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Course Skill Level:

Foundational to Intermediate

Course Duration:

1 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    Big Data & Data Science

  • Course Code:


Who should attend & recommended skills:

Those with basic IT, programming, & Linux skills

Who should attend & recommended skills

  • This course is geared for those who want to create insight from chaos and discover important analytic patterns – to set your business on the right track.
  • Skill-level: Foundation level Data Jungle skills for Intermediate skilled team members. This is not a basic class.
  • IT skills: Basic to Intermediate (1-5 years’ experience)
  • Programming skills: Basic (1-2 years’ experience)
  • Linux: Basic (1-2 years’ experience), including familiarity with command-line options such as ls, cd, cp, and su

About this course

Exploring the Data Jungle: Finding, Preparing, and Using Real-World Data is a collection of three hand-picked lessons introducing you to the often-overlooked art of putting unfamiliar data to good use. Brian Godsey, author of Think Like a Data Scientist, has selected these lessons to help you navigate data in the wild, identify and prepare raw data for analysis, modeling, machine learning, or visualization. As you explore the data jungle, you’ll discover real-world examples in Python, R, and other languages suitable for data science.

Skills acquired & topics covered

  • Working in a hands-on learning environment, led by our Data Jungle expert instructor, students will learn about and explore:
  • Need for the right perspective and guidance. (There’s no point hacking at overgrowth with a spoon after all!) Identify and prepare your data well
  • Create insight from chaos and discover important analytic patterns – to set your business on the right track
  • Data All Around Us: The Virtual Wilderness
  • Exploring Data
  • Real-world Data

Course breakdown / modules

  • Data as the object of study
  • Where data might live, and how to interact with it
  • Scouting for data
  • Example: microRNA and gene expression

  • Using summary statistics to spot problems
  • Spotting problems using graphics and visualization

  • Getting started: data collection
  • Preprocessing the data for modeling
  • Using data visualization
  • Terms from this lesson