Data Science using Python Training

Course Duration : 14 Weeks

Level : All Levels

Instructor-Led Training : Yes

Live Classroom: Yes

Live-Online : Yes

Transform data into insights with Python

The Data Science with Python course teaches you to master the concepts of Python programming. Through this Python for Data Science training, you will learn Data Analysis, Machine Learning, Data Visualization, Web Scraping, & NLP. Upon course completion, you will master the essential Data Science tools using Python

Vetted curriculum by Industry experts

Real-Time Experienced Trainers

Hands-On Learning

Extensive Career support

Unlock the power of Data Science with our Python Training! Dive into this comprehensive course to harness Python’s powerful libraries for analyzing, visualizing, and interpreting complex data. Enroll today!

Weeks Duration

Hr/Week Therory

Hr/Week Lab

Students

Introduction to Data Science

Application of Data Science

The Machine Learning

Supervised, Unsupervised & Reinforcement Learning

Essential Python for Data Science

Types of Data
1. Numeric, Text, List, & Dictionary
The pandas
1. DataFrame and Series
2. CSV Files
3. Excel Spreadsheets
4. JSON

Scikit-Learn package

Model
Model Hyperparameters
The scikit-learn API

How is Python code organized

How do we use modules and packages

Python’s execution model

Names and namespaces Scopes

Regression

Simple Linear Regression

The Method of Least Squares

Multiple Linear Regression

Estimating the Regression Coefficients (β₀, β₁, β₂ and β₃)
Logarithmic Transformations of
Variables
Correlation Matrices

Conducting Regression Analysis Using Python

The Correlation Coefficient
The Statsmodels formula API
Analyzing the Model Summary
The Model Formula Language
Intercept Handling

Multiple Regression Analysis
Assumptions of Regression Analysis
Explaining the Results of Regression Analysis

Regression Analysis Checks and Balances
The F-test
The T-test

Binary Classification

Understanding the Business Context

Business Discovery
Testing Business Hypotheses Using Exploratory Data Analysis
Visualization for Exploratory Data Analysis
Intuitions from the Exploratory Analysis

Feature Engineering

Business-Driven Feature Engineering

Data-Driven Feature Engineering

A Quick Peek at Data Types and a Descriptive Summary

Correlation Matrix and Visualization

Skewness of Data
Histograms
Density Plots
Other Feature Engineering Methods
Summarizing Feature Engineering
Building a Binary Classification Model Using the Logistic Regression Function
Logistic Regression Demystified
Metrics for Evaluating Model Performance
Confusion Matrix
Accuracy
Classification Report
Data Preprocessing

Testimonial's

Ideal place for anyone willing to learn Data Science and Data Analytics. The teacher is very passionate about conveying the subject knowledge in a very understandable way for anyone. Covers basics to advanced concepts. Highly recommend Invictus !! “

N. Arnav

The classes are interactive and the trainer has complete knowledge of the subject. The trainer had given us hands-on which helped us in clarifying the concepts. A detailed description of all the concepts related to deep learning was given like activation function, convolutional layer, Maxpooling layer, etc. were given.

M. Arun

All relevant topics were taught and understood well. Proper hands-on were given with encouragement of high level of participation. Teachings helped me in clearing all the related assessments

R. Veer

Excellent teaching, The way of explanation is too good and easy to understand. The way the trainer teaches the theory along with practical sessions has helped me a lot in learning and understanding Data Science. The whole teaching is done practically. It’s really helpful.

K. Pranav

Sessions were very informative and interactive. I got to learn about the in-depth working of the algorithms. I got to do a lot of Hands-on projects with the trainer’s help.

D. Aaradhya

Data Science using Python Training

Introduction to Python
Portability Coherence Developer productivity

An extensive library

Software quality

Software integration

Satisfaction and enjoyment What are the drawbacks?

Who is using Python today?
Setting up the environment
Python 2 versus Python 3 – the great debate

Installing Python

Installing IPython

Installing additional packages
How you can run a Python program
Running Python scripts

Running the Python interactive shell  Running Python as a service

Running Python as a GUI application
How is Python code organized
How do we use modules and packages

Python’s execution model

Names and namespaces

Scopes
Object-oriented Design
Introducing object-oriented

Objects and classes

Specifying attributes and behaviors

Data describes objects

Behaviors are actions
Hiding details and creating the public interface
Composition

Inheritance

Inheritance provides abstraction

Multiple inheritance
Objects in Python
Creating Python classes

Adding attributes

Behaviors

Initializing the object

Modules and packages

Organizing the modules

Absolute imports

Relative imports

Organizing module contents

Who can access my data?

Third-party libraries
When Objects Are Alike
Basic inheritance

Extending built-ins

Overriding and super

Multiple inheritance

The diamond problem

Different sets of arguments

Polymorphism Abstract base classes

Using an abstract base class

Creating an abstract base class

Demystifying the magic
Exceptions
Raising exceptions

Raising an exception

The effects of an exception

Handling exceptions

The exception hierarchy

Defining our own exceptions

When to Use Object-oriented Programming

Treat objects as objects
Adding behavior to class data with properties

Properties in detail

Decorators – another way to create properties

Deciding when to use properties

Manager objects

Removing duplicate code
Python Data Structures
Empty objects

Tuples and named tuples

Named tuples

Dictionaries

Dictionary use cases

Using defaultdict

Counter

Lists

Sorting lists

Sets Extending built-ins

Queues

FIFO queues  LIFO queues

Priority queues
Python Object-oriented Shortcuts
Python built-in functions

The len() function

Reversed Enumerate File I/O

Placing it in context

An alternative to method overloading

Default arguments

Variable argument lists

Unpacking arguments

Functions are objects too

Using functions as attributes

Callable objects
Strings and Serialization
Strings

String manipulation

String formatting

Escaping braces

Keyword arguments

Container lookups

Object lookups

Strings are Unicode

Converting bytes to text

Converting text to bytes

Mutable byte strings

Regular expressions

Matching patterns

Matching a selection of characters

Escaping characters

Matching multiple characters

Grouping patterns together

Getting information from regular expressions

Making repeated regular expressions efficient

Serializing objects

Customizing pickles  Serializing web objects
The Iterator Pattern
Design patterns in brief

Iterators

The iterator protocol

Comprehensions

List comprehensions

Set and dictionary comprehensions

Generator expressions

Yield items from another iterable

Back to log parsing

Closing coroutines and throwing exceptions

The relationship between coroutines, generators, and functions
Python Design Patterns – I
The decorator pattern

Decorators in Python

The observer pattern

The strategy pattern

Strategy in Python

The state pattern

State versus strategy

State transition as coroutines

The singleton pattern

Singleton implementation

The template pattern
Python Design Patterns – II
Testing Object-oriented Programs
Why test?

Unit testing

Assertion methods

Reducing boilerplate and cleaning up

Organizing and running tests

Ignoring broken tests

Testing with py.test

One way to do setup and cleanup

A completely different way to set up variables

Skipping tests with py.test

Imitating expensive objects

How much testing is enough?
Unit testing
Concurrency
Threads

The many problems with threads

Shared memory

The global interpreter lock

Thread overhead

Multiprocessing

Multiprocessing pools

Queues The problems with multiprocessing

Futures AsyncIO

AsyncIO in action

Reading an AsyncIO future
Database
MangoDb , Postgresql , MySql and Oracle
Docker
Docker and Containerisation
Java Design Patterns
Factory Design pattern

Builder design patterns

Adapter Design pattern

Composite Design Pattern

Prototype Design pattern

Observer design pattern

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