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QuickStart to Python for Data Science and Machine Learning

SS Course: GK100669

Course Overview

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Fast Track to Python for Data Science and/or Machine Learning is a three-day, hands-on course geared to equip you with the knowledge and skills necessary to handle various data science projects efficiently using Python, one of the most popular languages in the industry. Python's ease of use, extensive libraries, and robust community make it a fantastic choice for professionals seeking to enhance their data science capabilities. From automating small tasks to building complex data models, Python can enable you to streamline your work or provide significant insights for your organization.

Working in a hands-on learning environment led by our expert instructor, you ll also gain experience with Python's core topics like flow control, sequences, arrays, dictionaries, and handling files. You ll delve into functions, sorting, essential demos, the standard library, and even dates and times. You'll learn how to manage syntax errors and exceptions effectively, enhancing your code's resilience and your productivity. You'll delve into how Python it operates within web notebooks such as iPython, Jupyter, and Zeppelin, where you'll practice writing, testing, and debugging your Python code.

You ll also gain practical experience with Python and key data science libraries, enabling you to optimize data handling and create insightful visualizations. You ll explore working with large number sets and transforming data in numpy, reading, writing, and reshaping data with pandas, and creating data visualizations with matplotlib. You ll also gain experience optimizing data handling processes, creating insightful visualizations, or making data-driven decisions.

By the end of this journey, you'll have a solid understanding of Python for data science, including data analysis, manipulation, and visualization, ready to apply these new skills in your work. This course aims not just to teach Python but also to lay a strong foundation for you to continue building upon, enhancing your proficiency in Data Science and enabling you to contribute effectively to your team's data projects.

                                                                  

Scheduled Classes

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07/17/24 - GVT - Virtual Classroom - Virtual Instructor-Led
09/11/24 - GVT - Virtual Classroom - Virtual Instructor-Led
11/13/24 - GVT - Virtual Classroom - Virtual Instructor-Led
12/11/24 - GVT - Virtual Classroom - Virtual Instructor-Led

Outline

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  1. An Overview of Python
    • Why Python?
    • Python in the Shell
    • Python in Web Notebooks (iPython, Jupyter, Zeppelin)
    • Demo: Python, Notebooks, and Data Science
  2. Getting Started
    • Using variables
    • Builtin functions
    • Strings
    • Numbers
    • Converting among types
    • Writing to the screen
    • Command line parameters
    • Running standalone scripts under Unix and Windows
  3. Flow Control
    • About flow control
    • White space
    • Conditional expressions
    • Relational and Boolean operators
    • While loops
    • Alternate loop exits
  4. Sequences, Arrays, Dictionaries and Sets
    • About sequences
    • Lists and list methods
    • Tuples
    • Indexing and slicing
    • Iterating through a sequence
    • Sequence functions, keywords, and operators
    • List comprehensions
    • Generator Expressions
    • Nested sequences
    • Working with Dictionaries
    • Working with Sets
  5. Working with files
    • File overview
    • Opening a text file
    • Reading a text file
    • Writing to a text file
    • Reading and writing raw (binary) data
  6. Functions
    • Defining functions
    • Parameters
    • Global and local scope
    • Nested functions
    • Returning values
  7. Sorting
    • The sorted() function
    • Alternate keys
    • Lambda functions
    • Sorting collections
    • Using operator.itemgetter()
    • Reverse sorting
  8. Errors and Exception Handling
    • Syntax errors
    • Exceptions
    • Using try/catch/else/finally
    • Handling multiple exceptions
    • Ignoring exceptions
  9. Essential Demos
    • Importing Modules
    • Classes
    • Regular Expressions
  10. The standard library
    • Math functions
    • The string module
  11. Dates and times
    • Working with dates and times
    • Translating timestamps
    • Parsing dates from text
    • Formatting dates
    • Calendar data
  12. Nnumpy
    • Numpy basics
    • Creating arrays
    • Indexing and slicing
    • Large number sets
    • Transforming data
    • Advanced tricks
  13. Python and Data Science
    • Data Science Essentials
    • Working with Python in Data Science
  14. Working with Pandas
    • Pandas overview
    • Dataframes
    • Reading and writing data
    • Data alignment and reshaping
    • Fancy indexing and slicing
    • Merging and joining data sets
  15. Working with matplotlib
    • Creating a basic plot
    • Commonly used plots
    • Ad hoc data visualization
    • Advanced usage
    • Exporting images

BONUS Day Four or Optional Topics

For Dedicated / Private Classes:

Leveraging AI for Python in Data Science

  1. Introduction to AI with Python for Data Analysis
    • Overview of AI Libraries
    • Setting Up Your Environment:
    • Understanding AI Models
    • Creating Your First Model
    • Evaluating Model Performance
  2. Practical AI Projects in Python
    • Set up a Python project for AI applications.
    • Data Handling
    • Model Development
    • Test and validate your AI model's effectiveness.
    • Applying Your Model
  3. Using GPT Tools for Record Analysis in Data Science
    • Introduction to GPT
    • Setting Up GPT Tools
    • Analyzing Text Data
    • Generating Insights
    • Practical Applications

    Prerequisites

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    This course is geared for technical users, so familiarity with basic scripting skills is recommended, as this course does not teach general scripting basics. Students should be comfortable working with files and folders as well as command line scripting.

      Who Should Attend

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      This introductory-level technical course is geared for data analysts, developers, engineers or anyone new to Python, who are tasked with utilizing Python for data analytics tasks.