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Think Like a Data Scientist

  • Course Code: Data Science - Think Like a Data Scientist
  • Course Dates: Contact us to schedule.
  • Course Category: Big Data & Data Science Duration: 3 Days Audience: This course is geared for those who wants 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.

Course Snapshot 

  • Duration: 3 days 
  • Skill-level: Foundation-level Data Science skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for those who wants 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. 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation. 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

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. 

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. 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • The data science process, step-by-step 
  • How to anticipate problems 
  • Dealing with uncertainty 
  • Best practices in software and scientific thinking 

Audience & Pre-Requisites 

This course is designed for those who wants 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. 

Pre-Requisites:  Students should have familiar with  

programming skills  

knowledge of basic statistics 

Course Agenda / Topics 

  1. Philosophies of data science  
  • 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 
  1. Setting goals by asking good questions 
  • Listening to the customer 
  • Ask good questions—of the data 
  • Answering the question using data 
  • Setting goals 
  • Planning: be flexible 
  1. Data all around us: the virtual wilderness 
  • Data as the object of study 
  • Where data might live, and how to interact with it 
  • Scouting for data 
  • Example: microRNA and gene expression 
  1. Data wrangling: from capture to domestication 
  • Case study: best all-time performances in track and field 
  • Getting ready to wrangle 
  • Techniques and tools 
  • Common pitfalls 
  1. Data assessment: poking and prodding 
  • Example: the Enron email data set 
  • Descriptive statistics 
  • Check assumptions about the data 
  • Looking for something specific 
  • Rough statistical analysis 
  1. Developing a plan 
  • What have you learned? 
  • Reconsidering expectations and goals 
  • Planning 
  • Communicating new goals 
  1. Statistics and modeling: concepts and foundations 
  • How I think about statistics 
  • Statistics: the field as it relates to data science 
  • Mathematics 
  • Statistical modeling and inference 
  • Miscellaneous statistical methods 
  1. Software: statistics  
  • Spreadsheets and GUI-based applications 
  • Programming 
  • Choosing statistical software tools 
  • Translating statistics into software 
  1. Supplementary software: bigger, faster, more efficient 
  • Databases 
  • High-performance computing 
  • Cloud services 
  • Big data technologies 
  • Anything as a service 
  1. Plan execution: putting it all together 
  • 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 
  1. Delivering a product 
  • Understanding your customer 
  • Delivery media 
  • Content 
  • Example: analyzing video game play 
  1. After product delivery: problems and revisions 
  • Problems with the product and its use 
  • Feedback 
  • Product revisions 
  1. Wrapping up: putting the project away 
  • Putting the project away neatly 
  • Learning from the project 
  • Looking toward the future 
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