perform different types of joins on the tables

A bird’s eye view of the topics to be discussed in the course is as under:

1. Introduction to SQL
• Introduction
• Curriculum walkthrough
• What is a database?
• SQL Vs MySQL
• Accessing GoormIDE
• Coding Exercise

2. Working with Databases and Tables
• Creating databases and tables
• Inserting data
• Reading or extracting data
• Updating data
• Deleting data
• Coding Exercise

3. More on Data Extraction
• Improving selections
• Using aggregate functions
• Defining and converting data types
• Using logical operators
• Coding Exercise

4. Joining or merging tables
• Inner join
• Left Join
• Right join
• Working with foreign keys
• Coding Exercise
 
Course 3: Machine Learning with Python

Learning objectives - At the end of this course, you should be able to:

• Build programs in python
• Perform data analysis in python
• Perform statistical analysis in python
• Visualize data in python
• Apply machine learning algorithms python

A bird’s eye view of the topics to be discussed in the course is as under:

1. Introduction
• Course overview
• Installation of python

2. Basic concepts of python
• Introduction to data types
• Numbers and variable assignment
• Strings
• Indexing and slicing
• Strip methods
• Printing strings
• Lists
• Dictionaries
• Tuples & Sets
• Booleans
• Reading a basic external file
• Coding Exercise

3. Comparison operators & Logical Statements
• Comparison operators
• Chaining comparison operators
• If-elif statements
• Other useful operators
• Coding Exercise

4. Python statements
• For loops
• While loops
• List comprehension
• Coding Exercise

5. Methods and Functions
• Methods
• Functions
• Coding Exercise

6. Object Oriented Programming
• Introduction
• Attributes
• Classes
• Inheritance
• Coding Exercise

7. Data Analysis in python
• Numpy
• Coding Exercise
• Pandas
• Coding Exercise

8. Data visualization in python
• Using Matplotlib
• Coding Exercise
• Using Seaborn
• Coding Exercise
• Using Plotly
• Coding Exercise
• Using Cufflinks
• Coding Exercise
• Geographical plotting
• Coding Exercise

9. Application of Statistical topics
• Linear Regression
• Logistic Regression
• Cluster Analysis
• K-Nearest Neighbors
• Decision Trees and Random Forests
• Support Vector Machine

10. Machine Learning Projects
• Regression
• Logistic Regression
• K-Nearest Neighbors
• Support Vector Machine
• Neural Networks