Python for Analytics

Python for Analytics

This course will teach you the basic Python skills and data structures; how to load data from different sources, rearrange, and aggregate it; and finally how to analyze and visualize it to create high-quality products.

This course will teach you the basic Python skills and data structures - how to load data from different sources and aggregate it, and how to analyze and visualize it to create high-quality products.

$699 | Enroll Now
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Introduction to Python for Analytics

Overview

In this online course you’ll learn everything you need to get started using Python for data analysis.  Python is a general-purpose programming language that’s powerful, easy to learn and fast to code. It 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.

Following completion of this course, you will also be positioned to move on to the Predictive Analytics series using Python.

Introductory Level Course
100% Online Courses
4-Week Course
ACE + CAP Credit Eligible
Expert Instructors
Teacher Assistant Support
Tution-Back Guarantee

Learning Outcomes

Upon completion you will be able to read and write data, group, aggregate, merge and join data frames, create visualizations and more. By the end of this course you will be well positioned to move on to learning predictive analytics using Python.

  • Construct conditional statements and loops
  • Work with strings, lists, dictionaries, and variables
  • Read and write data
  • Use Pandas for data analysis
  • Group, aggregage, merge and join
  • Handle time series and data frames
  • Use matplotlib for visualization
  • Create format, and output figures

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.

Instructors

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Mr. David Masad

Dr. David Masad

David Masad is a consultant specializing in computational social science. He uses agent-based modeling, data science, network analysis and other computational techniques to study complex social systems. He has a PhD from George Mason University, and a BA from the University of Chicago.

See Instructor Bio

Course Syllabus

Week 1

Getting Started With Python

  • Using the Jupyter 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

Class Dates

2023

Jul 7, 2023 to Aug 4, 2023

Nov 10, 2023 to Dec 8, 2023

2024

Mar 8, 2024 to Apr 5, 2024

Jul 5, 2024 to Aug 2, 2024

2025

No classes scheduled at this time.

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Prerequisites

You should have familiarity with programming, even if it is not Python.  If you are a newcomer to programming, you should take Python Programming Introduction before taking this course.

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This course will introduce you to the basics of programming in Python on either Windows or Mac platform.
Topic: Data Science, Using Python | Skill: Introductory | Credit Options: CEU
Class Start Dates: May 12, 2023, Sep 8, 2023, Jan 12, 2024

What Our Students Say​

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It was great to get a better sense of the Python language. The course gave a great overview of the data analysis and visualization tools.

Benjamin Anderson
Financial Analyst, Nalco Champion
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Milan Hejtmanek

In a nutshell of the 13 Statistics.com courses I have completed to date, this is one of the top three in the intellectual excitement it generated, and the very best taught of all.  Prof. Sen has created a jewel of a class,packing an entire semester's work into 4 weeks, but doing so with such intelligence, panache, and elegance that I enjoyed every minute of the many hours I devoted to it.  His deft blending of video lectures, outside reading, sample programs, and personal feedback on homework was exemplary.  It would have taken me a whole year to learn this material on my own, if I could have done so at all.  I cannot praise this course highly enough

Milan Hejtmanek
Seoul National University
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Frequently Asked Questions

Can I transfer or withdraw from a course?

We have a flexible transfer and withdrawal policy that recognizes circumstances may arise to prevent you from taking a course as planned. You may transfer or withdraw from a course under certain conditions.
  • Students are entitled to a full refund if a course they are registered for is canceled.
  • You can transfer your tuition to another course at any time prior to the course start date or the drop date, however a transfer is not permitted after the drop date.
  • Withdrawals on or after the first day of class are entitled to a percentage refund of tuition.
Please see this page for more information.

Who are the instructors at the Institute?

The Institute has more than 60 instructors who are recruited based on their expertise in various areas in statistics. Our faculty members are:

  • Authors of well-regarded texts in their area;
  • Advisory board members;
  • Senior faculty; and
  • Educators who have made important contributions to the field of statistics or online education in statistics.

The majority of our instructors have more than five years of teaching experience online at the Institute.

Please visit our faculty page for more information on each instructor at The Institute for Statistics Education.

Please see our knowledge center for more information.

What type of courses does the Institute offer?

The Institute offers approximately 80 courses each year. Topics include basic survey courses for novices, a full sequence of introductory statistics courses, bridge courses to more advanced topics. Our courses cover a range of topics including biostatistics, research statistics, data mining, business analytics, survey statistics, and environmental statistics.

Please see our course search or knowledge center for more information.

Do your courses have for-credit options?

Our courses have several for-credit options:

  • Continuing education units (CEU)
  • College credit through The American Council on Education (ACE CREDIT)
  • Course credits that are transferable to the INFORMS Certified Analytics Professional (CAP®)

Please see our knowledge center for more information.

Is the Institute for Statistics Education certified?

The Institute for Statistics Education is certified to operate by the State Council of Higher Education for Virginia (SCHEV). For more information visit: https://www.schev.edu/

Please see our knowledge center for more information.

Visit our knowledge base and learn more.

FAQs + Knowledge Base

Related Courses

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This course introduces the basic paradigm for predictive modeling: classification and prediction.
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Predictive Analytics 2 with Python – Neural Nets and Regression

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Topic: Data Science, Analytics, Machine Learning, Prediction/Forecasting, SQL, Using Python | Skill: Introductory, Intermediate | Credit Options: ACE, CAP, CEU
Class Start Dates: Jul 7, 2023, Nov 10, 2023, Mar 8, 2024
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Predictive Analytics 3 with Python – Dimension Reduction, Clustering, and Association Rules

This course, with a focus on Python, will teach you key unsupervised learning techniques of association rules – principal components analysis, and clustering – and will include an integration of supervised and unsupervised learning techniques.
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Additional Course Information

Organization of 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 Requirements

This is a 4-week course requiring 10-15 hours per week of review and study, at times of your choosing.

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.

Course Text

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

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

Please order a copy of your course textbook prior to course start date.

Software

The required software is Python Programming Language.

Software Uses and Descriptions | Available Free Versions
To learn more about the software used in this course, or how to obtain free versions of software used in our courses, please read our knowledge base article “What software is used in courses?” 

Course Fee & Information

Enrollment
Courses may fill up at any time and registrations are processed in the order in which they are received. Your registration will be confirmed for the first available course date unless you specify otherwise.

Transfers and Withdrawals
We have flexible policies to transfer to another course or withdraw if necessary.

Group Rates
Contact us to get information on group rates.

Discounts
Academic affiliation?  In most courses you are eligible for a discount at checkout.

New to Statistics.com?  Click here for a special introductory discount code.  

Invoice or Purchase Order
Add $50 service fee if you require a prior invoice, or if you need to submit a purchase order or voucher, pay by wire transfer or EFT, or refund and reprocess a prior payment.

Options for Credit and Recognition

This course is eligible for the following credit and recognition options:

No Credit
You may take this course without pursuing credit or a record of completion.

Mastery or Certificate Program Credit
If you are enrolled in mastery or certificate program that requires demonstration of proficiency in this subject, your course work may be assessed for a grade.

CEUs and Proof of Completion
If you require a “Record of Course Completion” along with professional development credit in the form of Continuing Education Units (CEU’s), upon successfully completing the course, CEU’s and a record of course completion will be issued by The Institute upon your request.

ACE CREDIT | College Credit
This course has been evaluated by the American Council on Education (ACE) and is recommended for college credit.  For recommendation details (level, and number of credits), please see this page. Please note that the decision to accept specific credit recommendations is up to the academic institution accepting the credit.

ACE Digital Badge
Courses evaluated by the American Council on Education (ACE) have a digital badge available for successful completion of the course.

INFORMS-CAP
This course is 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.

Supplemental Information

There is no supplemental content for this course.

Miscellaneous

There is no additional information for this course.

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Python for Analytics
$699 | Enroll Now
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