**Data Science Foundations: Fundamentals**

English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 3h 41m | 710 MB

eLearning | Skill level: All Levels

Data science is driving a world-wide revolution that touches everything from business automation to social interaction. It’s also one of the fastest growing, most rewarding careers, employing analysts and engineers around the globe. This course provides an accessible, nontechnical overview of the field, covering the vocabulary, skills, jobs, tools, and techniques of data science. Instructor Barton Poulson defines the relationships to other data-saturated fields such as machine learning and artificial intelligence. He reviews the primary practices: gathering and analyzing data, formulating rules for classification and decision-making, and drawing actionable insights. He also discusses ethics and accountability and provides direction to learn more. By the end, you’ll see how data science can help you make better decisions, gain deeper insights, and make your work more effective and efficient.

Topics include:

- The demand for data science
- Roles and teams in data science
- Artificial intelligence
- Big data
- Predictive analytics
- Ethics and agency
- Sources of data and rules
- Data science tools
- Math and data science
- Analysis
- Creating actionable insights

**+ Table of Contents**

**Introduction**

1 The fundamentals of data science

**What Is Data Science **

2 Supply and demand for data science

3 The data science Venn diagram

4 The data science pathway

5 Roles and teams in data science

**The Place of Data Science in the Data Universe**

6 Artificial intelligence

7 Machine learning

8 Deep learning neural networks

9 Big data

10 Predictive analytics

11 Prescriptive analytics

12 Business intelligence

**Ethics and Agency**

13 Legal ethical and social issues of data science

14 Agency of algorithms and decision-makers

**Sources of Data**

15 Data preparation

16 In-house data

17 Open data

18 APIs

19 Scraping data

20 Creating data

21 Passive collection of training data

22 Self-generated data

**Sources of Rules**

23 The enumeration of explicit rules

24 The derivation of rules from data analysis

25 The generation of implicit rules

**Tools for Data Science**

26 Applications for data analysis

27 Languages for data science

28 Machine learning as a service

**Mathematics for Data Science**

29 Algebra

30 Calculus

31 Optimization and the combinatorial explosion

32 Bayes theorem

**Analyses for Data Science**

33 Descriptive analyses

34 Predictive models

35 Trend analysis

36 Clustering

37 Classifying

38 Anomaly detection

39 Dimensionality reduction

40 Feature selection and creation

41 Validating models

42 Aggregating models

**Acting on Data Science**

43 Interpretability

44 Actionable insights

**Conclusion**

45 Next steps

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