Fueling innovative software
July 15-18, 2019
Portland, OR

Demystifying data science

Grishma Jena (IBM)
11:00am–11:40am Wednesday, July 17, 2019
Building Data-Intensive Applications
 Location: D138-140
Secondary topics:  Data Driven
Average rating: ****.
(4.25, 12 ratings)

Who is this presentation for?

  • Software engineers, data enthusiasts, and budding data scientists

Level

Beginner

Description

Today’s world generates different kinds of data at unbelievably rapid rates. This has resulted in a shift away from traditional software development toward data science.

Grishma Jena provides an overview of data science and delves deep into the pipeline data scientists use—from fetching the data and creating models to gaining insights and telling a story. You’ll learn some trivia about data and why we need data science; you’ll also explore the capabilities of data science with use cases and get an overview of the data science pipeline, as Grishma details the questions that data science can answer, including data wrangling and cleaning, data exploration, building models and using algorithms, data visualization and storytelling, and common tools. You’ll leave with some additional resources.

What you'll learn

  • Gain a better grasp of the capabilities and processes of data science
  • Learn the general structure of a data science pipeline
  • Develop a strong foundation to continue learning and experimenting in data science
Photo of Grishma Jena

Grishma Jena

IBM

Grishma Jena is a data scientist on the UX research and design team at IBM Data & AI in San Francisco. She works across portfolios in conjunction with the user research and design teams and uses data to understand users’ struggles. Previously, she was a mentor for the nonprofit AI4ALL’s AI Project Fellowship, where she guided a group of high school students on using AI for prioritizing 911 EMS calls. Grishma also teaches Python at the San Francisco Public Library. She enjoys delivering talks and is passionate about encouraging women and youngsters in technology. She holds a master’s degree in computer science from the University of Pennsylvania. Her research interests include machine learning and natural language processing.