• Day-1 An Introduction to Python & Basic Python Syntax
• A Brief History of Python Versions
• Installing Python
• Variables
• Local & Global Variables
• Data Types
• Dynamic Types
• Python Reserved Words
• Naming Conventions
• Your First Python Program
• How Python Code Gets Executed
• Difference between Compiler and Interpreter
• Instruction/Statement
• Basic Syntax Comments
• Receiving Input
• Type Conversion/Casting
• Numeric Data Types
• Formatted Float
• Boolean Data Types
• Swapping
• Strings
• Formatted Strings

• Day-2 Language Components & Loop
• Arithmetic Operations
• Operator Precedence
• Math Functions
• Indentation
• If Statements
• Logical Operators
• Letter Grade Program
• Comparison/Relational/Conditional Operators
• Leap Year Program
• Assignment Operators
• Ternary Operators
• Weight Converter Program
• While Loops
• Break & Continue Statement
• Sum of n Numbers Program
• Building a Guessing Game
• Building the Car Game
• For Loops
• For-While Comparison
• For with Range Function
• Nested Loops

• Day-3 List & Functions
• Lists
• Bubble Sort 
• List Methods
• Range Function in a list
• 2D Lists/Matrix
• Tuples
• Unpacking/Comparing
• Set (Union/Intersection/Difference)
• Dictionaries
• Functions
• Parameters/Arguments
• Keyword Parameters/Arguments
• Default Parameter Value
• Return Statement
• Lambda Function
• Debugging
• Exception Handling

• Day-4 Classes, Objects & Method
• Object Oriented Programing (OOP)
• Classes
• Objects
• Introducing Method
• Default Constructors
• Parameterized Constructor
• Pass Statement
• Class/Static Variable
• Instance Variable
• Intro to Inheritance
• Single Inheritance
• Hierarchical Inheritance
• Multilevel Inheritance
• Multiple Inheritance
• Method Overloading
• Method Overriding
• Encapsulation
• Polymorphism

• Day-5 Data Analysis using NumPy-Part 1
• A brief introduction
• Installation instructions.
• NumPy arrays
• Built-in methods
• Array methods and attributes.
• Indexing, slicing

• Day-6 Data Analysis using NumPy-Part 2
• Broadcasting
• Boolean masking
• Arithmetic Operations
• Universal Functions            
• Exercises Overview
• Exercises Solutions
  
• Day-7 Data Analysis using Pandas-Part 1
• A brief introduction and installation instructions.
• Pandas Introduction.
• Pandas Data Structures - Series
• Pandas Data Structures – DataFrame

• Day-8 Data Analysis using Pandas-Part 2
• Hierarchical Indexing
• Handling Missing Data
• Data Wrangling - Combining, Merging, Joining, Group by
• Useful Methods and Operations

• Day-9 Project- Data Analysis using Pandas
• Project 1
• Customer Purchases Data (Overview)
• Customer Purchases Data (Solutions)
• Project 2
• Chicago Payroll Data (Overview)
• Chicago Payroll Data (Solutions)

• Day-10 Data Visualization using Matplotlib-Part 1
• Basic Plotting
• Creating multiple plot on the same canvas
• Matplotlib "Object Oriented" approach
• Creating inset plot
• Creating a figure and a set of subplots
• Saving figures, Decorating figures

• Day-11 Data Visualization using Matplotlib-Pandas-Part 2
• Pandas Built-in Data Visualization
• Style Sheets
• Area Plot, Bar/Barh Chart
• Histogram, Line Chart
• Scatter Plot, Box Plot
• Hexagonal Bin Plot, Pie Chart                          
• Kernel Density Estimation Plot (KDE)

• Day-12 Project - Data Analysis & Visualization
• Project 1
• Data Analysis & Visualization (Overview)
• Data Analysis & Visualization (Solutions)
• Project 2
• Data Analysis & Visualization (Overview)
• Data Analysis & Visualization (Solutions)

• Day-13 Introduction to Machine Learning
• Introduction to ML - What, Why
• Machine Learning Applications
• Supervised Learning
• Unsupervised Learning
• Machine Learning with Python
• What is Machine Learning Model?
• Training and Test Sets: Splitting Data
• Underfitting and Overfitting
• Confusion Matrix (Precision, Recall, f1 score)

• Day-14 Linear Regression- K Nearest Neighbors
• Linear Regression Theory
• Linear Regression Algorithm
• Linear Regression Pen & Paper Exercise
• K Nearest Neighbors Theory
• K Nearest Neighbors Algorithm
• K Nearest Neighbors Pen & Paper Exercise

• Day-15 Hands-on - Linear Regression Model
• Linear Regression Model, No Free Lunch, Bias Variance Tradeoff
• A note on student’s concerns and questions on Future Warnings
• Linear Regression Model - Hands-on (Part 1)
• Linear Regression Model Hands-on (Part 2)
• How to save and load your trained Machine Learning Model
• Project
• Insurance Data Project (Overview)
• Insurance Data Project (Solutions)

• Day-16 Hands-on - K Nearest Neighbors
• K Nearest Neighbors, Curse of dimensionality
• K Nearest Neighbors - Hands-on
• Project
  • K Nearest Neighbors (Overview)
  • K Nearest Neighbors (Solutions)
• Day-17 Hands-on - Decision Tree
  • Decision Tree Theory
  • Decision Tree Algorithm
  • Decision Tree Pen & Paper Exercise
  • Decision Tree - Hands-on
• Day-18 Support Vector Machines -K Means Clustering
• Support Vector Machines Theory
• Support Vector Machines Algorithm
• K Means Clustering Theory
• K Means Clustering Algorithm
• K Means Clustering Pen & Paper Exercise

• Day-19 Hands-on -Support Vector Machines (SVMs)
• Support Vector Machines (SVMs)
• Support Vector Machines - Hands-on (SVMs)
• Project
• Support Vector Machines (Overview)
• Support Vector Machines (Solutions)
• Day-20 Hands-on - K Means Clustering
  • K Means Clustering, Elbow method
  • K Means Clustering - Hands-on
  • Project
    • K Means Clustering (Overview)
    • K Means Clustering (Solutions)

Having 18 years of experiences on Conducted IT consultancy and training in the core area of SQL, SQL Server, C, C++, Python, MS Excel, Power BI, Data Science & Machine learning using Python, Machine learning algorithm, UML with proven skill.

Also have the experiences on Business Analysis, Project Management, User Engagement, Service Operation, Systems Management and IT Innovation. Large ERP, with proven records for success of delivering service into robust IT infrastructure, ERP Systems Operations like Hospital Management Information System (HMIS).

I like to strive to position myself further with leadership and management challenges in a leading role through working in different cultural or business environment. More over a lots of experience in software coding, planning, designing, customization, implementation, technical support and training. Specialized in C# .Net, SQL Server, PHP, MySQL, HTML5 for web based application. Also experienced in networking installation, maintenance, security and administration.

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