Python Primer for Data Science

Python Primer for Data Science is a three-day, hands-on course that introduces data analysts and business analysts 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. Students will explore basic Python syntax and concepts applicable to using Python to work with data. The course begins with quick introduction to 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. Students will explore the concepts and work with large data sets in a workshop style lab.

Retail Price: $2,495.00

Next Date: 05/24/2021

Course Days: 3

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Course Objectives 

Throughout the course students will be led through a series of progressively advanced topics, where each topic consists of lecture, demo, hands-on lab exercises, and lab review. This course is “skills-centric”, designed to train attendees in core Python data science skills, coupling the most current, effective techniques with best practices.


Working within in an engaging, hands-on learning environment, guided by our expert, students will explore:

  • 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


Audience & Pre-requisites

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 SAS or 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 After:

  •  TTPS4876  Next Level Python for Data Science – 3 days




Session 1:  An Overview of Python

·       Why Python?

·       Python in the Shell

·       Python in Web Notebooks (iPython, Jupyter, Zeppelin)

·       Demo: Python, Notebooks, and Data Science

·       Python 2 vs 3


Session 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



Session 3: Flow Control

·       About flow control

·       White space

·       Conditional expressions

·       Relational and Boolean operators

·       While loops

·       Alternate loop exits


Session 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


Session 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


Session 6:  Functions

·       Defining functions

·       Parameters 

·       Global and local scope

·       Nested functions

·       Returning values


Session 7:  Sorting

·       The sorted() function

·       Alternate keys

·       Lambda functions

·       Sorting collections

·       Using operator.itemgetter()

·       Reverse sorting


Session 8:  Errors and Exception Handling

·       Syntax errors

·       Exceptions

·       Using try/catch/else/finally

·       Handling multiple exceptions

·       Ignoring exceptions


Session 9:  Essential Demos

·       Importing Modules

·       Classes

·       Regular Expressions


Session 10:  The standard library

·       Math functions

·       The string module


Session 11:  Dates and times

·       Working with dates and times

·       Translating timestamps

·       Parsing dates from text

·       Formatting dates

·       Calendar data


Session 12: numpy

·       numpy basics

·       Creating arrays

·       Indexing and slicing

·       Large number sets

·       Transforming data

·       Advanced tricks


Session 13: Python and Data Science

·       Data Science Essentials

·       Working with Python in Data Science


Session 14: Working with Pandas

·       pandas overview

·       Dataframes

·       Reading and writing data

·       Data alignment and reshaping

·       Fancy indexing and slicing

·       Merging and joining data sets


Time Permitting


Session: matplotbil

·       Creating a basic plot

·       Commonly used plots

·       Ad hoc data visualization

·       Advanced usage

·       Exporting images



Course Dates Course Times (EST) Delivery Mode GTR
5/24/2021 - 5/26/2021 10:00 AM - 6:00 PM Virtual gauranteed to run course date Enroll
7/15/2021 - 7/17/2021 10:00 AM - 6:00 PM Virtual Enroll
7/15/2021 - 7/17/2021 10:00 AM - 6:00 PM Virtual Enroll
9/1/2021 - 9/3/2021 10:00 AM - 6:00 PM Virtual Enroll
9/1/2021 - 9/3/2021 10:00 AM - 6:00 PM Virtual gauranteed to run course date Enroll
10/20/2021 - 10/22/2021 10:00 AM - 6:00 PM Virtual Enroll
10/20/2021 - 10/22/2021 10:00 AM - 6:00 PM Virtual Enroll
12/1/2021 - 12/3/2021 10:00 AM - 6:00 PM Virtual Enroll
12/1/2021 - 12/3/2021 10:00 AM - 6:00 PM Virtual gauranteed to run course date Enroll