Learning Data Science: Understanding the Basics
1h 16mBeginner2016-11-16
Authors

Doug Rose
Teaching Fortune 500s and professionals how to lead change
Course details
Many of the people who work on data science teams won't be data scientists. They'll be the managers and associates who want to gain real business value from your organization's data. These team members need to understand the language of data science so they can ask better questions, understand processes, and help effectively lead their teams and organizations to making better data-driven decisions. This course is an introduction to data science for people who aren't planning on being full-time data scientists. It introduces big data concepts, tools, and techniques, including gathering and sorting data, working with databases, understanding structured and unstructured data types, and applying statistical analysis. Business coach and author Doug Rose helps you speak the language of data science so that you can guide your organization through the opportunities and limitations in this dramatically growing field.
Learning objectives
What is data science?
Making connections with relationship databases
Importing data into warehouses
Recognizing different data types
Applying statistical analysis
Focusing on knowledge
Learning objectives
What is data science?
Making connections with relationship databases
Importing data into warehouses
Recognizing different data types
Applying statistical analysis
Focusing on knowledge
Skills covered
Data Science FoundationsTech Career SkillsFoundationsCybersecurityCloud ComputingData ScienceSoftware Development
Concepts
0. Introduction
- 01 - Welcome
1. What Is Data Science
- 02 - Define a multidisciplinary practice with multiple meanings
- 03 - Use statistics and software
- 04 - Uncover insights and create knowledge
2. Working with Databases
- 05 - Make connections with relational databases
- 06 - Get data into warehouses using ETL
- 07 - Let go of the past with NoSQL
- 08 - Address big data problems
3. Recognizing Different Data Types
- 09 - Keep things simple with structured data
- 10 - Share semistructured data
- 11 - Collect unstructured data
- 12 - Sift through big garbage
4. Applying Statistical Analysis
- 13 - Start out with descriptive statistics
- 14 - Understand probability
- 15 - Find a correlation
- 16 - See how correlation does not imply causation
- 17 - Comb techniques for predictive analytics
5. Avoiding Pitfalls
- 18 - Focus on knowledge
Conclusion
- 19 - Next steps
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