When does class start/end?
Classes begin promptly at 9:00 am, and typically end at 5:00 pm.
Python Primer for Data Science and Machine Learning | Hands-on Technical Overview
Python for Data Science Primer is a two-day Python training course that introduces data analysts and business analysts (as well as anyone interested in Data Science) to the Python programming...
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$1,795 USD
Course Code
TTPS4872
Duration
2 days
Available Formats
Classroom, Virtual
Python for Data Science Primer is a two-day Python training course that introduces data analysts and business analysts (as well as anyone interested in Data Science) to the Python programming language, as it’s often used in Data Science in web notebooks. This goal of this course is to provide students with a baseline understanding of core concepts that can serve as a platform of knowledge to follow up with more in-depth training and real-world practice.
The course begins with quick overview of Python, with demonstrations of both script-based and web notebook-based Python, and then dives into the essentials of Python necessary to a data scientist. The tail end of the course explores a quick integration of these skills with key Data Science libraries including NumPy, Pandas and Matplotlib. This class is hands-on and includes light programming labs that introduce students to basic Python syntax and concepts applicable to using Python to work with data.
- NOTE: Students heading into real project work right after training might consider a more robust hands-on course with deeper topics and lab coverage for similar subjects, such as the TTPS4873 Introduction Python for Data Science
Skills Gained
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
Who Can Benefit
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.
Prerequisites
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)
Course Details
Course Agenda
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