PYAUndergraduate courses
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.
- Introductory
- 4 weeks
- Approx. 15 hours per week
- 3 semester hours
About this course
This course teaches the basic Python skills and data structures an analyst needs: how to load data from different sources and aggregate it, and how to analyze and visualize it to produce high-quality output. You learn to write code both interactively, a line at a time while you are working something out, and as a complete program that runs unattended — the pair of habits that make Python useful to scientists and researchers rather than only to software engineers. The second half is spent in the two libraries that carry most analytical work in Python: pandas for handling and combining data, and matplotlib for drawing it.
Who this course is for
Data scientists, statisticians and software engineers who need Python for data analytics — web scraping, pulling data, cleaning and preparing it, and analyzing it.
What you will learn
8 outcomes
By the end of this course, you will be able to:
- Construct conditional statements and loops
- Work with strings, lists, dictionaries and variables
- Read and write data
- Use pandas for data analysis
- Group, aggregate, merge and join data sets
- Work with time series and data frames
- Use matplotlib for visualization
- Create, format and output figures
Week by week
4-Week curriculum overview
This curriculum is identical across all start dates. Expand a week to explore the topics covered.
- Weeks
- 4
- Your week
- Approx. 15 hours
- Level
- Introductory
Getting started with PythonWeek 1
- Using the Jupyter notebook
- Python basics — variables, conditionals and loops
- Data structures — lists and dictionaries
Data handling and stringsWeek 2
- Reading data into memory
- Working with strings
- Catching exceptions to deal with bad data
- Writing the data back out again
Python and pandasWeek 3
- Using pandas, the Python data analysis library
- Series and data frames
- Grouping, aggregating and applying
- Merging and joining
VisualizationWeek 4
- Visualization with matplotlib
- Figures and subplots
- Labeling and arranging figures
- Outputting graphics
Instructors
Expert-Led Guidance
Each cohort is led by dedicated instructors and assistant teachers who actively lead weekly discussions, provide personalized feedback and grade your assignments.
Mr. Kuber Deokar
Mr. Kuber Deokar is Data Science Lead at UpThink EduTech Services. He holds a master's degree in Statistics from the University of Pune, India, where he previously taught undergraduate statistics. He co-authored Machine Learning for Business Analytics with Galit Shmueli, Peter Bruce and Nitin Patel. With over a decade of experience, he specializes in course design, development, delivery and management, coordinating online courses and the communication between course creators, instructors, teaching assistants and students. His interests are machine learning and responsible artificial intelligence.
Before you start
What you need
Everything to have to hand before the first week. Open a row for the detail.
Course Format & Schedule
This is a 4-week, 100% online, asynchronous course.
- No mandatory live sessions: Log in and complete your work at times that fit your schedule.
- Weekly Releases: At the start of each week, you will receive new lecture materials and answer keys for the previous week's exercises.
- Interactive Community: Work through exercises, submit assignments, and engage with your instructor and peers via a private discussion board.
Homework
Short-answer questions testing the concepts, guided exercises in writing code, and guided data analysis problems, supported by example code and supplemental readings. There is also an end-of-course data analysis project.
Texts
No text is required; all materials will be provided online. If you want a reference, Python for Data Analysis is recommended. The 3rd edition of Python for Data Analysis is now available as an "Open Access" HTML version on this site in addition to the usual print and e-book formats.
Software
This is a hands-on course. You need Python, which is free, along with the Jupyter notebook, pandas and matplotlib — a current Python 3 distribution includes or can install all of them. Install before week one so that a broken environment is not the first thing you debug.
FAQ
I already know another language. Where will I actually be spending my time?This course
Week one will be quick — the conditionals and loops are the ones you know, wearing Python's syntax. The value is in weeks three and four: pandas has its own way of thinking about grouping, merging and indexing that does not come across from any other language, and getting a matplotlib figure to look right is a skill in itself.
Is this a general Python course or an analytics one?This course
An analytics-focused course in which the content is specifically designed around the practical tasks performed by data analysts. The course emphasizes importing and inspecting data, handling data-quality issues and irregular records, transforming and combining datasets, and producing effective data visualizations. Topics typically covered in general-purpose Python courses, such as application development and object-oriented programming, are intentionally excluded to allow greater emphasis on data analysis and its associated workflows.
Is there a money-back or satisfaction guarantee?Every course
We handle cancellation and refund requests on an individual basis. If a course is not meeting your expectations or your circumstances change, please reach out to our team (support@learnstatistics.org) so we can work with you on a solution.
Can I transfer to a later start date or withdraw after the course begins?Every course
Yes. If you need to defer your enrollment to a future start date, withdraw mid-course, or transfer your seat to a colleague, please contact us directly. We address these requests flexibly on a case-by-case basis.
Who teaches the course?Every course
Each cohort is led by dedicated instructors and assistant teachers who actively lead weekly discussions, provide personalized feedback and grade your assignments.
Something not answered here? Ask us before you book — and the terms set out what a purchase covers.
Credit, and where it counts
This course carries a verified figure of 3 semester hours. A credit recommendation from the American Council on Education says what a course is worth; the institution receiving it decides whether to award it.
Anyone can take this subject: there is no application, and you do not have to be studying at a university. It also counts toward a degree at Thomas Edison State University, which decides what to award for it. The full position — including the limits each university publishes is on its own page, and worth reading before you buy.