Introduction to Python for Analytics
Taught by Mr. David Masad

Introduction to Python for Analytics

taught by David Masad

 

This course is recognized by the Operations Research Society (INFORMS) as useful preparation for the Certified Analytics Professional (CAP) exam. Complete info below to test your CAP readiness with a practice exam from INFORMS.
 
 
 
 

 
Aim of Course:

In this online course, "Introduction to Python for Analytics," you'll learn everything you need to get you started using Python for data analysis. We'll start with basic Python skills and data structures, move on to how to load data from different sources, rearrange and aggregate it, and finally how to analyze and visualize it to create high-quality products.

About Python:  Python is a general-purpose programming language that's powerful, easy to learn and fast to code. It has a mature and growing ecosystem of open-source tools for mathematics and data analysis, and is rapidly becoming the language of choice for scientists and researchers of all stripes. Python code can be written like a traditional program, to execute an entire series of instructions at once; it can also be executed line by line or block by block, making it perfect for working with data interactively.

This course may be taken individually (one-off) or as part of a certificate program.
Course Program:

WEEK 1:  Getting Started With Python

  • Using the IPython notebook
  • Python basics: variables, conditionals, loops
  • Data structures: lists and dictionaries


WEEK 2:  Data Handling and Strings

  • Reading data into memory
  • Working with strings
  • Catching exceptions to deal with bad data
  • Writing the data back out again


WEEK 3:  Python and Pandas

  • Using Pandas, the Python data analysis library
  • Series and Data Frames
  • Grouping, aggregating and applying
  • Merging and joining


WEEK 4:  Visualization

  • Visualization with matplotlib
  • Figures and subplots
  • Labeling and arranging figures
  • Outputting graphics

Homework:

The homework in this course consists of short answer questions to test concepts, guided exercises in writing code and guided data analysis problems using software.

This course also has example software codes, supplemental readings available online, and an end of course data analysis project.

Introduction to Python for Analytics

Who Should Take This Course:
Data scientists, statisticians, software engineers who need to use Python for data analytics, including web scraping, pulling data, data cleaning, data prep and data analysis.
Level:
Introductory
Prerequisite:
You should have some familiarity with programming or command line scripting.  If you are a complete newcomer to this area, we recommend "R Programming - Introduction 1" (intended for those with no programming experience).
Organization of the Course:

This course takes place online at the Institute for 4 weeks. During each course week, you participate at times of your own choosing - there are no set times when you must be online. Course participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor.

At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials, work through exercises, and submit answers. Discussion among participants is encouraged. The instructor will provide answers and comments, and at the end of the week, you will receive individual feedback on your homework answers.

Time Requirement:
About 15 hours per week, at times of  your choosing.

Options for Credit and Recognition:
Students come to the Institute for a variety of reasons. As you begin the course, you will be asked to specify your category:
  1. No credit - You may be interested only in learning the material presented, and not be concerned with grades or a record of completion.
  2. Certificate - You may be enrolled in PASS (Programs in Analytics and Statistical Studies) that requires demonstration of proficiency in the subject, in which case your work will be assessed for a grade.
  3. CEUs and/or proof of completion - You may require a "Record of Course Completion," along with professional development credit in the form of Continuing Education Units (CEU's).  For those successfully completing the course,  CEU's and a record of course completion will be issued by The Institute, upon request.
  4. Other options - Statistics.com Specializations, INFORMS CAP recognition, and academic (college) credit are available for some Statistics.com courses
College credit:
Introduction to Python for Analytics has been evaluated by the American Council on Education (ACE) and is recommended for the lower-division baccalaureate/ associate degree category, 3 semester hours in computer information systems, statistics, or programming. Note: The decision to accept specific credit recommendations is up to each institution. More info here.

INFORMS CAP:
This course is also recognized by the Institute for Operations Research and the Management Sciences (INFORMS) as helpful preparation for the Certified Analytics Professional (CAP®) exam, and can help CAP® analysts accrue Professional Development Units to maintain their certification .
Course Text:

No text is required; all materials will be provided online.

If you want a reference, Python for Data Analysis is recommended. 

Software:
The required software is Python Programming Language.
Instructor(s):

Dates:

May 12, 2017 to June 09, 2017 September 08, 2017 to October 06, 2017 January 12, 2018 to February 09, 2018 May 11, 2018 to June 08, 2018 September 07, 2018 to October 05, 2018

Introduction to Python for Analytics

Instructor(s):

Dates:
May 12, 2017 to June 09, 2017 September 08, 2017 to October 06, 2017 January 12, 2018 to February 09, 2018 May 11, 2018 to June 08, 2018 September 07, 2018 to October 05, 2018

Course Fee: $549

Do you meet course prerequisites? What about book & software? (Click here to learn more)

We have flexible policies to transfer to another course, or withdraw if necessary (modest fee applies)

Group rates: Click here to get information on group rates. 

First time student or academic? Click here for an introductory offer on select courses. Academic affiliation?  You may be eligible for a discount at checkout.

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