Data Science with Python Certification Training with Project

Data Science with Python Certification Training with Project

Free Gifts – Get Any Course or E-Degree For Free*

Necessities

  • Enthusiasm and willpower 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 Vital?
  • Utility of Data Science
  • What’s going to you be taught on this course?

2. Introduction to Python Programming

  • What’s Python Programming?
  • Historical past of Python Programming
  • Options of Python Programming
  • Utility of Python Programming
  • Setup of Python Programming
  • Getting began with the primary Python program

3. Variables and Data Sorts

  • What’s a variable?
  • Declaration of variable
  • Variable project
  • Data varieties in Python
  • Checking Data sort
  • Data varieties Conversion
  • Python packages for Variables and Data varieties

4. Python Identifiers, Key phrases, Studying Enter, Output Formatting

  • What’s an Identifier?
  • Key phrases
  • Studying Enter
  • Taking a number of inputs from person
  • Output Formatting
  • Python finish parameter

5. Operators in Python

  • Operators and sorts of operators

– Arithmetic Operators

– Relational Operators

– Project Operators

– Logical Operators

– Membership Operators

– Id Operators

– Bitwise Operators

  • Python packages for all sorts of operators

6. Determination Making

  • Introduction to Determination making
  • Kinds of determination making statements
  • Introduction, syntax, flowchart and packages for- if statement- if…else assertion

    – nested if

  • elif assertion

7. Loops

  • Introduction to Loops
  • Kinds of loops- for loop- whereas loop

    – nested loop

  • Loop Management Statements
  • Break, proceed and move assertion
  • Python packages for all sorts of loops

8. Lists

  • Python Lists
  • Accessing Values in Lists
  • Updating Lists
  • Deleting Record Components
  • Primary Record Operations
  • Constructed-in Record Features and Strategies for listing

9. Tuples and Dictionary

  • Python Tuple
  • Accessing, Deleting Tuple Components
  • Primary Tuples Operations
  • Constructed-in Tuple Features & strategies
  • Distinction between Record and Tuple
  • Python Dictionary
  • Accessing, Updating, Deleting Dictionary Components
  • Constructed-in Features and Strategies for Dictionary

10. Features and Modules

  • What’s a Perform?
  • Defining a Perform and Calling a Perform
  • Methods to write down a operate
  • Kinds of features
  • Nameless Features
  • Recursive operate
  • What’s a module?
  • Making a module
  • import Assertion
  • Finding modules

11. Working with Recordsdata

  • Opening and Closing Recordsdata
  • The open Perform
  • The file Object Attributes
  • The shut() Methodology
  • Studying and Writing Recordsdata
  • Extra Operations on Recordsdata

12. Common Expression

  • What’s a Common Expression?
  • Metacharacters
  • match() operate
  • search() operate
  • re.match() vs re.search()
  • findall() operate
  • cut up() operate
  • sub() operate

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. Parts of Python Ecosystem

  • Parts of Python Ecosystem
  • Utilizing Pre-packaged Python Distribution: Anaconda
  • Jupyter Pocket book

15. Analysing Data utilizing Numpy and Pandas

  • Analysing Data utilizing Numpy & Pandas
  • What’s numpy? Why use numpy?
  • Set up of numpy
  • Examples of numpy
  • What’s ‘pandas’?
  • Key options of pandas
  • Python Pandas – Setting Setup
  • Pandas – Data Construction with instance
  • Data Evaluation utilizing Pandas

16. Data Visualisation with Matplotlib

  • Data Visualisation with Matplotlib- What’s Data Visualisation?- Introduction to Matplotlib

    – Set up of Matplotlib

  • Kinds of information visualization charts/plots- Line chart, Scatter plot- Bar chart, Histogram

    – Space 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 Floor Plot

18. Data Visualisation with Seaborn

  • Introduction to seaborn
  • Seaborn Functionalities
  • Putting in seaborn
  • Totally different classes of plot in Seaborn
  • Exploring Seaborn Plots

19. Introduction to Statistical Evaluation

  • What’s Statistical Evaluation?
  • Introduction to Math and Statistics for Data Science
  • Terminologies in Statistics – Statistics for Data Science
  • Classes in Statistics
  • Correlation
  • Imply, Median, and Mode
  • Quartile

20. Data Science Methodology (Half-1)

Module 1: From Downside to Method

  • Enterprise Understanding
  • Analytic Method

Module 2: From Necessities to Assortment

  • Data Necessities
  • Data Assortment

Module 3: From Understanding to Preparation

  • Data Understanding
  • Data Preparation

21. Data Science Methodology (Half-2)

Module 4: From Modeling to Analysis

  • Modeling
  • Analysis

Module 5: From Deployment to Suggestions

  • Deployment
  • Suggestions

Abstract

22. Introduction to Machine Studying and its Sorts

  • What’s a Machine Studying?
  • Want for Machine Studying
  • Utility of Machine Studying
  • Kinds of Machine Studying- Supervised learning- Unsupervised studying

    – Reinforcement studying

23. Regression Evaluation

  • Regression Evaluation
  • Linear Regression
  • Implementing Linear Regression
  • A number of Linear Regression
  • Implementing A number of Linear Regression
  • Polynomial Regression
  • Implementing Polynomial Regression

24. Classification

  • What’s Classification?
  • Classification algorithms
  • Logistic Regression
  • Implementing Logistic Regression
  • Determination Tree
  • Implementing Determination Tree
  • Help Vector Machine (SVM)
  • Implementing SVM

25. Clustering

  • What’s Clustering?
  • Clustering Algorithms
  • Okay-Means Clustering
  • How does Okay-Means Clustering work?
  • Implementing Okay-Means Clustering
  • Hierarchical Clustering
  • Agglomerative Hierarchical clustering
  • How does Agglomerative Hierarchical clustering Work?
  • Divisive Hierarchical Clustering
  • Implementation of Agglomerative Hierarchical Clustering

26. Affiliation Rule Studying

  • Affiliation Rule Studying
  • 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 freshmen aspiring for a profession in Data Science
  • Data Engineers
  • Machine Studying Engineers
  • Software program Engineers and Programmers
  • Python Builders
  • Data Science Managers
  • Machine Studying / Data Science SMEs
  • Digital Data Analysts
  • Anybody all in favour of Data Science, Data Analytics, Data Engineering

Get This Free Course

 

The submit Data Science with Python Certification Training with Project – Restricted time Supply .

Get the Best Selling Courses Now | Huge Discounts – bit.ly/best-udemy-courses

Leave a Comment

Your email address will not be published.