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
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
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
Time Requirements
About 15 hours per week, 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.