## Udemy 100% Off Coupon Course | 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

## Requirements

- Enthusiasm and determination to make your mark on the world!

## Description

**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

## 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

Category: **Development, Data Science**

Instructor: Uplatz Training

Language: English

Price: $~~94.99~~ **Free** (Udemy 100% off coupon code) **ENROLL NOW**