Python Essentials (Core)

• Overview of Python
• Data Types & Data objects/structures (Numbers,Strings, Tuples, Lists, Dictionaries)
• List and Dictionary Comprehensions
• Variable & Value Labels – Data & Time Values
• Basic Operations – Mathematical – string – data
• Control flow & conditional statements
• CGI( Web Applications)
• Regular Expression
• Python with Database
• GUI Applications
• Regular Expressions
• Python Files I/O
• Python Exceptions
• Python Build-in-Functions(Text, numeric, date, utility functions)
• User defined functions – Lambda functions
• Python – Objects – OOPs concepts
• Python Django
• Concept of Packages – Important packages(Pandas, Matplotlib, etc.)

Advance Python with Data Science and Machine learning

Operations with NumPy (Numerical 1Python)

• What is NumPy
• Overview of functions & methods in NumPy
• Data Structure in NumPy

Overview of Pandas

• What is pandas, its functions & methods
• Pandas Data Structures( Series & Data Frame)
• Creating Data Structures(Data import-reading pandas)

Accessing/importing and Exporting Data using Python modules

• Importing Data from various sources
• Database Input(Connecting to database)
• Viewing Data objects – sub setting, methods
• Exporting Data to various formats

Data Analysis – Visualization using Python

• Creating different graphs using multiple python packages – Bar/pie/line chart/histogram/stack chart/boxplot/scatter/density etc)
• Important Packages for Visualization(Graphical analysis)- Pandas ,Matplotlib ,Seaborn,Bokeh etc)

Data Science using  Python-Machine Learning

Introduction to Machine Learning

• Difference between data science,data analysis,data analytics,data mining
• What is Machine Learning
• What is the goal of Machine Learning
• Applications of ML(Marketing, Risk, operations etc)

ML concepts- Learning Algorithms

• Major classes of Learning Algorithms-Supervised, Unsupervised and semi supervised
• Important Consideration like fitment of techniques
• Concept of overfitting and underfitting
• Concept of optimization

Supervised Learning- Regression problem using Linear Regression

• Introduction – Applications
• Assumptions of Linear Regression
• Building Linear Regression Model

Supervised Learning : Classification Problems using Logistic Regression

• Introduction-Applications
• Linear Vs Logistics
• Building Logistics Regression model
• Important steps in model building

Supervised Learning: Classification and Regression Problems using Decision Trees

• Overview of Decision Trees
• Types of Decision Trees
• Types of Decision Trees Algorithm
• How to used Decision Tree to solve Regression, Classification and segmentation problem
• Pruning Decision Tree
• Model Validation

Supervised Learning: Classification and Regression Problems using KNN

• What is concept of Instance based learning?
• What is KNN?
• KNN method for regression and classification

Supervised Learning: Classification and Regression Problems using Bayesian Techniques

• Bayes Theoram and its Applications
• Naive Bayes

Supervised Learning: Classification and Regression Problems using Support Vector Machines

• What is support vector machines
• Understand SVM
• Train/Test/Tune the model using SVM

Unsupervised Learning : Segmentation problems using Cluster analysis

• K-Means/K-Medians Clustering

Supervised Learning: Forecasting  problems  using  Time Series Analysis