Python Primer for Data Science and Machine Learning | Hands-on Technical Overview

Back to all topics
, , , , , ,
  • 2 day
  • Private Group Training
01

Course 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 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 our Introduction Python for Data Science.

02

Key Learning Areas

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
03

Course Outline

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
  • Built-in 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
04

Who Benefits

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.

05

Prerequisites

Students should have skills at least equivalent to the following course(s) or should have attended as a prerequisite:

  • Understanding Data Science | A Technical Overview – 1 day (Helpful, but not required)
  • Working with Excel (Helpful, but not required)
Want this course for your team?

Atmosera can provide this course virtually or on-site. Please reach out to discuss your requirements.

Request a Quote

Atmosera is thrilled to announce that we have been named GitHub AI Partner of the Year.

X