Python for Analytics
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
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
- 4 Weeks
- Expert Instructor
- Tuiton-Back Guarantee
- 100% Online
- TA Support
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.
Our Instructors
Matt Bezdek
Matt Bezdek is a Senior Data Scientist at Elder Research. He has over 10 years of experience in performing advanced statistical analyses. At Elder Research he helps commercial and nonprofit clients with model validation, software development, interactive data visualization, data literacy education, and building data analytic strategies.
He holds a PhD in Cognitive Psychology from Stony Brook University and has conducted research at the Georgia Institute of Technology and Washington University in St. Louis.
Matt will teach our Regression, Meta Analysis in R, and Python for Analytics courses.
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
2024
Instructors: Matt Bezdek
Instructors: Matt Bezdek
Instructors: Matt Bezdek
2025
Instructors: Matt Bezdek
Instructors: Matt Bezdek
Instructors: Matt Bezdek
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.
The Statistics.com courses have helped me a lot, pushing me to the limit and making me learn much more than I expected I could. The knowledge I gained I could immediately leverage in my job … then eventually led to landing a job in my dream company – Amazon.
Karolis Urbonas
This program has been a life and work game changer for me. Within 2 weeks of taking this class, I was able to produce far more than I ever had before.
Susan Kamp
The material covered in the Analytics for Data Science Certificate will be indispensable in my work. I can’t wait to take other courses. Great work!
Stephen McAllister
I learned more in the past 6 weeks than I did taking a full semester of statistics in college, and 10 weeks of statistics in graduate school. Seriously.
Amir Aminimanizani
This is the best online course I have ever taken. Very well prepared. Covers a lot of real-life problems. Good job, thank you very much!
Elena Rose
The more courses I take at Statistics.com, the more appreciation I have for the smart approach, quality of instructors, assistants, admin and program. Well done!
Leonardo Nagata
This course greatly benefited me because I am interested in working in AI. It has given me solid foundational knowledge…After completing this last course, I feel I have gained valuable skills that will enhance my employability in Data Science, opening up diverse career opportunities.
Richard Jackson
Frequently Asked Questions
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Additional 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.
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.