Data Science With Python (Beginner To Expert)

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

Data science with Python includes using Python programming skills to forecast and uncover trends that can be useful in making business choices, in addition to using Python to clean, analyze, and visualize data.

Data Science With Python (Beginner To Expert) Course Overview

The Data Science with Python course from Uplatz will guide you through the Python language basics, easy and complicated dataset exploration, and model construction and predictive analysis. You will learn how to prepare data for analysis, conduct intricate statistical analyses, produce insightful data visualizations, forecast future trends from data, design machine learning & deep learning models, and more in this Data Science with Python course.

You will steadily go from beginner Python programming to advanced Python programming in the course's Python programming section. It will be able to create your own Python scripts and carry out simple practical data analysis. This course is ideal for you if you want to broaden your horizons and pursue a career as a data scientist. This course's main objective is to give you a thorough educational framework for using Python for data science.

You will learn to apply data science methodologies and techniques while taking the Data Science with Python program. You will also develop your analytics abilities. You will be able to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques once you have a firm grasp of the fundamentals of Python that were covered in the course's first section. Popular Python toolkits like pandas, NumPy, matplotlib, scikit-learn, and others will help you do this.

The Data Science with Python training will assist you in learning and appreciating how this flexible language (Python) enables you to perform rich operations beginning with the import, cleansing, manipulation, to form a data lake or structured data sets, and finally to visualize data - thus combining all integral skills for any aspiring data scientist, analyst, consultant, or researcher.

Also, you will interact with real-world datasets and learn the statistical & machine learning methods required to train decision trees and/or employ natural language processing during this Data Science using Python training (NLP). Simply improve your Python abilities, comprehend data science principles, and start on the path to becoming a great data scientist.

Why Python for Data Science?

Today's decisions are based on a multidisciplinary approach that uses data, mathematical models, statistics, graphs, and databases for various business needs, such as weather forecasting, customer segmentation, studying protein structures in biology, designing a marketing campaign, opening a new store, and the like. This is because the data revolution has made data the new oil for organizations. Identification, integration, storing, and analysis of data drive modern data-powered technology systems so they can make valuable business decisions.

Data-supported scientific reasoning offers a thorough understanding of the business and its analysis. So, there is a need for a programming language that can efficiently be applied to real-world situations and can meet all these varied needs of data science, machine learning, data analysis, and visualization. Given its enormous capability, extensive library support, and built-in capabilities that make it simple to handle the numerous parts of Data Science, Python is a programming language that precisely fits the bill in this context. It stands out as one such language.

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 the Python Ecosystem

• Components of the 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

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