Module 1 Introduction to Python Programming

• Comparison with other programming language
• Installing Python and pip
• Scope of Python in Production
• Working with IDE and interpreter
• Features of Python
• Python Versions
• Creating your first python program
• Python keywords and identifiers
• Python Variables
• Type of Conversion

Module 2 Data Types, Operators, Strings, Conditional statements & Loops

• Python Data Types
• Operators
  • Arithmetic operators
  • Relational operators
  • Conditional operators
  • Logical operators
• Introduction to string
  • Basic string operation
  • String functions and methods
  • Deleting a string
  • String manipulation and concatenation
• Conditional Statements
  • What are conditional statements?
  • Working with “if” condition
  • Working with “if-else” condition
  • Working with “if-elif” condition
  • Nested if condition
• Loops
  • Introduction to Loops concepts
  • Working with ‘while’ loop
  • Working with ‘for’ loop
  • Nested loop
  • Break statement and Continue statement
  • Practical examples for each topic

Module 3 Sequence of Data (Data Types)

• Python Lists
  • Lists are mutable
  • Getting to Lists
  • List indices
  • Traversing a list
  • List operations, slices and methods
  • Map, filter and reduce
  • Deleting elements
• Python Tuples
  • Creating Tuples
  • Advantages of Tuple over List
  • Comparing tuples
  • Deleting a Tuple
  • Slicing of Tuple
  • Built-in functions with Tuple
• Sets
  • How to create a set?
  • Iteration Over Sets
  • Python Set Methods
  • Python Set Operations
  • Union of sets
• Dictionaries
  • How to create a dictionary?
  • Python Dictionary Methods
  • Copying dictionary
  • Python List cmp() Method
  • Programming examples for all type of data types

Module 4 Functions

• What is a function?
• How to define and call a function in Python?
• Types of Functions
• How Function Return Value?
• Types of Arguments in Functions
• Rules to define a function in Python
• Nested Functions
• Call By Value, Call by Reference
• map(), filter(), reduce() functions
• Anonymous Functions/Lambda functions

Module 5 Modules, Packages, Date & Time

• Python Module
  • What is a Module?
  • Types of Modules
  • The import Statement
  • The from…import Statement
  • import * Statement
  • Creating User defined Modules
• Python Packages
  • What is a Package?
  • Introduction to Packages?
  • Importing module from a package
  • Creating a Package
  • Popular Python Packages
• Python Date and time
  • How to Use Date & DateTime Class
  • How to Format Time Output
  • Calendar in Python
  • The Time Module
  • Python Calendar Module
  • Practical examples

**Module 6 **

• File and Error Handling
• Python File Handling
• What is a data, Information File?
• How to create a Text Fill and Append
• Data to a File and Read a File
• Closing a file
• Read, read line, read lines, write, write lines
• Renaming and Deleting Files
• Directories in Python
• Python Error Handling
• Python Errors
• Common RunTime Errors in PYTHON
• Chain of importance Of Exception
• Exception Handling
• Try & Except
• Try, Except & else
• Python Custom Exceptions
• Ignore Errors
• Practical Examples

**Module 7 **

Classes and Objects

• Introduction to OOPs Programming
• Object Oriented Programming System
• OOPS Principles
• Define Classes
• Creating Objects
• Class variables and Instance Variables

Constructors

• Basic concept of Object and Classes
• Access Modifiers
• How to define Python classes
• Python Namespace–
• What is Inheritance?
• Types of Inheritance?
• Polymorphism
• Encapsulation
• Abstraction
• Interfaces

• DDL Statement: (CREATE, ALTER, DROP, RENAME, TRUNCATE)
• DML Statement: (INSERT, UPDATE, DELETE)
• DRL Statement: (SELECTION, PROJECTION, JOINS)
• Different types of Constraints in MySQL
• Functions in MySQL
• SELECT Statements in MySQL
• Different types of Operators
• If Conditions in MySQL
• GROUP Functions in MySQL
• SUB-QUERY in MySQL
• Different types of JOINS in MySQL
• Analytical Functions in MySQL
• SET Operators in MySQL

• Installation of mysql database connection
• Creating of mysql database using python
• Creating of mysql tables using python
• Inserting only one record to table using python
• Inserting multiple-records to table using “execute many” methods using python
• Updating table records using python
• Deleting table records using python
• Selecting of table records using 'WHERE’ clause.
• Fetching of multiple-records from two or more tables
• using joins in tables using python

Normalization

1. Normalization & why it's done
2. Standard Score & how it helps
3. Deviation Scores
4. Understanding the Deviation Score with an Example

Hypothesis Testing

1. What is a Hypothesis?
2. What are Null & Alternative hypotheses - Examples
3. Calculating the P-Value & Significance Level Alpha
4. T-Tests (one sample, two samples)
5. The Chi-Square Test of Independence
6. Test of Independence and Tests of Homogeneity
7. Conclusion, Exercises, & Solutions

Introduction to Probability

1. Understanding the definition of probability
2. When to add probabilities and when to multiply probabilities
3. Data, Distribution and Probability Density Function: Practical uses
4. The Central Limit Theorem and what it means
5. Moments
6. Covariance and Co-relation: Foundation and Interpretation

Conditional Probability & Bayesian Analysis

1. Intuitive understanding of Conditional Probability
2. Understanding Naive Bayes Probability theorem
3. Application of Naive Bayes in Drug testing
4. Hands-on Application of Bayesian analysis in spam filtering.

Introduction to DATAtab

1. Importing your data
2. Data pre-processing
3. Defining the NULL, NAN, and NV
4. Color Setting
5. Defining the Variables
6. Discretization
7. Data Labelling and Encoding in DATAtab

Basic visualization using DATAtab

1. Box plot, Bar chart, Histogram, Scatter plot
2. line chart, Bland-Altman plot, Pareto chart

Advanced computation

1. Correlation
2. Regression Analysis
3. Hypothesis Testing
4. t-Test
5. Mann-Whitney U-Test
6. Chi-Square Test
7. Anova
8. Survival Analysis

1.Introduction to python- why Python? 2.Installing the IDE's and related libraries. 3. Python hands on 4. Running python scripts 5. MatplotLib 6. Introduction to Pandas Introduction to Numpy

1. Types of data
2. Mean, Median , Mode using python
3. Standard deviation using python
4. Probability Density function and Probability mass function
5. Common data distribution, PDF, PMF, Lorenz Curves and Poisson's
6. Hands On Activity Covariance and Correlation
7. Probability Basics
8. Problems on Conditional Probability
9. Solution Conditional Probability
10. Baye’s Theorem

1. Supervised vs. Unsupervised Learning, and Train/Test
2. Linear Regression
3. Polynomial Regression
4. Logistic Regression with hands on activity
5. Multivariate Analysis: Predict housing Prices
6. Preventing Overfitting of a Hypothesis
7. Bayesian Methods
8. Implementing a Spam Classifier with Naive Bayes
9. K-Means Clustering
10. Hands in Activity Clustering people based on income and Education
11. Measuring the chaos in data: Entropy
12. Decision Trees
13. Random Forest
14. Project: Apply Decision Trees concepts
15. Ensemble Learning

1. Bias Variance Tradeoff
2. Hands On Activity K-Fold Cross-Validation to avoid over fitting Data
3. Cleaning and Normalization
4. Hands On Activity Cleaning web log data
5. Normalizing numerical data
6. Hands On Activity Detecting outliers
7. Feature Engineerin

1. Meditations on reference systems(Cartesian, spherical etc) and linear algebra
2. Support Vector Machines(SVM)
3. Project: Apply SVM concepts using Scikit
4. Principal Component Analysis(PCA) and Dimensionality Reduction
5. PCA example with the iris data set
6. Data Warehousing: ETL and ELT concepts

1. User-Based Collaborative Filtering
2. Item-Based Collaborative Filtering
3. Project: Finding Similar Movies
4. Improving the Results of Movie Similarities
5. Project: Movie Recommendations Engine
6. Improve the recommenders results

1. Spark and RDD
2. Introduction to MLLib
3. Exercise Decision Trees in Spark
4. K-Means Clustering in Spark
5. TF -IDF : Term frequency and Inverse
6. Searching documents with Spark

1. AB Testing
2. T-Tests and P-Values.
3. Hands-on With T-Tests
4. K-Means Clustering in Spark
5. When to stop an Experiment
6. AB Testing Summary