Who is the 2-day bootcamp for?


The training is designed primarily to give your a head start in data science and data-driven decision making. Our typical attendees for the training range from beginners in data science, engineering/technical managers, business leaders, program/project/product/marketing managers and engineers who want to learn the fundamentals.

  • Working professionals in product/program/project management roles working on building innovative data science products
  • Marketing professionals who want to take a data-driven approach to marketing
  • Managers and business leaders who want to understand what is possible and where the technology puck is going
  • Technical/Engineering managers are leading teams building machine learning and AI products
  • Anyone wanting to get a head start in their data science career without getting into the details of programming

Will I be a good fit for the 2-day course?

Our program is specifically designed for working professionals who want to add some data science to their current positions, or who just want to learn what this new field is all about. We focus on teaching the concepts and underlying knowledge needed to do data science, rather than on platform-specific techniques.

What are the prerequisites?

This program has absolutely no prerequisites. You only need to come with a willingness to learn and a business-savvy mindset.

How is this different from the 5-Day Data Science & Data Engineering Bootcamp?

This is a training based on essential data science skills for non-programmers that is only two days. Our 5-day data science bootcamp teaches more complex skills in programs like R, Azure and Hadoop.

What will I learn?

After the 2-day training, students will be able to:

  • Understand the end-to-end structure of a data science pipeline.
  • Understand the best practices for handling data, and why a good machine learning model depends on the quality of data.
  • Set up an evaluation and metrics pipeline and discuss common pitfalls and how they can be avoided.
  • Understand the different types of metrics and know when to use each one offline and online.
  • Understand the fundamental concepts of data mining.
  • Explore and visualize data with summary statistics, histograms, boxplots, and more!
  • Understand the business problems that can be solved with machine learning.
  • Learn about the machine learning techniques we can use to solve the identified business problems.
  • Understand the business problem and create a machine learning algorithm for the solution.
  • Discuss the ethical dimensions of data science and understand the regulatory issues facing the industry.
  • Speak the data science language: Big Data, Hadoop, MapReduce, Spark, IoT, and Real-Time Analytics.
  • Understand the Apache Spark project and how various vendors, like Microsoft Azure and AWS are different.

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