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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
Data Science with Python Certification Training with Project | Udemy
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
- 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
- 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
- Data Requirements
- Data Collection
- Data Understanding
- Data Preparation
21. Data Science Methodology (Part-2)
Module 4: From Modeling to Evaluation
- Modeling
- Evaluation
- Deployment
- Feedback
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
- 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
