Data Science Foundations: Fundamentals

Course details

Data science is the collection of fields involved in artificial intelligence, machine learning, and business intelligence. It’s one of the fastest growing, most rewarding careers, employing analysts and engineers around the globe. This course provides an accessible, non-technical introduction to the field of data science, covering the vocabulary, skills, jobs, tools, and techniques of data science, and connects it to the data revolution, providing you with a foundation for your continued development in data science.

Join instructor Barton Poulson as he identifies the components that make up data science and examines how data science has grown and evolved, particularly with the advent of generative AI.

Learning objectives
Evaluate the interplay between different branches of data science (AI, ML, deep learning) by analyzing their distinct characteristics, applications, and relationships to solve complex data problems.
Apply ethical principles and regulatory requirements in data science projects by implementing appropriate privacy protections, bias mitigation strategies, and transparency measures.
Design effective data collection and preparation strategies by selecting and utilizing appropriate data sources, tools, and techniques while considering factors like data quality, accessibility, and ethical implications.
Analyze complex datasets using various learning paradigms (supervised, unsupervised, reinforcement) and mathematical foundations to extract meaningful patterns and insights.
Create interpretable and actionable data science solutions by integrating appropriate tools, techniques, and emerging technologies (like foundation models and quantum computing) to address real-world challenges.

Concepts

0. Introduction

01 - Welcome

1. What Is Data Science

02 - Supply and demand for data science
03 - The data science Venn diagram revisited
04 - The evolution of data science
05 - The CRISP-DM framework
06 - Roles, teams, and tools in modern data science
07 - The central 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 and neural networks
11 - Transformers and attention for generative AI
12 - Big data
13 - Predictive analytics
14 - Prescriptive analytics
15 - The evolution of business intelligence

3. Ethics, Privacy, and Regulation

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

4. Sources of Data and Insights

21 - Data preparation
22 - Labeling data for supervised learning
23 - In-house data
24 - Open data
25 - APIs
26 - Scraping data
27 - Synthetic data and simulation environments
28 - Passive collection of training data
29 - Data vendors
30 - New data from surveys and experiments
31 - Data ethics

5. Tools and Techniques for Data Science

32 - Applications for data analysis
33 - Languages for data science
34 - Alternatives to programming - Low-code, no-code, and AutoML
35 - MLOps
36 - Machine learning and AI as a service

6. Math Foundations for Data Science

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

7. Learning Paradigms

42 - Supervised, unsupervised, and reinforcement learning
43 - Descriptive analytics
44 - Clustering techniques
45 - Dimensionality reduction
46 - Anomaly detection
47 - Trend analysis
48 - Aggregating models
49 - Validating models

8. Algorithms That Create

50 - Generative Adversarial Networks (GANs)
51 - Reinforcement learning

9. Acting on Data Science

52 - The importance of interpretability in AI
53 - Techniques for creating interpretable models
54 - Delivering actionable insights

Conclusion

55 - Next steps