In this 10-week certificate, students will learn all the necessary skills to become data scientists. This immersive course teaches students the programmatic side of data science using Python and SQL. Additionally, students will be taught all the necessary mathematical foundations to data science such as statistics, probability, and linear algebra. After completing this certificate, students will be able to apply for the following roles: data scientist, data analyst, business analyst, and many more. Noble Desktop will support students in their effort to find jobs by helping students with resume, interview prep, and introducing students to companies looking to hire engineers. 

This course will start with the basics of Python as no pre-requisites are required. In the first week, students will learn about the lexical syntax of Python and quickly move onto more advanced topics using Numpy such as broadcasting, u-functions, and arrays. After learning how to manipulate data, students will be taught how to programmatically plot data using Python libraries.  After week 1 students will feel comfortable with Python and its supporting data science libraries. In week 2, students will learn SQL, starting with relational database and SQL fundamentals, and compose basic queries. Create views, subqueries, and conversion functions. By the end, you will create your own SQL database, import and export data from Microsoft Office applications, and automate workflow with stored procedures. 

After completing the first two weeks of Python and SQL, students dive into machine learning concepts which will teach students how to predict outcomes from historical data. Students will learn how to build a forecasting model using a linear, mutiple, and logitsic regression in Python. After that, students wil learn how to test the model for accuracy. In the next week, students will be introduced to advanced topics in Pandas, Statstics, and Machine Learning. 

In the week 5, students will learn how to build machine learning alrogithims using Random Forrest, Bayesian Learing, SVMs, and many more. Studnets will than move onto a week of artifical intellignece were the calss will cover neural netowrk, nomralizaiton, convexity - culminating in a final project. In week 7, students will be taught all the theory behind data science including understanding matrices, correlation, Bays' Theorerm, and many more. 

In the last weeks of the course, students will work on their own projects which will be guided by the lead instructor. Additionally, there will be lectures covering interview prep, resume support, and developer tools.