Python Primer for Data Science
Retail Price: $1,895.00
Next Date: 10/18/2021
Course Days: 2
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Course Objectives
This course is approximately 40% hands-on, combining expert lecture, real-world demonstrations and group discussions with machine-based practical labs and exercises. Our engaging instructors and mentors are highly experienced practitioners who bring years of current "on-the-job" experience into every classroom. Throughout the hands-on course students will learn to leverage Python scripting for data science (to a basic level) using the most current and efficient skills and techniques.
Working in a hands-on learning environment, guided by our expert team, attendees will learn about and explore (to a basic level):
- How to work with Python interactively in web notebooks
- The essentials of Python scripting
- Key concepts necessary to enter the world of Data Science via Python
Course Prerequisites
This introductory-level course is intended for Business Analysts and Data Analysts (or anyone else in the data science realm) who are already comfortable working with numerical data in Excel or other spreadsheet environments. No prior programming experience is required, and a browser is the only tool necessary for the course.
Take Before: Students should have skills at least equivalent to the following course(s) or should have attended as a pre-requisite:
- Understanding Data Science | A Technical Overview – 1 day (helpful but not required)
- Working with Excel (helpful, but not required)
Outline
Please note that this list of topics is based on our standard course offering, evolved from typical industry uses and trends. We’ll work with you to tune this course and level of coverage to target the skills you need most.
- An Overview of Python
- Why Python?
- Python in the Shell
- Python in Web Notebooks (iPython, Jupyter, Zeppelin)
- Demo: Python, Notebooks, and Data Science
- Getting Started
- Using variables
- Builtin functions
- Strings
- Numbers
- Converting among types
- Writing to the screen
- Command line parameters
- Flow Control
- About flow control
- White space
- Conditional expressions
- Relational and Boolean operators
- While loops
- Alternate loop exits
- 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
- Working with files
- File overview
- Opening a text file
- Reading a text file
- Writing to a text file
- Reading and writing raw (binary) data
- Functions
- Defining functions
- Parameters
- Global and local scope
- Nested functions
- Returning values
- Essential Demos
- Sorting
- Exceptions
- Importing Modules
- Classes
- Regular Expressions
- The standard library
- Math functions
- The string module
- Dates and times
- Working with dates and times
- Translating timestamps
- Parsing dates from text
- Formatting dates
- Calendar data
- Python and Data Science
- Data Science Essentials
- Pandas Overview
- NumPy Overview
- SciKit Overview
- MatPlotLib Overview
- Working with Python in Data Science
