Python for Data Analytics II

Course Number:

NTVPYT011

The Python for Data Analytics II course builds on concepts from the first Python for Data Analytics course. Students will learn to leverage Python generators to conserve memory, serialize data into multiple formats, collect data from the web, integrate with Excel, use regular expressions, and perform predictive analytics. In the end, students will obtain the skills necessary to use data to make projections capable of influencing a diverse array of business decisions.

Audience:

This course is intended for data analysts with some experience in Python and basic data analysis.
Course Duration:
3 days

Prerequisites:

The following are prerequisites for this course:

  • Little to some programming knowledge in Python
  • Python for Data Analytics course or understanding of the concepts
    • Advanced Data Structures
    • Accessing Data from External Sources
    • Descriptive Analytics in Python
    • Statistical Analysis in Python
    • Data Visualization in Python
Course Objectives:

Upon completion of this course, participants should:

  • Use Python generators
  • Pickle data
  • Serialize data to JSON, XML, CSV, bytes
  • Collect data from the web
  • Integrate your Python code with Microsoft Excel
  • Write regular expressions for matching, searching, splitting, and replacing
  • Apply predictive analysis techniques
  • Develop models for predictive analysis
Course Outline:

Part 1: Python Generators

  • The yield Keyword
  • Generators
    • Generator Expressions
    • Generator Classes
  • Generator Methods
    • .send()
    • .throw()
    • .close()

Part 2: Python Data Serialization

  • The Python pickle module
  • JSON
  • XML
  • CSV
  • Bytes

Part 3: Integrating Python with Web Data

  • Sources
    • FTP
    • RESTful Data
    • Screen Scraping

Part 4: Integrating Python with Excel

  • Python Modules for Excel
    • xlrd
    • xlwr
    • xlutil
  • Interacting with Excel from Python
    • Creating Excel Spreadsheets
    • Reading Excel Spreadsheets
    • Updating Excel Spreadsheets

Part 5: Regular Expressions in Python

  • Regular Expression Syntax
    • Special Characters
    • Repetition Characters
  • The re Module
  • Regular Expression Objects
  • re Functions
    • Searching
    • Matching
    • Replacing
    • Splitting

Part 6: Introduction to Predictive Analytics

  • Answering the Question, “What could happen in the future?”
  • Case Studies of Business Applications of Predictive Analysis
    • Future Cash Flow
    • Investment Performance
    • Staffing Needs
    • Behavioral Marketing
    • Manufacturing Malfunctions
  • Ethical Concerns of Predictive Analysis
    • Infamous and Illegal, Redlining
  • Three Pillars of Data Analytics
    • Needs
    • Data and Technology
    • Actions and Insights

Part 7: Predictive & Prescriptive Analytics Techniques

  • Decision Trees
  • Regression
    • Single Linear Regression
    • Multiple Regression
  • Neural Networks
  • Time Series Modeling

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