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Home » Technology » Big Data & Data Science » Think Like a Data Scientist

Course Skill Level:

Foundational

Course Duration:

3 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    Big Data & Data Science

  • Course Code:

    TLADS0L21E09

Who should attend & recommended skills:

Those with basic programming and statistics skills

Who should attend & recommended skills

  • Those seeking a step-by-step approach to data science, combining analytic, programming, and business perspectives into easy-to-digest techniques and thought processes for solving real world data-centric problems.
  • Skill-level: Foundation-level Data Science skills for Intermediate skilled team members. This is not a basic class.
  • Programming skills: Basic (1-2 years’ experience)
  • Statistics: Basic (1-2 years’ experience)

About this course

Think Like a Data Scientist teaches you a step-by-step approach to solving real-world data-centric problems. By breaking down carefully crafted examples, you’ll learn to combine analytic, programming, and business perspectives into a repeatable process for extracting real knowledge from data. As you read, you’ll discover (or remember) valuable statistical techniques and explore powerful data science software. More importantly, you’ll put this knowledge together using a structured process for data science. When you’ve finished, you’ll have a strong foundation for a lifetime of data science learning and practice.

Skills acquired & topics covered

  • Combining analytic, programming, and business perspectives into a repeatable process for extracting real knowledge from data.
  • Valuable statistical techniques and explore powerful data science software.
  • Using a structured process for data science.
  • The data science process, step-by-step
  • How to anticipate problems
  • Dealing with uncertainty
  • Best practices in software and scientific thinking

Course breakdown / modules

  • Data science and this course
  • Awareness is valuable
  • Developer vs. data scientist
  • Do I need to be a software developer?
  • Do I need to know statistics?
  • Priorities: knowledge first, technology second, opinions third
  • Best practices

  • Listening to the customer
  • Ask good questions-of the data
  • Answering the question using data
  • Setting goals
  • Planning: be flexible

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

  • Case study: best all-time performances in track and field
  • Getting ready to wrangle
  • Techniques and tools
  • Common pitfalls

  • Example: the Enron email data set
  • Descriptive statistics
  • Check assumptions about the data
  • Looking for something specific
  • Rough statistical analysis

  • What have you learned?
  • Reconsidering expectations and goals
  • Planning
  • Communicating new goals

  • How I think about statistics
  • Statistics: the field as it relates to data science
  • Mathematics
  • Statistical modeling and inference
  • Miscellaneous statistical methods

  • Spreadsheets and GUI-based applications
  • Programming
  • Choosing statistical software tools
  • Translating statistics into software

  • Databases
  • High-performance computing
  • Cloud services
  • Big data technologies
  • Anything as a service

  • Tips for executing the plan
  • Modifying the plan in progress
  • Results: knowing when they’re good enough
  • Case study: protocols for measurement of gene activity

  • Understanding your customer
  • Delivery media
  • Content
  • Example: analyzing video game play

  • Problems with the product and its use
  • Feedback
  • Product revisions

  • Putting the project away neatly
  • Learning from the project
  • Looking toward the future

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