Python For Data Science Training Course Online Course

Premium

Python for Data Science Training Course

All levels
English

Course Description

This interactive and comprehensive course is a great place for you to get started on Python programming language and its use in Data Science.This Data Science with Python course from BIT aims at helping you understand the core concepts of Data science including exploratory data science, statistics, hypothesis testing, regression classification modeling techniques, data visualization and machine learning algorithms. The Data Science with Python course has been designed to provide in-depth knowledge of the various libraries and packages that are required to perform data analysis, data visualization, web scraping, machine learning, and natural language processing using Python. Data Science with Python course enables you to master Data Science Analytics using Python. You will work on various python libraries like SciPy, NumPy, Matplotlib, Lambda function, etc. You will master data science analytics skills through real-world projects in multiple domains like Retail, e-commerce, Finance, etc

See more less

What you’ll learn

Live Class Practical Oriented Training
Timely Doubt Resolution
Dedicated Student Success Mentor
Certification & Job Assistance
Free Access to Workshop & Webinar
Free Access to Workshop & Webinar
No Cost EMI Option
To perform scientific and technical computing using SciPy package and its sub-packages such as Integrate, Optimize, Stat...
Perform data analysis and manipulation using data structures and tools provided in Pandas package
Gain an in-depth understanding of supervised learning and unsupervised
learning models like linear regression, logistic regression, clustering, dimensionality reduction, K-NN, and pipeline. U...
How to use the matplotlib library of Python for data visualization. Extract useful data from websites by performing web...
Integrate Python with Hadoop, Spark, and MapReduce

Covering Topics

Lecture-1 Python Environment Setup

Lecture-2 Introduction to Python

Lecture-3 Sequences and File Operations

Lecture-4 Functions, OOPs, Modules, Errors and Exceptions

Lecture-5 Database connection

Lecture-6 NumPy for mathematical computing

Lecture-7 SciPy

Lecture-8 Matplotlib for data visualization

Lecture-9 Pandas for data analysis and machine learning

Lecture-10 Exception Handling

Lecture-11 Multi Threading & Race Condition

Lecture-12 Packages and Functions

Lecture-13 Web scraping with Python

Case Studies

Curriculum

  Lecture-1 Python Environment Setup
Live Lecture 
·      Introduction to Python Language

·      Features, the advantages of Python over other programming languages

·      Python installation – Windows, Mac & Linux distribution for Anaconda Python

·      Deploying Python IDE

·      Basic Python commands

·      Data types

·      Variables

·      Keywords and more

·      Practical Exercise

  Lecture-2 Introduction to Python
Live Lecture 
·      The Companies using Python

·      Different Applications where Python is used

·      Discuss Python Scripts on UNIX/Windows

·      Values, Types, Variables

·      Operands and Expressions

·      Conditional Statements

·      Loops

·      Command Line Arguments

·      Writing to the screen

·      Practical Exercise

  Lecture-3 Sequences and File Operations
Live Lecture 
·      Python files I/O Functions

  Lecture-4 Functions, OOPs, Modules, Errors and Exceptions
Live Lecture 
·      Functions

·      Function Parameters

·      Global Variables

·      Variable Scope and Returning Values

·      Lambda Functions

·      Object-Oriented Concepts

·      Standard Libraries

·      Modules Used in Python

·      The Import Statements

·      Module Search Path

·      Package Installation Ways

·      Errors and Exception Handling

·      Handling Multiple Exceptions

·      Practical Exercise

  Lecture-5 Database connection
Live Lecture 
·      Understanding the Database, need of database

·      Installing MySQL on windows

·      Understanding Database connection using Python.

·      Practical Exercise

  Lecture-6 NumPy for mathematical computing
Live Lecture 
·      Introduction to arrays and matrices

·      Broadcasting of array math, indexing of array

·      Standard deviation, conditional probability, correlation and covariance.

·      Reading and writing arrays on files

·      How to import NumPy module

·      Creating array using ND-array

·      Calculating standard deviation on array of numbers

·      Calculating correlation between two variables.

·      Practical Exercise

  Lecture-7 SciPy
Live Lecture 
·      Introduction to SciPy

·      Functions building on top of NumPy

·      Cluster, linalg, signal, optimize, integrate

·      Subpackages, SciPy with Bayes Theorem.

·      Importing of SciPy

·      Applying the Bayes theorem on the given dataset.

·      Practical Exercise

  Lecture-8 Matplotlib for data visualization
Live Lecture 
·      How to plot graph and chart with Python

·      Various aspects of line, scatter, bar, histogram, 3D

·      The API of MatPlotLib

·      Subplots

·      Practical Exercise

  Lecture-9 Pandas for data analysis and machine learning
Live Lecture 
·      Introduction to Python dataframes

·      Importing data from JSON, CSV, Excel, SQL database,

·      NumPy array to dataframe

·      Various data operations like selecting

·      Filtering, sorting, viewing, joining, combining

·      Working on importing data from JSON files

·      Selecting record by a group

·      Applying filter on top, Viewing records

·      Practical Exercise

  Lecture-10 Exception Handling
Live Lecture 
·      Introduction to Exception Handling

·      Scenarios in Exception Handling with its execution

·      Arithmetic exception

·      RAISE of Exception

·      What is Random List

·      Running a Random list on Jupyter Notebook

·      Value Error in Exception Handling.

·      Practical Exercise

  Lecture-11 Multi Threading & Race Condition
Live Lecture 
·      Introduction to Thread, need of threads

·      What are thread functions

·      Performing various operations

·      Joining a thread

·      Starting a thread

·      Enumeration in a thread

·      Creating a Multithread

·      Finishing the multithreads

·      Understanding Race Condition

·      Lock and Synchronization

·      Practical Exercise

  Lecture-12 Packages and Functions
Live Lecture 
·      Intro to modules in Python, need of modules

·      How to import modules in python

·      Locating a module, namespace and scoping

·      Arithmetic operations on Modules using a function

·      Intro to Search path,

·      Global and local functions

·      Filter functions

·      Python Packages

·      Import in packages

·      Various ways of accessing the packages

·      Decorators

·      Pointer assignments, and Xldr

·      Practical Exercise

  Lecture-13 Web scraping with Python
Live Lecture 
·      Introduction to web scraping in Python

·      Installing of beautifulsoup

·      Installing Python parser lxml

·      Various web scraping libraries

·      Beautifulsoup,

·      Scrapy Python packages

·      Creating soup object with input HTML

·      Searching of tree, full or partial parsing, output print

·      Practical Exercise

  Case Studies

Frequently Asked Questions

There are no prerequisites for this course. The Python basics course included with this course provides an additional coding guidance.

Ans: The course offers a variety of online training options, including: Live Virtual Classroom Training: Participate in real-time interactive sessions with instructors and peers. 1:1 Doubt Resolution Sessions: Get personalized assistance and clarification on course-related queries. Recorded Live Lectures*: Access recorded sessions for review or to catch up on missed classes. Flexible Schedule: Enjoy the flexibility to learn at your own pace and according to your schedule.

Ans: Live Virtual Classroom Training allows you to attend instructor-led sessions in real-time through an online platform. You can interact with the instructor, ask questions, participate in discussions, and collaborate with fellow learners, simulating the experience of a traditional classroom setting from the comfort of your own space.

If you miss a live session, you can access recorded lectures* to review the content covered during the session. This allows you to catch up on any missed material at your own pace and ensures that you don't fall behind in your learning journey.

The course offers a flexible schedule, allowing you to learn at times that suit you best. Whether you have other commitments or prefer to study during specific hours, the course structure accommodates your needs, enabling you to balance your learning with other responsibilities effectively. *Note: Availability of recorded live lectures may vary depending on the course and training provider.