ALl Classes + Events

Real World Applications of Data Science

Predictive analytics were first put to use at a large scale in the banking industry. “Quants” were predicting future stock prices based off of patterns deciphered by parsing through historical records of the behavior of the stocks. In today’s world, predictive analytics are ubiquitous in not only the banking industry, but also healthcare, business, and politics amongst many others.

In this course, we will build tools to learn from data organized from real-life events to make predictions about what the future might hold. We will utilize tools in feature extraction, machine learning and deep learning to make inferences about the very world that we live in.

Building a data science tool belt to learn from and make predictions about real-life data.

This course is about streamlining the thought process used by data scientists and utilizing methodologies of data science to build real tools that could be applied in a number of industries. By the end of this course, you will be able to collect, organize, and learn from data generated in today’s world and catalyze a new way of thinking about problem solving.

Applications of DSPrerequisites + Downloads

  • Basic understanding of command line operations
  • A GitHub account (
  • A laptop with Python installed and a working knowledge of Python
  • Understanding of fundamental statistical concepts
  • Ability to think like a data-driven problem solver

Course Outline

Each class will be an engaging, interactive session where we build tools together to make predictions about our data. The classes will be focused on actually building the predictive tools; however, each class will have supplementary lecture notes that describe the methodologies in further detail and extra programming tasks if anyone wants extra practice.

  • Class 1: Introduction + Python Basics
    • In this session we will go over some of the fundamental functions, packages, and libraries that we will use. We will also train our first model and make our first prediction using statistical analysis.
  • Class 2: Machine Learning 101 + Model Evaluation
    • In this session we will focus on how to set up a machine learning infrastructure and build our first models! We will also apply quantifiable metrics to evaluate the efficacy of our models.
  • Class 3: Deep Learning + Cloud Computing
    • In this session we will apply elements of deep learning to our prediction tool belt. This includes constructing a deep learning architecture and applying it to a large-scale problem using a cloud computing instance.
  • Class 4: Health + Data
    • In this session we will introduce some more advanced machine learning models and utilize our knowledge learned in the previous 3 sessions to make predictions about health-based data.
  • Class 5: Business + Data
    • In this session we will learn about “deeper” layers of deep learning and apply them to make predictions about a company’s customer acquisition strategy.
  • Class 6: Politics + Data
    • In this session we will apply the techniques from previous sessions to make predictions about our very own Baltimore mayoral race.


This course begins on Monday March 21 at 6pm and will meet every Mon + Wed from 6pm-9pm for three weeks.

This course will be held at Spark Baltimore:

Spark Baltimore
8 Market Place Suite 300
Baltimore, MD 21202


We have an information session scheduled for 6pm on Wednesday, March 16 at Spark Baltimore where you can meet the instructor, check out the space, and learn a little more about what will be covered in the course. You can register for the information session here. If you cannot attend the info session, please don’t hesitate to send any questions to:

Reserve Your Spot Now

Please note: seats are limited for this course.


  • date_range
    Mon, Mar 21st

    6:00pm – 9:00pm

    Photo of Hunter Jackson
    Hunter Jackson

    Cofounder at Proscia

    Hunter founded Proscia in 2014 with technologists from the Johns Hopkins University and the University of Pittsburgh. Proscia is a digital pathology informatics company providing cloud-based storage, management, and analysis for digitized biopsy images.

    His training in pure mathematics provides a deep understanding of the theory and methodologies used in data science and distributed computing. Through the years, he has published papers in complex analysis and algebraic topology, TA'd multiple upper-level undergraduate mathematics courses, as well as constructed the machine learning architecture for Proscia's tissue analysis platform. When he's not day-dreaming about neural networks, he's enjoying an IPA somewhere on the Chesapeake Bay.

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