Fast Track to Python for Data Science

Learn Python for Data Science effortlessly in our Fast Track course, designed for aspiring data analysts and engineers to master Python's powerful tools and libraries for data-driven insights.

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Essential Skills Gained

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Grasp core Python concepts and syntax for effective programming.

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Leverage key Python libraries like numpy and pandas for data manipulation.

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Create visualizations using matplotlib to communicate insights clearly.

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Master error handling to improve code reliability and productivity.

Format

  • Instructor-led
  • 3 days with lectures and hands-on labs.

Audience

  • Data Analysts
  • Developers
  • Engineers
  • IT Professionals

Description

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.

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Upcoming Course Dates

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Course Outline

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An Overview of Python

  1. Why Python?

  2. Python in the Shell

  3. Python in Web Notebooks

  4. Demo: Python, Notebooks, and Data Science

Getting Started

  1. Using variables

  2. Builtin functions

  3. Strings

  4. Numbers

  5. Converting among types

  6. Writing to the screen

  7. Command line parameters

  8. Running standalone scripts under Unix and Windows

Flow Control

  1. About flow control

  2. White space

  3. Conditional expressions

  4. Relational and Boolean operators

  5. While loops

  6. Alternate loop exits

Sequences, Arrays, Dictionaries and Sets

  1. About sequences

  2. Lists and list methods

  3. Tuples

  4. Indexing and slicing

  5. Iterating through a sequence

  6. Sequence functions, keywords, and operators

  7. List comprehensions

  8. Generator Expressions

  9. Nested sequences

  10. Working with Dictionaries

  11. Working with Sets

Working with files

  1. File overview

  2. Opening a text file

  3. Reading a text file

  4. Writing to a text file

  5. Reading and writing raw (binary) data

Functions

  1. Defining functions

  2. Parameters

  3. Global and local scope

  4. Nested functions

  5. Returning values

Sorting

  1. The sorted() function

  2. Alternate keys

  3. Lambda functions

  4. Sorting collections

  5. Using operator.itemgetter()

  6. Reverse sorting

Errors and Exception Handling

  1. Syntax errors

  2. Exceptions

  3. Using try/catch/else/finally

  4. Handling multiple exceptions

  5. Ignoring exceptions

Essential Demos

  1. Importing Modules

  2. Classes

  3. Regular Expressions

The standard library

  1. Math functions

  2. The string module

Dates and times

  1. Working with dates and times

  2. Translating timestamps

  3. Parsing dates from text

  4. Formatting dates

  5. Calendar data

numpy

  1. numpy basics

  2. Creating arrays

  3. Indexing and slicing

  4. Large number sets

  5. Transforming data

  6. Advanced tricks

Python and Data Science

  1. Data Science Essentials

  2. Working with Python in Data Science

Working with Pandas

  1. pandas overview

  2. Dataframes

  3. Reading and writing data

  4. Data alignment and reshaping

  5. Fancy indexing and slicing

  6. Merging and joining data sets

Working with matplotlib

  1. Creating a basic plot

  2. Commonly used plots

  3. Ad hoc data visualization

  4. Advanced usage

  5. Exporting images

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Your team deserves training as unique as they are.

Let us tailor the course to your needs at no extra cost.