[45 HOUR] Data Science with Python Certification Training with Project

Short Description

Start your career as Data Scientist from scratch. Learn Data Science with Python. Predict trends with advanced analytics

What you’ll learn

  • End-to-end knowledge of Data Science
  • Prepare for a career path as Data Scientist / Consultant
  • Overview of Python programming and its application in Data Science
  • Detailed level programming in Python – Loops, Tuples, Dictionary, List, Functions & Modules, etc.
  • Decision-making and Regular Expressions
  • Introduction to Data Science Libraries
  • Components of Python Ecosystem
  • Analysing Data using Numpy and Pandas
  • Data Visualisation with Matplotlib
  • Three-Dimensional Plotting with Matplotlib
  • Data Visualisation with Seaborn
  • Introduction to Statistical Analysis – Math and Statistics
  • Terminologies & Categories of Statistics, Correlation, Mean, Median, Mode, Quartile
  • Data Science Methodology – From Problem to Approach, From Requirements to Collection, From Understanding to Preparation
  • Data Science Methodology – From Modeling to Evaluation, From Deployment to Feedback
  • Introduction to Machine Learning
  • Types of Machine Learning – Supervised, Unsupervised, Reinforcement
  • Regression Analysis – Linear Regression, Multiple Linear Regression, Polynomial Regression
  • Implementing Linear Regression, Multiple Linear Regression, Polynomial Regression
  • Classification, Classification algorithms, Logistic Regression
  • Decision Tree, Implementing Decision Tree, Support Vector Machine (SVM), Implementing SVM
  • Clustering, Clustering Algorithms, K-Means Clustering, Hierarchical Clustering
  • Agglomerative & Divisive Hierarchical clustering
  • Implementation of Agglomerative Hierarchical Clustering
  • Association Rule Learning
  • Apriori algorithm – working and implementation

This course includes

  • 44.5 hours on-demand video
  • 36 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of completion
  • Enthusiasm and determination to make your mark on the world!


Data Science with Python Programming – Course Syllabus

  1. Introduction to Data Science

Introduction to Data Science

Python in Data Science

Why is Data Science so Important?

Application of Data Science

What will you learn in this course?

  1. Introduction to Python Programming

What is Python Programming?

History of Python Programming

Features of Python Programming

Application of Python Programming

Setup of Python Programming

Getting started with the first Python program

  1. Variables and Data Types

What is a variable?

Declaration of variable

Variable assignment

Data types in Python

Checking Data type

Data types Conversion

Python programs for Variables and Data types

  1. Python Identifiers, Keywords, Reading Input, Output Formatting

What is an Identifier?


Reading Input

Taking multiple inputs from user

Output Formatting

Python end parameter

  1. Operators in Python

Operators and types of operators

      - Arithmetic Operators

      - Relational Operators

      - Assignment Operators

      - Logical Operators

      - Membership Operators

      - Identity Operators

      - Bitwise Operators

Python programs for all types of operators

  1. Decision Making

Introduction to Decision making

Types of decision making statements

Introduction, syntax, flowchart and programs for

  • if statement
  • if…else statement
  • nested if

elif statement

  1. Loops

Introduction to Loops

Types of loops

  • for loop
  • while loop
  • nested loop

Loop Control Statements

Break, continue and pass statement

Python programs for all types of loops

  1. Lists

Python Lists

Accessing Values in Lists

Updating Lists

Deleting List Elements

Basic List Operations

Built-in List Functions and Methods for list

  1. Tuples and Dictionary

Python Tuple

Accessing, Deleting Tuple Elements

Basic Tuples Operations

Built-in Tuple Functions & methods

Difference between List and Tuple

Python Dictionary

Accessing, Updating, Deleting Dictionary Elements

Built-in Functions and Methods for Dictionary

  1. Functions and Modules

What is a Function?

Defining a Function and Calling a Function

Ways to write a function

Types of functions

Anonymous Functions

Recursive function

What is a module?

Creating a module

import Statement

Locating modules

  1. Working with Files

Opening and Closing Files

The open Function

The file Object Attributes

The close() Method

Reading and Writing Files

More Operations on Files

  1. Regular Expression

What is a Regular Expression?


match() function

search() function

re.match() vs re.search()

findall() function

split() function

sub() function

  1. Introduction to Python Data Science Libraries

Data Science Libraries

Libraries for Data Processing and Modeling

  • Pandas
  • Numpy
  • SciPy
  • Scikit-learn

Libraries for Data Visualization

  • Matplotlib
  • Seaborn
  • Plotly
  1. Components of Python Ecosystem

Components of Python Ecosystem

Using Pre-packaged Python Distribution: Anaconda

Jupyter Notebook

  1. Analysing Data using Numpy and Pandas

Analysing Data using Numpy & Pandas

What is numpy? Why use numpy?

Installation of numpy

Examples of numpy

What is ‘pandas’?

Key features of pandas

Python Pandas – Environment Setup

Pandas – Data Structure with example

Data Analysis using Pandas

  1. Data Visualisation with Matplotlib

Data Visualisation with Matplotlib

  • What is Data Visualisation?
  • Introduction to Matplotlib
  • Installation of Matplotlib

Types of data visualization charts/plots

  • Line chart, Scatter plot
  • Bar chart, Histogram
  • Area Plot, Pie chart
  • Boxplot, Contour plot
  1. Three-Dimensional Plotting with Matplotlib

Three-Dimensional Plotting with Matplotlib

  • 3D Line Plot
  • 3D Scatter Plot
  • 3D Contour Plot
  • 3D Surface Plot
  1. Data Visualisation with Seaborn

Introduction to seaborn

Seaborn Functionalities

Installing seaborn

Different categories of plot in Seaborn

Exploring Seaborn Plots

  1. Introduction to Statistical Analysis

What is Statistical Analysis?

Introduction to Math and Statistics for Data Science

Terminologies in Statistics – Statistics for Data Science

Categories in Statistics


Mean, Median, and Mode


  1. Data Science Methodology (Part-1)

Module 1: From Problem to Approach

Business Understanding

Analytic Approach

Module 2: From Requirements to Collection

Data Requirements

Data Collection

Module 3: From Understanding to Preparation

Data Understanding

Data Preparation

  1. Data Science Methodology (Part-2)

Module 4: From Modeling to Evaluation



Module 5: From Deployment to Feedback




  1. Introduction to Machine Learning and its Types

What is a Machine Learning?

Need for Machine Learning

Application of Machine Learning

Types of Machine Learning

  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning
  1. Regression Analysis

Regression Analysis

Linear Regression

Implementing Linear Regression

Multiple Linear Regression

Implementing Multiple Linear Regression

Polynomial Regression

Implementing Polynomial Regression

  1. Classification

What is Classification?

Classification algorithms

Logistic Regression

Implementing Logistic Regression

Decision Tree

Implementing Decision Tree

Support Vector Machine (SVM)

Implementing SVM

  1. Clustering

What is Clustering?

Clustering Algorithms

K-Means Clustering

How does K-Means Clustering work?

Implementing K-Means Clustering

Hierarchical Clustering

Agglomerative Hierarchical clustering

How does Agglomerative Hierarchical clustering Work?

Divisive Hierarchical Clustering

Implementation of Agglomerative Hierarchical Clustering

  1. Association Rule Learning

Association Rule Learning

Apriori algorithm

Working of Apriori algorithm

Implementation of Apriori algorithm

Who this course is for:

  • Data Scientists
  • Data Analysts / Data Consultants
  • Senior Data Scientists / Data Analytics Consultants
  • Newbies and beginners aspiring for a career in Data Science
  • Data Engineers
  • Machine Learning Engineers
  • Software Engineers and Programmers
  • Python Developers
  • Data Science Managers
  • Machine Learning / Data Science SMEs
  • Digital Data Analysts
  • Anyone interested in Data Science, Data Analytics, Data Engineering

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