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Python for Data Science

Python for Data Science online training, you will gain knowledge in data analysis, machine learning, data visualization, web scraping, & natural language processing.

  • 35000
  • 40000
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  • Course Includes
  • Live Class Practical Oriented Training
  • 90 + Hrs Instructor LED Training
  • 55 + Hrs Practical Exercise
  • 35 + Hrs Project Work & Assignment
  • Timely Doubt Resolution
  • Dedicated Student Success Mentor
  • Certification & Job Assistance
  • Free Access to Workshop & Webinar
  • No Cost EMI Option
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What you will learn

  • 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

Requirements

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

Description

|| About Python for Data Science Training Course

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 Python for Data Science online training 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.

 

This Python for Data Science training is beneficial for analytics professionals willing to work with Python, Software, and IT professionals interested in the field of analytics, and anyone with a genuine interest in Data Science. Python Data Science courses including, Introduction to Data Science in Python, Math Refresher, Data Science in Real Life, and Statistics Essentials for Data Science. These courses are offered as free companions with this program. 

Course Content

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              

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              

Live Lecture

·      Python files I/O Functions

·      Numbers

·      Strings and related operations

·      Tuples and related operations

·      Lists and related operations

·      Dictionaries and related operations

·      Sets and related operations

·      Practical Exercise              

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