Data Science Foundations: Fundamentals

Data Science Foundations: Fundamentals

English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 5h 17m | 2.78 GB

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.

Table of Contents

Introduction
1 Getting started

What Is Data Science
2 Supply and demand for data science
3 The data science Venn diagram
4 The data science pathway
5 The CRISP-DM model in data science
6 Roles and teams in data science
7 The role of questions in data science

The Place of Data Science in the Data Universe
8 Artificial intelligence
9 Machine learning
10 Deep learning neural networks
11 Big data
12 Predictive analytics
13 Prescriptive analytics
14 Business intelligence

Ethics and Agency
15 Bias
16 Security
17 Legal
18 Explainable AI
19 Agency of algorithms and decision-makers

Sources of Data
20 Data preparation
21 Labeling data
22 In-house data
23 Open data
24 APIs
25 Scraping data
26 Creating data
27 Passive collection of training data
28 Self-generated data
29 Data vendors
30 Data ethics

Sources of Rules
31 The enumeration of explicit rules
32 The derivation of rules from data analysis
33 The generation of implicit rules

Tools for Data Science
34 Applications for data analysis
35 Languages for data science
36 AutoML
37 Machine learning as a service

Mathematics for Data Science
38 Sampling and probability
39 Algebra
40 Calculus
41 Optimization and the combinatorial explosion
42 Bayes’ theorem

Unsupervised Learning
43 Supervised vs. unsupervised learning
44 Descriptive analyses
45 Clustering
46 Dimensionality reduction
47 Anomaly detection

Supervised Learning
48 Supervised learning with predictive models
49 Time-series data
50 Classifying
51 Feature selection and creation
52 Aggregating models
53 Validating models

Generative Methods in Data Science
54 Generative adversarial networks (GANs)
55 Reinforcement learning

Acting on Data Science
56 The importance of interpretability
57 Interpretable methods
58 Actionable insights

Conclusion
59 Next steps and additional resources

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