Considering applying to our Data Science and Analytics Bootcamps but need to build or brush up on your basic skills first? Our Beginner Python & Math for Data Science course was designed for you, the beginner looking for an introduction to the building blocks essential to developing data science skills or forging a new career in the field. This course sets you on the right track, covering everything you’ll encounter during the bootcamp application process. You’ll learn:
Introduction to programming in Python
Common Python libraries: NumPy, Pandas, Matplotlib
Course Co-designer Roberto Reif, Senior Data Scientist, Metis and Gordon Dri, Data Scientist, Oracle
The Beginner Python and Math for Data Science course was instrumental in preparing me for the Metis Bootcamp Application. I was able to improve my Python skills and brush up on my math fundamentals, which ultimately enabled me get accepted and successfully complete the Bootcamp.
Business Intelligence Analyst, Outreach
Who the course is designed for:
Those interested in applying to our Data Science and Analytics Bootcamps but who have little to no prior experience or need a refresher with fundamental Python programming and/or math concepts necessary to succeed at the next level. The only prerequisite is to have Python installed.
The ability to tackle courses in data science, particularly our Introduction to Data Science part-time course and full-time immersive Data Science and Analytics Bootcamps.
An understanding of the fundamentals of mathematical concepts in linear algebra, calculus, probability, and statistics.
The ability to write Python code to solve mathematical problems using linear algebra, calculus, probability, and statistics.
Have questions? Get answers to frequently asked questions. FAQs
What you'll receive upon completion:
Certificate of completion
Dates & Instructors
Beginner Python and Math for Data Science
March 1 to April 8
Mondays and Thursdays
6:30 - 9:30PM EST
33% savings on tuition through April 1.
Heather is a Data Scientist at iHeartMedia. Heather started her career studying mathematics, specializing in modeling applications to biological systems. After receiving a PhD and leaving academia, she transitioned to data science, working in a variety of industries, including finance, consulting, retail, and government. Getting back to her roots, she enjoys combining her love teaching mathematics with her experience of applying data science to real-world problems.
With a background in physics and quantitative finance, Adam has applied data science and machine learning solutions in a wide variety of research settings ranging from recognizing and clustering behavioral patterns to applying natural language processing techniques to help build superior equity portfolios. Previously a Senior Data Scientist at Metis, he is now a Senior Artificial Intelligence Engineer at Bond Financial Technologies, Inc. Adam is passionate about teaching and is excited to share his industry experiences with students.
Anastassia is a Research Scientist at FiscalNote, where she uses Machine Learning and Natural Language Processing to create insights from legislative and regulatory data. Over her four and a half year tenure, Anastassia has run a number of projects ranging from forecasting if legislation will pass to identifying key words and topics in text. In addition to building platform features, Anastassia has authored several papers for top-tier Natural Language Processing. Previously, she received a bachelors degree in Computer Science from Carnegie Mellon University. She loves to spend her free time outside, hiking, climbing and skiing.
33% savings on tuition through April 1. There will be no class on May 31st.
Scott is the owner of an analytics consulting business where he works with companies to build and improve data science and analytics capabilities. With over 8 years of experience in the industry, he brings practical knowledge and strong technical skills to every situation. He has worked in a broad array of fields from derivative pricing with a hedge fund to running computer vision projects on an assembly line. He lives in Fort Collins, CO and enjoys hiking, biking and snowshoeing with his family. He has a passion for both teaching and learning.
33% savings on tuition through April 1. There will be no class on July 4th.
Kevin has a formal background in physics and programming from UC Berkeley. For three years, he built data pipelines for interdisciplinary research labs on and off campus. He previously attended the Metis Data Science Bootcamp in San Francisco and then served as a teaching assistant for three cohorts. He currently works on full-stack data at Doximity while freelancing as a data science consultant.
There are no prerequisites for this course - if you’re an absolute beginner or just interested in data science, this course is for you! Students must have a Github account to get access to the content. Sign-up for an account on their site is free, fast and easy.
Students only need to be able to install and verify the installation of Anaconda (for Python 3) by running a "Hello World" sample code.
Considering our immersive data science bootcamp?
Part-time course alumni can apply the amount of tuition paid for one part-time course towards enrollment in an upcoming bootcamp upon admittance.
Course Structure & Syllabus
Get an introduction to programming in Python. Learn how Jupyter Notebooks work, and cover the basics of programming including data structures, data operations, if else statements, for and while loops, and logical operations.
Learn advanced functionality in Python, including functions, debugging, error handling, string manipulations, and writing efficient code.
Python Mathematical Libraries
Learn about using libraries that are useful for data manipulation and visualization. Specifically, we will be using NumPy, Pandas, and Matplotlib. These libraries will allow us to load and save data, manipulate data such as aggregating, filtering, detecting outliers, and visualizing.
Learn the fundamentals of linear algebra, including vectors, and vector manipulations, matrices and matrix manipulations, linear equations and solutions, eigenvalues and eigenvectors.
Calculus and Probability
Learn the fundamentals of calculus and gain an intuition for derivatives, integrals, determining local maximum and minimum, and limits. Similarly, we will cover an introduction to probability and learn about random variables, mean, variance, probability mass and density functions, and cumulative distribution functions.
Learn the basics of statistics and its applications. Some topics include ANOVA, hypothesis testing and p-value, and confidence intervals.
Live Online Interactive Learning
Learn from world-class data science practitioners.
Our Live Online instructors bring deep industry experience from a broad range of industries and companies including Viacom, Spotify, and Capital One Labs. You’ll have an Instructor and Assistant Instructor to support you throughout your learning process.
Interact with instructors and classmates in real-time.
This course is truly live, which means you can interact with the instructors and your fellow students in real-time. Stay engaged by asking questions and participating in polls and conversations, and join your course Slack channel for additional support, communication, and collaboration.
Learn online without sacrificing the value of live instruction.
The world is your classroom. Log in from wherever you are and gain access to live, interactive data science instruction that will push your career further in the right direction. In case you have to miss a class, you can access all recordings 24/7 to stay caught up and refer back.
Earn CEUs for accredited courses.
Not only will you walk away with new data science skills and knowledge, you’ll also earn up to 3.3 Continuing Education Units (CEUs). Our courses are accredited by ACCET, who requires we maintain high standards in areas such as quality of instruction and positive student feedback.
Register for an on-demand sample class
Our 1-hour on-demand sample class is a great way to preview what the new Live Online experience is like for the Beginner Python & Math for Data Science professional development course.
Gordon Dri, Data Scientist at Oracle, and instructor of the Live Online Beginner Python & Math for Data Science course, will cover a few sample topics in the on-demand class:
How to setup Jupyter Notebook and begin to write basic Python code
A brief overview of a fundamental math topic from the course
While there is no official homework, you can expect to spend a minimum of 3 hours per week reviewing material or working on projects. The non-class time spent will depend on your background and the course itself. Each instructor will address this on the first day of class, and there will be lab/office hours outside of class during which students and the instructor can collaborate.
No. We do not offer career support for students of these courses like we do for our bootcamp students, but you will gain access to our alumni community network of 1200+ data scientists and analysts. Networking events and job opportunities are posted on a regular basis in this active digital community.
Our part-time course instructors come to teach at Metis from industry and have real-world experience as practitioners of data science. Please visit the respective course pages for specific information on each instructor’s background and current jobs.
Our part-time courses typically run two nights per week over the course of 6 weeks, totaling 36 hours of instruction, but this can vary. Please see the full schedule here for the most up-to-date information. We consistently add new courses, so be sure to check back routinely.
The beauty of the Live Online format is that you’re taught by our industry-leading instructors live, but you can attend class sessions from literally anywhere you have an internet connection. Unlike some other online course options out there, which might consist of pre-recorded lectures, our courses allow for interaction with the instructor, assistant instructors, and other students – and because these are on a set schedule, you’ll be held accountable to actually attend, do the work, and learn the material (which is what you’re really here for anyway!).
It does not, simply because we evaluate each and every bootcamp applicant the same way in order to ensure fairness. However, if accepted into the bootcamp, you do have the advantage of applying the amount of one part-time course tuition toward the bootcamp tuition, so you essentially got your part-time course for free.
The curriculum will be provided via Github; therefore, you must register aGithubaccount. Sign-up for an account on their site is free, fast and easy. Github is a web-based hosting service for version control using Git.