Course Details
Course Duration: 3 days; Instructor-led
Audience
This course “Data Science with Python” is intended for learners who have basic python knowledge and wants to apply statistics, machine learning, information visualization, social network analysis, and text analysis techniques to gain new insight into data.
Prerequisites
There are no prerequisites for this course but python knowledge with a little programming background is preferred
Methodology
This program will be conducted with interactive lectures, PowerPoint presentation, discussion and practical exercise.
Course Objectives
After completing this course, you should be able to:
- Explore Python fundamentals, including basic syntax, variables, and types.
- Create and manipulate regular Python lists.
- Use functions and import packages.
- Build Numpy arrays and perform interesting calculations.
- Create and customize plots on real data.
- Supercharge with control flow, and get to know the Pandas DataFrame
- Use Python to read and write files.
- Illustrate Supervised Learning Algorithms
- Identify and recognize machine learning algorithms around us
Outlines
Module 1: Python Crash Course
- Introduction to the Course
- Environment Set-Up
- Virtual Environments
- Data types and Operators
- Integers, Floats, Strings, Bytes, Tuples and Lists
- Dictionaries and Ordered Dictionaries
- Sets and frozen sets.
- Flow control – if, elif statements
- Flow control – while loops
- Creating and using functions
- Creating modules and packages
- Distributing code to repositories
Module 2: Python Object Oriented
- Creating Classes
- Creating Objects and Instances
- Data Encapsulation
- Class Inheritance
- Multiple Inheritance
- Decorators
Module 3: Error Handling and Testing
- Handling Exception
- Raising exceptions
- Writing tests cases
- Executing tests
- Checking code coverage by tests
Module 4: Working with Files and Directories
- Accessing different types of files
- File handling principles
- Creating and reading Files
- Updating Files
- Deleting files
- Text Files
- CSV Files
- Microsoft Word
- Microsoft Excel
- Regular Expressions
- Extracting data from text files using Regular Expressions
- Creating and deleting directories
- Listing and searching for files
Module 5: Accessing RDBMS Databases
- Introduction to RDBMS Databases and Concepts
- Types of SQL Statements (DDL vs DML)
- How to Access Databases Using Python
- CREATE TABLE
- ALTER, DROP and TRUNCATE
- SELECT statement
- COUNT, DISTINCT, LIMIT
- INSERT statement
- UPDATE AND DELETE statements
- Using String Patterns and Ranges
- Sorting Result Sets
- Grouping Result Sets
- Built-in Database Functions
- Date and Time Built-in Functions
- Sub-Queries and Nested Selects
- Working with Multiple Tables
Module 6: Accessing RDBMS Databases using SQLAlchemy
- Object Relational Mapper (SQLAlchemy)
- Introduction and Architecture
- Introduction to SQLAlchemy ORM
- Database Models
- Relationships
- Queries
- Inserting Data
- Updating Data
- Deleting Data
Module 7: Accessing NOSQL Databases
- Introduction
- Differences between MongoDB and MySQL
- Setting up MongoDB
- Overview of MongoDB Features and Architecture
- Mapping between a relational database and MongoDB
- Connecting to MongoDB
- Starting a Python + MongoDB application
- Understanding the MongoDB Data Processing Pipeline
- Reading and Writing to the database
- Creating a New Database
- Understanding Availability in MongoDB
- Summary and Conclusion
Module 8: Website Crawling and Scraping
- Introduction
- Setting up the Development Environment
- Python Primer: Data Structures, Conditionals, File Handling, etc.
- Python Packages for Web Scraping: Scrapy and BeautifulSoup
- How a Website Works
- How HTML is Structured
- Making a Web Request
- Scraping an HTML Page
- Working with XPath and CSS
- Filtering Data Using Regular Expressions
- Creating a Web Crawler
- Crawling AJAX and JavaScript Pages with Selenium.
- Web Scraping Best Practices
- Troubleshooting
- Summary and Conclusion
Module 9: Python for Data Analysis - NumPy
- Introduction
- Ndarray Object
- Data Types
- Array Attributes
- Array Creation Routines
- Array from existing data
- Numerical ranges
- Array Indexing and Slicing
- Advanced Indexing
- Iterating over Array
- Array Manipulation
- Arithmetic Operators
- Binary Operators
- String Functions
- Mathematical Functions
- Statistical Functions
Module 10: Python for Data Analysis – SciPy
- Introduction
- Basic functions
- Special functions
- Integration
- Optimization
- Interpolation
- Fourier transforms
- Signal Processing
- Linear Algebra
- Sparse Eigenvalue Problems with ARPACK
- Compressed Sparse Graph Routines
- Spatial data structures and algorithms
- Statistics
- Multidimensional image processing
- File IO
Module 11: Python for Data Analysis - Pandas
- Introduction to Pandas
- Series
- DataFrames
- Missing Data
- Groupby
- Merging Joining and Concatenating
- Operations
- Data Input and Output
Module 12: Python for Data Visualization
- Matplotlib
- Seaborn
- Distribution Plots
- Categorical Plots
- Matrix Plots
- Grids
- Regression Plots
- Pandas Built-in Data Visualization
- Plotly and Cufflinks
- Geographical Plotting
- Choropleth Maps
