Overview:

This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you ’ll learn the essential concepts of Python programming and gain deep knowledge in data analytics, machine learning, data visualization, web scraping, and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course

Duration:

40 hours

Course outlines:

1.      Data Science with Python

• Course Overview
• Data Science Overview
• Data Analytics Overview
• Statistical Analysis and Business Applications
• Python Environment Setup and Essentials
• Mathematical Computing with Python (NumPy)
• Scientific computing with Python (Scipy)
• Data Manipulation with Pandas
• Machine Learning with Scikit–Learn1
• Natural Language Processing with Scikit Learn
• Data Visualization in Python using matplotlib
• Web Scraping with BeautifulSoup
• Python integration with Hadoop MapReduce and Spark

2.      Math Refresher

3.      Data Science in Real life

• Course Objective
• Defining Data Science
• What do Data Science People do
• Data Science in Business
• Use Cases for Data Science
• Data Science People

4.      Python for Data Science

• Welcome
• Python Basics
• Python Data Structures
• Python Programming Fundamentals
• Working with Data in Python
• Working with Numpy Arrays
• Course Summary

5.      Statistics Essential for Data Science

• Introduction
• Sample or population data
• The fundamentals of descriptive statistics
• Measures of central tendency, asymmetry, and variability
• Practical example descriptive statistics
• Distributions
• Estimators and Estimates
• Confidence intervals advanced topics
• Practical example inferential statistics
• Hypothesis testing Introduction
• Hypothesis testing Let's start testing
• Practical example hypothesis testing
• The fundamentals of regression analysis
• Subtleties of regression analysis
• Assumptions for linear regression analysis
• Dealing with categorical data
• Practical example regression analysis