Name: Essential Mathematical Foundations for Data Science
Start: 2018-03-26T18:00:00-06:00
End: 2018-03-26T21:00:00-06:00
Location: Galvanize- Platte Street

**Make sure to register for this paid 3-evening class on eventbrite: https://www.eventbrite.com/e/essential-mathematical-foundations-for-data-science-tickets-42924042968 **

Considering a career as a Data Scientist? Want to understand the mathematical skills critical to thriving in this profession? Come join us for an intensive three-night class on the essential Mathematical Foundations for to Data Science.

In this course, taught by Galvanize Data Science Instructors, we will walk you through the essentials of linear algebra from matrix operations and properties to matrix decomposition using NumPy. Then we’ll explore calculus essentials for data science by utilizing the symbolic computation tool SymPy. We’ll wrap up by turning theory into practice by using matrix decomposition techniques to build a recommendation engine based of user ratings.

After completion of this 3-evening workshop you’ll have a clear understanding of the mathematical skills required to thrive as a data scientist. Additionally, this course will enable you grasp the mathematical operations running ‘under the hood’ for many common data science solutions.

If you’re interested in being a data scientist, curious about the required mathematical skills to thrive in this industry, or just looking to level up your math skills, this course is for you!

Course Dates:

March 26, 27, & 28 - 6pm-9pm

• What to bring
Your Laptop, Fully Charged

Charger

• Important to know
There is street and lot parking around campus

Colorado Data Science

Technology

Python

Hadoop

Big Data

Machine Learning

Data Analytics

Data Visualization

Predictive Analytics

Data Mining

Data Science

Applied Statistics

Data Science using Python

Essential Mathematical Foundations for Data Science

Galvanize- Platte Street

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Colorado Data Science

Essential Mathematical Foundations for Data Science

Colorado Data Science

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