• Introduction
• Installation of Python and Eclipse IDE
• Variables
• Functional Programming
• Object Oriented Programming
• Modules and Packages
• Exception Handling
• File Handling
• Work with MongoDB.
• Introduction of Data Science
• Introduction of Essential Python Libraries
• Installation of Numpy, Matplotlib, Pandas, Scikit-learn, Ipython, and Jupyter.
• Data Loading
• Storage
• Data cleaning and preparation
• Time series
• Data Aggregation and Group Operations Data Analysis Examples

• Working with Interactive mode
• Working with Script mode
• Python Character Set
• Python Tokens, Keywords, Identifiers, Literals, Operators
• Variables and Assignments
• Input and Output in Python

DATA HANDLING

• Data Types
• Numbers
• Strings
• Lists
• Tuples
• Dictionary
• Set
• Frozenset
• Bool
• Mutable and Immutable

STRING MANIPULATION

• Introduction to Python String
• Accessing Individual Elements
• String Operators
• String Slices
• String Functions and Methods

Introduction to Python List Creating List Accessing List Joining List Replicating List List Slicing

TUPLES

Introduction to Tuple Creating Tuples Accessing Tuples Joining Tuples Replicating Tuples Tuple Slicing

DICTIONARIES

Introduction to Dictionary Accessing values in dictionaries Working with dictionaries Properties

SET AND FROZENSET

Introduction to Set and Frozenset

Creating Set and Frozenset Accessing and Joining Replicating and Slicing

OPERATORS

Arithmetic Operators Relational Operators Logical Operators Membership Operators Identity Operators Bitwise Operators Assignment Operators Operators Precedence Evaluating Expression Type Casting

Conditional Statements

• The if Statement
• The if-else Statement
• The if-elif Statement
• Nested if Statements
• Python Indentation
• Looping and Iteration
• The For Loop
• The While Loop
• Loop else Statement
• Nested Loops
• Break and Continue
• The Range Function
• Introduction to range()
• Types of range() function Use of range() function

Built-In Functions

• Introduction to Functions
• Using a Functions
• Python Function Types
• Structure of Python Functions
• E.g.map, zip, reduce, filter, any,chr, ord, sorted, globals, locals, all, etc.

User Defined Functions

• Structure of a Python Program w.r.t. UDF
• Types of Functions
• Invoking UDF
• Flow of Execution
• Arguments and Parameters
• Default Arguments, Named Arguments
• Scope of Variables

Recursion Function

• Use of recursion function

MODULES AND PACKAGES

Built-in Modules

• Importing Modules in Python Programs
• Working with Random Modules
• E.g.-builtins, os, time, datetime, calendar, twilio, smtp, pillow.

User Defined Modules

• Structure of Python Modules

FILE OPERATIONS

Text and Bytes files

• Opening a file
• Reading and Writing Files
• Other File tools

FORMAT CLASSES AND OBJECTS

• Classes as User Defined Data Type
• Objects as Instances of Classes
• Creating Class and Objects
• Creating Objects By Passing
• Values Variables & Methods

EXCEPTION HANDLING

• Default Exception and Errors
• Catching Exceptions
• Raise an exception
• Try...except statement
• Raise, Assert, Finally blocks
• User defined exception

INTRODUCTION TO OOPS

• Procedural Vs Modular Programming
• The Object Oriented Programming
• Data Abstraction
• Data Hiding
• Encapsulation
• Inheritance
• Polymorphism
• Generators
• Iterators

DATABASE

• SIntroduction to MySQL
• PYMYSQL Connections
• Executing queries
• Transaction Handling error

GUI PROGRAMMING

• Introduction
• Tkinter programming
• Tkinter widgets
• Frame
• Button
• Label
• Entry
• All Widget

REVISITING PYTHON

• Revisiting Python
• List and dictionary comprehension
• Programming assignment

INTRODUCTION TO DATA ANALYTICS

• Why Analytics?
• Traditional Data Management
• Types of Analytics
• Dimensions and measures
• Why learn Python for data analysis?

LIBRARIES FOR DATA ANALYTICS

• Anaconda
• Numpy, Scipy, Pandas
• Matplotlib, Seaborn

STATISTICS:

• Mean, Median, Mode
• Z-scores
• Bias -variance dichotomy
• Sampling and t-tests
• Sample vs Population statistics
• Random Variables
• Probability distribution function
• Expected value
• Binomial Distributions
• Normal Distributions
• Central limit Theorem
• Hypothesis testing
• Z-Stats vs T-stats
• Type 1 type 2 error
• Chi Square test
• ANOVA test and F-stats

JUPYTER NOTEBOOK

• Create Documentation
• Code mode
• Markdown mode
• Heading mode

NUMPY:

• Creating NumPy arrays
• Indexing and slicing in NumPy
• Downloading and parsing data
• Creating multidimensional arrays
• NumPy Data types
• Array tributes
• Indexing and Slicing
• Creating array views copies
• Manipulating array shapes I/O

SCIPY:

• Introduction to SciPy
• Create function
• modules of SciPy

• Using multilevel series
• Series and Data Frames
• Grouping, aggregating
• Merge DataFrames
• Generate summary tables
• Group data into logical pieces.
• Manipulate dates
• Creating metrics for analysis
• Data wrangling
• Merging and joining
• Analytics Vidhya dataset- Loan Prediction Problem
• Data Mugging using Pandas
• Building a Predictive Model

MATPLOTLIB:

• Scatter plot
• Bar charts, histogram
• Legend title Style
• Figures and subplots
• Plotting function in pandas
• Labelling and arranging figures
• Save plots

SEABORN:

• Style functions, Color palettes
• Distribution and Categorical plots
• Regression plots
• Axis grid objects

WEB SCRAPING:

• Scraping Webpages
• Beautifulsoup package
• Real time project

INTRODUCTION TO ML

• What is ML? And Why ML?
• Introduction to Supervised ML
• Introduction to Unsupervised ML
• Mathematical Background for ML
• Matrix ops Probability Theory (Bayes' Theorem)
• ML Glossary- Variable types, k-fold
• Overfitting/Underfitting
• Data split & hyper parameter

• Real time projects

NOTE: PREREQUISITES:

• Python programming

Machine Learning

• Introduction to ML
• What is ML? Why ML?
• Introduction to Supervised ML
• Introduction to Unsupervised ML
• Difference Between Al|DLIML

Tools required for development

Anaconda, Jupyter NB/Google Colab/Spyder

• ML libraries
• Numpy: Introduction to Numpy
• pandas:Introduction] DataFrame Loading
• datasets | Loading data from database | pandas Operation.
• Matplotlib: Introduction| Line Chart |Pie Chart
• Scatter Plot | Bar chart |Histogram
• Sklearn:: Introduction |Sklearn-API]
• Statsmodels.api
• ML Glossary
• Variable types, k-fold CV, AUC,
• F1 score,Overfitting/Underfitting,
• Generalization, ROC |Confusion matrix
• Mathematical Background for ML- Matrix ops
• Probability Theory (Bayes' Theorem)
• Statistical knowledge for ML- Mean, Median,
• Mode, Z-scores, bias-variance dichotomy.
• Exploratory Data analysis using Visualisation
• Scikit-learn Library for ML
• Code Exercises

Steps of Machine Learning

• Data Collection. The quantity & quality of your data
• dictate how accurate our model is.....
• Data Preparation. Wrangle data and prepare it for
• training Data wrangling using Pandas|Preprocessing

data and feature engineering Data split

• Choose a Model.
• Train the Model....
• Evaluate the Model.....
• 6-Parameter Tuning] hyper parameter training
• Make Predictions.

Supervised Learning

• Introduction Maths behind Supervised Machine
• Learning and Algo.

Regression:

• Linear Regression
• Multi-Linear Regression
• Lasso/Rigde
• Decision Tree Regressor
• Support Vector Regressor

Classification

• Logistic Regression
• KNN-K Nearest Neighbors
• Support Vector Classifier(SVM-SVC)
• Decision Tree Classifier(DTC)
• Random Forest
• Naïve Bayes
• Ensemble Learning

Unsupervised Machine Learning

Clustering:

• Introduction:
• Mathematics behind Clustering

k-means clustering

• Implementation of K-mean Clustering

H-clustering

• Implementation of H-clustering
• Code Exercises

Assocation Rule:

• Apiori rule

Dimensionality Reduction

• Principle Component Analysis(PCA).

• IBM ATTRITION RATE PREDICTION USING MACHINE LEARNING
• COVD-19 PATIENT OUTCOME PREDICTION USING ML
• ESTIMATE THE ONLINE SALES OF A E-COMMERCE RETAIL FIRM USING ML
• GUI BASED MACHINE LEARNING APPLICATION TO CLASSIFY THE PLANT SPECIES OF IRIS FLOWER
• PREDICT THE CHURN RATE IN A TELECOM COMPANY USING ML
• MALL CUSTOMER SEGMENTATION USING ML
• MARKET BASKET ANALYSIS AND ASSIT A SHOPPING MALL TO STACK PRODUCT
• PREDICT AND ESTIMATE CAR RE-SALE VALUE USING MACHINE LEARNING
• WORKING ON INBULIT DATASETS
• PREDICTION CLASSIFICATION OF HANDWRITTEN DIGITS