Data Science with Python (beginner to expert)

About Data Science with Python (beginner to expert)

A warm welcome to the Data Science with Python course by Uplatz.

Data Science with Python involves not only using Python language to clean, analyze and visualize data, but also applying Python programming skills to predict and identify trends useful for decision-making.

Why Python for Data Science?

Since data revolution has made data as the new oil for organizations, today's decisions are driven by multidisciplinary approach of using data, mathematical models, statistics, graphs, databases for various business needs such as forecasting weather, customer segmentation, studying protein structures in biology, designing a marketing campaign, opening a new store, and the like. The modern data-powered technology systems are driven by identifying, integrating, storing and analyzing data for useful business decisions. Scientific logic backed with data provides solid understanding of the business and its analysis. Hence there is a need for a programming language that can cater to all these diverse needs of data science, machine learning, data analysis & visualization, and that can be applied to practical scenarios with efficiency. Python is a programming language that perfectly fits the bill here and shines bright as one such language due to its immense power, rich libraries and built in features that make it easy to tackle the various facets of Data Science.

This Data Science with Python course by Uplatz will take your journey from the fundamentals of Python to exploring simple and complex datasets and finally to predictive analysis & models development. In this Data Science using Python course, you will learn how to prepare data for analysis, perform complex statistical analyses, create meaningful data visualizations, predict future trends from data, develop machine learning & deep learning models, and more.

The Python programming part of the course will gradually take you from scratch to advanced programming in Python. You'll be able to write your own Python scripts and perform basic hands-on data analysis. If you aspire to become a data scientist and want to expand your horizons, then this is the perfect course for you. The primary goal of this course is to provide you a comprehensive learning framework to use Python for data science.

In the Data Science with Python training you will gain new insights into your data and will learn to apply data science methods and techniques, along with acquiring analytics skills. With understanding of the basic python taught in the initial part of this course, you will move on to understand the data science concepts, and eventually will gain skills to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular Python toolkits such as pandas, NumPy, matplotlib, scikit-learn, and so on.

The Data Science with Python training will help you learn and appreciate the fact that how this versatile language (Python) allows you to perform rich operations starting from import, cleansing, manipulation of data, to form a data lake or structured data sets, to finally visualize data - thus combining all integral skills for any aspiring data scientist, analyst, consultant, or researcher. In this Data Science using Python training, you will also work with real-world datasets and learn the statistical & machine learning techniques you need to train the decision trees and/or use natural language processing (NLP). Simply grow your Python skills, understand the concepts of data science, and begin your journey to becoming a top data scientist.

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?

2. 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

3. 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

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

What is an Identifier?

Keywords

Reading Input

Taking multiple inputs from user

Output Formatting

Python end parameter

5. 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

6. 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

7. 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

8. Lists

Python Lists

Accessing Values in Lists

Updating Lists

Deleting List Elements

Basic List Operations

Built-in List Functions and Methods for list

9. 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

10. 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

11. 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

12. Regular Expression

What is a Regular Expression?

Metacharacters

match() function

search() function

re match() vs re search()

findall() function

split() function

sub() function

13. 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

14. Components of Python Ecosystem

Components of Python Ecosystem

Using Pre-packaged Python Distribution: Anaconda

Jupyter Notebook

15. 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

16. 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

17. Three-Dimensional Plotting with Matplotlib

Three-Dimensional Plotting with Matplotlib

  - 3D Line Plot

  - 3D Scatter Plot

  - 3D Contour Plot

  - 3D Surface Plot

18. Data Visualisation with Seaborn

Introduction to seaborn

Seaborn Functionalities

Installing seaborn

Different categories of plot in Seaborn

Exploring Seaborn Plots

19. 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

Correlation

Mean, Median, and Mode

Quartile

20. 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

21. Data Science Methodology (Part-2)

Module 4: From Modeling to Evaluation

Modeling

Evaluation

Module 5: From Deployment to Feedback

Deployment

Feedback

Summary

22. 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

23. Regression Analysis

Regression Analysis

Linear Regression

Implementing Linear Regression

Multiple Linear Regression

Implementing Multiple Linear Regression

Polynomial Regression

Implementing Polynomial Regression

24. Classification

What is Classification?

Classification algorithms

Logistic Regression

Implementing Logistic Regression

Decision Tree

Implementing Decision Tree

Support Vector Machine (SVM)

Implementing SVM

25. 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

26. Association Rule Learning

Association Rule Learning

Apriori algorithm

Working of Apriori algorithm

Implementation of Apriori algorithm