Udemycoupon.id

Free deals, coupons, discounts for learners

Post Top Ad

Wednesday, December 9, 2020

Data Science with Python Certification Training with Project [Free 100% off premium Udemy course coupon code]

Image for course Data Science with Python Certification Training with Project

Discount FREE 100% Off

Data Science with Python Certification Training with Project

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

What you'll learn?

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

Requirements and What you should know?

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

Who is this course for?

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

What is this course about?

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

To get a course with a coupon code given by the instructor, you can click or touch the following button.

If the coupon code above doesn't work, check the FAQ page to find out more about coupon codes.

course-id:b1d5a74c-1b4f-4ae8-8811-2479bce708fe
course-coupon-id:ccf25f20-ac75-4568-a684-90d1df484d5e
blogpost-id:9c976f1d-0dac-4aa9-ab98-49519ce74c41
100%
off
Free discount
Wednesday, December 9, 2020

We’ll never share your email address with a third-party.