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Data Sciences

Think Like a Data Scientist

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Working in a hands-on learning environment, led by our Data Science expert instructor, students will learn about and explore: you'll learn to combine analytic, programming, and business perspectives into a repeatable process for extracting real knowledge from data. you'll discover (or remember) valuable statistical techniques and explore powerful data science software. you'll put this knowledge together using a structured process for data science.

PRIVATE
Course Access

Unlimited Duration

Last Updated

July 29, 2021

Students Enrolled

20

Total Reviews

Posted by

Ernesto Lee

Certification

Working in a hands-on learning environment, led by our Data Science expert instructor, students will learn about and explore: · you'll learn to combine analytic, programming, and business perspectives into a repeatable process for extracting real knowledge from data. · you'll discover (or remember) valuable statistical techniques and explore powerful data science software. · you'll put this knowledge together using a structured process for data science.

Course Curriculum

    • Data science and this course 00:00:00
    • Awareness is valuable 00:00:00
    • Developer vs. data scientist 00:00:00
    • Do I need to be a software developer? 00:00:00
    • Do I need to know statistics? 00:00:00
    • Priorities: knowledge first, technology second, opinions third 00:00:00
    • Best practices 00:00:00
    • Listening to the customer 00:00:00
    • Ask good questions—of the data 00:00:00
    • Answering the question using data 00:00:00
    • Setting goals 00:00:00
    • Planning: be flexible 00:00:00
    • Data as the object of study 00:00:00
    • Where data might live, and how to interact with it 00:00:00
    • Scouting for data 00:00:00
    • Example: microRNA and gene expression 00:00:00
    • Case study: best all-time performances in track and field 00:00:00
    • Getting ready to wrangle 00:00:00
    • Techniques and tools 00:00:00
    • Common pitfalls 00:00:00
    • Example: the Enron email data set 00:00:00
    • Descriptive statistics 00:00:00
    • Check assumptions about the data 00:00:00
    • Looking for something specific 00:00:00
    • Rough statistical analysis 00:00:00
    • What have you learned? 00:00:00
    • Reconsidering expectations and goals 00:00:00
    • Planning 00:00:00
    • Communicating new goals 00:00:00
    • How I think about statistics 00:00:00
    • Statistics: the field as it relates to data science 00:00:00
    • Mathematics 00:00:00
    • Statistical modeling and inference 00:00:00
    • Miscellaneous statistical methods 00:00:00
    • Spreadsheets and GUI-based applications 00:00:00
    • Programming 00:00:00
    • Choosing statistical software tools 00:00:00
    • Translating statistics into software 00:00:00
    • High-performance computing 00:00:00
    • Cloud services 00:00:00
    • Big data technologies 00:00:00
    • Anything as a service 00:00:00
    • Tips for executing the plan 00:00:00
    • Modifying the plan in progress 00:00:00
    • Results: knowing when they’re good enough 00:00:00
    • Case study: protocols for measurement of gene activity 00:00:00
    • Understanding your customer 00:00:00
    • Delivery media 00:00:00
    • Content 00:00:00
    • Example: analyzing video game play 00:00:00
    • Problems with the product and its use 00:00:00
    • Feedback 00:00:00
    • Product revisions 00:00:00
    • Putting the project away neatly 00:00:00
    • Learning from the project 00:00:00
    • Looking toward the future 00:00:00

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