Python for Data Science

Course Description

Python for Data Science. Python is open source, interpreted, high level language and provides great approach for object-oriented programming. It is one of the best languages used by data scientists for various data science projects/applications. It provides great libraries to deal with data science applications.

Data Science with Python has a really good future. Data Scientists and Data Science are always improving and are projected to change to a vast extent over the next ten years. We can clearly say that Data Scientists will have a lot of scope in the future, and companies looking for Data Scientists will increase. The best job in the future you will get is data science jobs.

Objectives

• Basic process of data science.
• Python and Jupyter notebooks.
• An applied understanding of how to manipulate and analyze uncurated datasets.
• Basic statistical analysis and machine learning methods.
• How to effectively visualize results.

By the end of the course, you should be able to find a dataset, formulate a research question, use the tools and techniques of this course to explore the answer to that question, and share your findings.

Target Audience

• This course is intended for learners who have a basic knowledge of programming in any language (Java, C, C++, Pascal, Fortran, JavaScript, PHP, python, etc.). You could have learned these basic programming skills on your own or taken a course in programming in high school or college.

Prerequisites

• Before proceeding with this tutorial, you should have a basic knowledge of writing code in Python programming language, using any python IDE and execution of Python programs. If you are completely new to python then please refer our Python tutorial to get a sound understanding of the language.

Duration

• 40 hours

Course Outline

Module 1: Python Basics

• Running Python
• Hello, World!
•  Literals
• Python Comments
• Variables
• Writing a Python Module 
• print() Function 
• Collecting User Input 
• Getting Help 

Module 2: Functions and Modules

• Defining Functions 
• Variable Scope 
• Global Variables
• Function Parameters
• Returning Values 
• Importing Modules 

Module 3: Math

• Arithmetic Operators
• Assignment Operators 
• Built-in Math Functions 
• The math Module 
• The random Module 

Module 4: Python Strings

• Quotation Marks and Special Characters
• String Indexing
• Slicing Strings 
• Concatenation and Repetition 
• Common String Methods 
• String Formatting 
• Formatted String Literals (f-strings)
• Built-in String Functions 

Module 5: Iterables: Sequences, Dictionaries, and Sets

•  Definitions
• Sequences 
• Unpacking Sequences
• Dictionaries
• The len() Function 
• Sets

Module 6: Flow Control

• Conditional Statements 
• Loops in Python 
• break and continue
• The enumerate() Function
• Generators
• List Comprehensions 

Module 7: Virtual Environments

•  Virtual Environment 

Module 8: File Processing

Module 9: Introduction to Machine Learning

• Data Science Overview
• Data Gathering 
• Data Filtering 
• Data Transformation 
• Data Exploration
• Data Integration
• Data Analysis Concepts 
• Data Classification and Machine Learning
• Data Communication and Visualization