Our mini-courses are designed for those interested in part-time format classes, and, unsure of their time commitment. Mini-courses are designed for those looking for a specialize in a certain topic and/or for those to get their feet wet with data science technology. Individuals can sign-up for mini-courses in 12, 18, and 24-hour formats. Courses are organized by level with 100 as beginner.
Our curriculum is designed to prepare students for data-driven jobs such as entry-level data scientists, data engineers or data analysts. Classes are project heavy and learning intensive. Women and veterans receive 20% of all mini-courses.
|Python 101||Python For Data Science||This three week course introduces students to data science, statistics, and linear algebra using Python. No prior experience is necessary.||June 13||3 Weeks, 2 evenings/week|
|DataSci 109||Intro to Data Science + Statistics using R||This three week course introduces students to data science and statistics using R. No prior experience is necessary.||May 1 & 23||3 Weeks, 2 evenings/week|
|DataSci 200||Data Wrangling with Python||This course focuses on the methodology behind data acquisition, cleaning, and preparation. Proficiency with either Python or R is required.||May 23||2 Weeks, 2 evenings/week|
|DataSci 201||Data Visualization with Python||This course focuse on various data visualization techniques and how they can help us engage and learn from our data. Proficiency with either Python or R is required.||May 22||2 Weeks, 2 evenings/week|
|DataSci 209||Fundamentals of Machine Learning with Regression||Machine Learning and Prediction Analysis is the heart of many technological companies, from Google to Facebook to Amazon. Over three weeks, you’ll learn the fundamentals of machine learning and further your knowledge of the field with Regression Analysis. Fluency with Python or R and statistics is required.||July 5||3 Weeks, 2 evenings/week|
|DataSci 210||Natural Language Processing for Data Science||Natural Language Processing is a rapidly growing field because of its application across all disciplines. In this 3 week course, learn about the basics of text analysis, regular expressions, different topic models, and much more. This course is designed to give you a thorough understanding of the field. Fluency with Python, statistics, and data wrangling is required.||Oct 16||3 Weeks, 2 evenings/week|
|DataSci 303||Data Bases & Big Data||With the consistent growing of data every day, engineers are forced to become equipped to handle, prepare, and process this data in a computationally efficient manner. This course reviews different database & big data architecture tools available in the data science industry today. Strong knowledge of Python and data acquisition required.||June 5||4 Weeks, 2 evenings/week|
|DataSci 309||Machine Learning With Data Science||Working off of the DataSci 209 curriculum, this course enters the sphere of machine learning algorithms, including topics in both supervised and unsupervised learning. These four weeks will be heavily project and exercise based, but with the mathematical implications of this work heavily in mind. Strong knowledge of Python, statistics, linear algebra are required.||July 25||4 Weeks, 2 evenings/week|
|DataSci 403||Deep Learning with Data Science||Deep Learning is a lucrative subfield of Machine Learning. In this course, we’ll cover the mathematical skills required to understand the material, as well as the fundamentals of Neural Networks and different deep learning models. Prior knowledge of Python, linear algebra, and calculus is required.||July 3||2 Weeks, 2 evenings/week|
|DataSci 500||Build An Application: Projects|| Students are required to use at least three of the technologies covered in the other courses. On Demo Day, students present their final projects to hiring partners. ||As part of the final project, students will be guided by instructors at Byte Academy, as well as working professionals in the field.||July 17||2 Weeks, 2 evenings/week|