Data Science Foundations: Fundamentals (2022)

Course details

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.

Learning objectives
Assess the skills required for a career in data science.
Evaluate different sources of data, including metrics and APIs.
Explore data through graphs and statistics.
Discover how data scientists use programming languages such as R, Python, and SQL.
Assess the role of mathematics, such as algebra, in data science.
Assess the role of applied statistics, such as confidence intervals, in data science.
Assess the role of machine learning, such as artificial neural networks, in data science.
Define the components of effective data visualization.

Concepts

0. Introduction

01 - Getting started

1. What Is Data Science

02 - Supply and demand for data science
03 - The data science Venn diagram
04 - The data science pathway
05 - The CRISP-DM model in data science
06 - Roles and teams in data science
07 - The role of questions in data science

2. The Place of Data Science in the Data Universe

08 - Artificial intelligence
09 - Machine learning
10 - Deep learning neural networks
11 - Big data
12 - Predictive analytics
13 - Prescriptive analytics
14 - Business intelligence

3. Ethics and Agency

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

4. 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

5. Sources of Rules

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

6. Tools for Data Science

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

7. Mathematics for Data Science

38 - Sampling and probability
39 - Algebra
40 - Calculus
41 - Optimization and the combinatorial explosion
42 - Bayes' theorem

8. Unsupervised Learning

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

9. 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

10 - Generative Methods in Data Science

54 - Generative adversarial networks (GANs)
55 - Reinforcement learning

11. Acting on Data Science

56 - The importance of interpretability
57 - Interpretable methods
58 - Actionable insights

Conclusion

59 - Next steps and additional resources