Tue, 17 Nov | Online

Python & Data Science Course Nov 2020

Registration is Closed

Time & Location

17 Nov 2020, 18:30 GMT+4
Online

About the Event

Python and Data Science

In this course, you will get to learn from basics and the it is sutable for studdents with any degree qualification or Working professionals who want to make it in the Information Technology Environment with a secure career and job package.

The following are the details of the course conducted by Gulf Consulting Group :

Module 1

Introduction to Python

Goal: Brief idea of what Python and basics of python.

Module 2

Data types and operations

Goal: Learn different types of sequence structures, related operations, and their usage.

Module 3

Functions and OOPs

Goal: In this Module, you will learn how to create user-defined functions and different Object-Oriented Concepts like Inheritance, Polymorphism, Overloading etc.

Module 4

Working with Modules and Handling Exceptions

Goal: In this Module, you will learn how to create generic python scripts, how to address errors/exceptions in code

Module 5

Introduction to NumPy & Pandas

Goal: This Module helps you get familiar with the basics of statistics, different types of measures and probability distributions

Module 6

Data Visualisation

Goal: In this Module, you will learn in detail about data visualization.

Module 7

Data Manipulation

Goal: Through this Module, you will understand in detail about Data Manipulation.

Module 8

Introduction to Machine Learning with Python

Goal:

In this module, you will learn the concept of Machine Learning and its types.

Module 9

Regression

Goal :

In this module, you will learn different regression Techniques and their implementation

Module 10

Supervised Learning - I

Goal:

In this module, you will learn Supervised Learning Techniques and their implementation, for example, Decision Trees, Random Forest Classifier etc.

Module 11

Supervised Learning - II

Goal:

In this module, you will learn Supervised Learning Techniques and their implementation, for example, Decision Trees, Random Forest Classifier etc.

Hands On:

  • Implementation of Naïve Bayes, SVM
Registration is Closed

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