Course Description

Data Science Course in Pune

Data Science Training in Pune | Data Science Classes in Pune

Module 1 - Introduction To Python

• What is Python and brief history
• Why Python and who use Python
• Discussion on Python 2 and 3
• Unique features of Python
• Discussion on various IDE’s
• Demonstration of practical use cases
• Python use cases using data analysis

Module 2 - Setting Up And Installations

• Installing python
• Setting up Python Development Environment
• Installation of Jupyter Notebook
• How to access our course material using Jupyter
• Write your first program in python
• Deployment on local and cloud platforms using google colab

Module 3 - Python Object And Data Structures Operations

• Introduction to Python objects
• Python built-in functions
• Number objects and operations
• Variable assignment and keywords, String objects and operations
• Print formatting with strings
• List objects and operations
• Tuple objects and operations
• Dictionary objects and operations
• Sets and Boolean
• Object and data structures assessment test

Module 4 – Python Statements

• Introduction to Python statements
• If, elif and else statements
• Comparison operators
• Chained comparison operators
• What are loops
• For loops
• While loops
• Useful operator
• List comprehensions
• Statement assessment test
• Game challenge

Module 5 – UDF Functions And Methods

• Methods
• What are various types of functions
• Creating and calling user defined functions
• Function practice exercises
• Lambda Expressions
• Map and filter
• Nested statements and scope
• Args and kwargs in Python
• Functions and methods assignment

Milestone Project using Python

Module 6 – File And Exception Handling

• Process files using python
• Read/write and append file object
• File functions
• File pointer and operations
• Introduction to error handling
• Try, except and finally
• Python standard exceptions
• User defined exceptions
• Unit testing
• File and exceptions assignment

Module 7 – Python Modules, Packages & Inbuilt Modules

• Python inbuilt modules
• Creating UDM-User defined modules
• Passing command line arguments
• Writing packages
• Define PYTHONPATH
• __name__ and __main__

Module 8 – OOPs Concepts In Python

• Object oriented features
• Implement object oriented programming with Python
• Creating classes and objects
• Creating class attributes
• Creating methods in a class
• Inheritance
• Polymorphism
• Special methods for class

Assignment - Creating a python script to replicate deposits and withdrawals in a bank with appropriate classes and UDFs

Module 9 – Advanced Python Modules

• Collections module
• Datetime
• Python debugger
• Timing your code
• Regular expressions
• StringIO
• Python decorators
• Python generators

Module 10 – Package Installation And Parallel Processing

• Install packages on python
• Introduction to pip, easy install
• Multithreading
• Multiprocessing

Module 11 – Introduction To Machine Learning With Python

• Understanding Machine Learning
• Scope of ML
• Supervised and Unsupervised learning
• Milestone Project – 2

Module 12 – Data Analysis With Python

• Introduction to data analysis
• Why Data analysis?
• Data analysis and Artificial Intelligence Bridge
• Introduction to Data Analysis libraries
• Data analysis introduction assignment challenge

Module 13 – Data Analysis Using Numpy

• Introduction to Numpy arrays
• Creating and applying functions
• Numpy Indexing and selection
• Numpy Operations
• Exercise and assignment challenge

Module 14 – Pandas And Advanced Analysis

• Introduction to Series
• Introduction to DataFrames
• Data manipulation with pandas
• Missing data
• Groupby
• Merging, joining and Concatenating
• Operations
• Data Input and Output
• Pandas in depth coding exercises
• Text data mining and processing
• Data mining applications in Data engineering
• POC - Analysis of e-commerce dataset using pandas
• POC - Getting insights on employee salaries data using data analysis in python

Module 15 – Data Visualization With Python

Matplotlib

• Plotting using Matplotlib
• Plotting Numpy arrays
• Plotting using object-oriented approach
• Subplots using matplotlib
• Matplotlib attributes and functions
• Matplotlib exercises

Seaborn Visualization

• Categorical Plot using Seaborn
• Distributional plots using Seaborn
• Matrix plots
• Grids
• Seaborn exercises

Project- Getting insights using python analysis and visualizations on finance credit score data.

 

Assignment- Pandas built-in data visualization Data visualization

Module 16 – Mathematics And Statistics For Data Science

• Need of Mathematics for Data Science
• Exploratory data analysis (EDA)
• Numeric Variables
• Qualitative and Quantitative Analysis
• Types of Data Formats
• Measuring the Central Tendency – The Model
• Measuring Spread – Variance and Standard Deviation
• Euclidean Distance
• Confidence Coefficient
• Understanding Parametric Tests

Module 17- Machine Learning Algorithms

• Introduction to Data Science
• Introduction to Artificial Intelligence
• Introduction to Machine Learning
• Need of Machine learning in forecasting
• Demand of forecasting analytics in current industrial trends
• Introduction to Machine Learning Algorithms Categories
• Introduction to Natural Language Processing (NLP)
• Introduction to Deep Learning

Linear Regression with Python

• Introduction to Regression
• Exercise on Linear Regression using Scikit Learn Library
• Project on Linear regression using USA_HOUSING data
• Evaluation of Linear regression using python visualizations
• Practice project for Linear regression using advertisement data set to predict appropriate advertisements for users.

K- Nearest neighbours using Python

• Exercise on K-Nearest neighbors using Sci-kit Learn Library
• Project on Logistic regression using Dogs and horses’ dataset
• Getting the correct number of clusters
• Evaluation of model using confusion matrix and classification report
• Standard scaling problem
• Practice project on KNN algorithm.

Decision tree and Random forest with python

• Intuition behind Decision trees
• Implementation of decision tree using a real time dataset
• Ensemble learning
• Decision tree and random forest for regression
• Decision tree and random forest for classification
• Evaluation of the decision tree and random forest using different methods
• Practice project on decision tree and random forest using social network
• Data to predict if someone will purchase an item or not

Support Vector Machines

• Linearly separable data
• Non-linearly separable data
• SVM project with telecom dataset to predict the users portability

Principal Component Analysis

• Introduction to PCA
• Need for PCA
• Implementation to select a model on breast-cancer dataset
• Model evaluation
• Bias variance trade-off
• Accuracy paradox
• CAP curve and analysis

Clustering in unsupervised learning

• K-means clustering intuition
• Implementation of K-means with Python using mall customers data to implement clusters on the basis of spending and income
• Hierarchical clustering intuition
• Implementation of Hierarchical clustering with python

Association Algorithms

• A priori theory and explanation
• Market basket analysis
• Implementation of Apriori
• Evaluation of association learning

POC - To make a model to predict the relationship between frequently bought products together on the given dataset from a supermarket.

Module 18 - Natural Language Processing with NLTK

• Introduction to Natural Language processing
• NLTK Python library
• Data stemming technique
• Data Vectorization
• Exercise on NLTK
• POC- Apply NLP techniques to understand reviews given by customers in a dataset and predict if a review is good/bad without human intervention.

Module 19 - Deep Learning with TensorFlow and Keras

• Neural Network and Deep Learning
• What is TensorFlow?
• TensorFlow Installation
• TensorFlow basics
• TensorFlow with Contrib Learn
• TensorFlow Exercise
• What is Keras?
• Keras Basics
• Pipeline implementation using Keras
• MNIST implementation with Keras

Module 20 – Rest API With Flask And Python

• REST principles
• Creating application endpoints
• Implementing endpoints
• Using Postman for API testing

Module 21 - Rest API Integration With Databases For Web App Development

• CRUD operations on database
• REST principles and connectivity to databases
• Creating a web development API for login registers and connecting it to the database
• Deploying the API on a local server

Module 22 - Major Project

• Project use cases Introduction
• Project Scenarios
• Project life cycle
• What is version controlling in project management
• What is GitHub
• Significance of GitHub in project management
• Code submission for testing and deployment
• Predictive analytics tools and techniques
• Project best practices