Data Science Foundations: Fundamentals

Data Science Foundations: Fundamentals
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