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: Request Date

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

 


Outline

 

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

 

 



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