Learning Data Science

Course details

Many people who work on data science teams will become something other than data scientists. That said, many will become 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. In this course, get an introduction to data science for people who aren't planning on working as full-time data scientists. Explore big data concepts, tools, and techniques, including gathering and sorting data, working with databases, understanding structured and unstructured data types, applying statistical analysis, asking critical questions, and telling stories about data. 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
Understand the role of a data scientist.
Work with various database systems.
Classify and manage different types of data.
Apply statistical analysis techniques.
Communicate data insights effectively.

Concepts

0. Introduction

01 - Exploring data science

1. What Is Data Science

02 - A multidisciplinary practice with multiple meanings
03 - Using statistics and software
04 - Uncovering insights and creating knowledge

2. Working with Databases

05 - Making connections with relational databases
06 - Getting data into warehouses using ETL
07 - Letting go of the past with NoSQL
08 - Addressing a big data problem

3. Recognizing Different Data Types

09 - Keeping things simple with structured data
10 - Sharing semistructured data
11 - Collecting unstructured data
12 - Sifting through big garbage

4. Statistical Analysis

13 - Starting out with descriptive statistics
14 - Understanding probability
15 - Finding a correlation
16 - Correlation does not imply causation
17 - Combing techniques for predictive analytics

5. Critical Thinking

18 - Harness the power of questions
19 - Gold panning
20 - Focus on reasoning
21 - Test your reasoning

6. Encourage Questions

22 - Run question meetings
23 - Identify question types
24 - Organize your questions
25 - Create question trees
26 - Find new questions

7. Challenge Assumptions

27 - Clarify key terms
28 - Root out assumptions
29 - Find errors
30 - Challenge evidence
31 - See other causes
32 - Uncover misleading statistics
33 - Highlight missing data

8. Tell Stories

34 - Defining a story
35 - Spinning a story
36 - Weaving a story together
37 - Using story structure
38 - Introducing plot
39 - Presenting conflict

9. Engaging the Audience

40 - Defining details
41 - Reporting isn't telling
42 - Knowing your audience
43 - Believing what you say

10. Using Data Visuals

44 - Working with data
45 - Introducing visuals
46 - Eliminating distractions

11. Motivating Action

47 - Using metaphors
48 - Setting a vision
49 - Motivating the audience

Conclusion

50 - Next steps and additional resources