Note from the Editor: Take Two
In the old days of 2013, the OSDSM was born. Then, there were "little to no Data Scientists with 5 years experience, because the job simply did not exist." (David Hardtke, Nov 2012) Since then, history has witnessed many things, including:
• Data Scientists working across industries and the world
• social media manipulation disrupts many elections
• BLM and #metoo and Extinction Rebellion and many other social movements
• machine learning begins falling under engineering domain
• a pandemic
• climate change disasters becoming very frequent while climate warms faster than predicted
• remote work becoming common
• multiple global recession shocks
In that decade, Data Science has seen growth of jobs, shortfall of goals, success in many industries, abject failure in others, and nefarious use cases. In particular, adverse consequences and complications of learning from data appear in too many examples: elections undermined by psychographics, dismal gender (Men=74%) and BIPOC diversity in the AI field, a revived eugenics, an explainability crisis, facial recognition used to identify people and systematically detain them, "aggression" detection microphones in schools, and many others. It has never been more clear that we need to talk about the real world impacts of our work, and consider how our creations are used. As you consider this, read a prescient novel that grapples with the consequences of birthing, of creation, of technology.
Like any tool, data-driven technologies are indifferent to the morality of their ends. Perhaps the greatest risk of all is leaving this tool in the hands of the few expensively-educated people who cannot possibly represent all of us. To balance this, open source movements seek to lower the barriers to education for everyone. Data science and data literacy must be widespread, accessible, and leveraged for building our collective future. More than ever, we need that future to be built by members of society who are diverse and focused on generative, sustainable, resilient, emergent solutions. After all, the things we build are mirrors of ourselves (seriously, read Shelley's Frankenstein).
Computers reflect the biases and belief systems of the people programming them -@alicegoldfuss
The OSDSM is built with the belief that open source education makes a diverse, collective, generative future-building possible. I hope that you are one of the next people -- whether you call yourself a Data Scientist or not -- to help make better decisions with the scientific process, critical thinking, and everything else your unique perspective brings to the table. This rewritten curriculum focuses on what is needed to be successful in the entry-level role, but that is just a generic outline; truly, I hope where you take it extends far beyond that.
Start here 👇
The open-source curriculum for learning to be a Data Scientist. Curriculum resources from both universities and working Data Scientists focuses on foundational theory and applied skills. The OSDSM is collectively-maintained and open to PRs.
The goal of this curriculum is to prepare the student for an entry level Data Scientist role, using open source materials, at no cost but with the same calibur of materials found in the most reputable paid programs. Books not offered for free are often available through a public library, also indicated here with current list price. The Masters is self-guided and self-accredited. To better support credibility, the structure now includes a Capstone project intended to demonstrate the student's problem solving approach, skills in execution, and communication. Upon completion, the student can award oneself a Credential on LinkedIn from the Open Source Data Science Masters. As with all things, the OSDSM is best played as a team sport (try finding people on r/learndatascience).
This is called a "Masters" because it is primarily concerned with "upper-level" college course material in mathematics, programming, economics, or related disciplines. Come as you are!
- 📖 The Core - This is a critical foundation for what is to come; don't skip the foundational lessons.
- ❄️ Specialty - Choose what is most interesting to you, or most relevant to the work you plan to do.
- 🤝 Doing Data Science - Learn about how doing science with others and for businesses can work.
- 🧑💻 Capstone Project - Choose a meaningful project or dataset to demonstrate what you've learned.
This is a critical foundation for what is to come; don't skip!
One could argue that "Data Science" is a recent term for an already existing information analysis discipline. Humans instinctually search for patterns, a purpose we also see in this more digitized discipline. Read different sources (and search beyond this list) about the uses of data science.
- The Signal and The Noise / Nate Silver Book
$18-- Narrated cases of Data Science at play in the real world. - Dataclysm: Who We Are (When We Think No One's Looking) / Christian Rudder Book
$17-- From the inside of OKCupid, real examples of how data science can illustrate human behavior. - Informatics of the Oppressed / Rodrigo Ochigame Logic Magazine -- Algorithms of oppression have been around for a long time. So have radical projects to dismantle them and build emancipatory alternatives.
- A showcase of Jupyter Python Data Analysis Notebooks across disciplines.
When there are no answers in the back of the book, how do you proceed? Breaking down problems is a skill, one that can and should be learned. Follow Pólya's process, and for extra credit, seek out resources on computer science decomposition.
- Problem-Solving Heuristics "How To Solve It" George Pólya Berkeley / Summary Book
$18
It is crucial as a Data Scientist that you show integrity in and transparency of scientific process. Even if you've been here before, review and draw out the process diagram for the scientific method.
Get familiar and comfortable with manipulating data in a database with a common relational querying language. There are diverse query languages, but SQL is a widely used foundation.
- SQL School Mode Analytics / Tutorials
The foundational mathematics for working with large samples of data. Spend time in exercises until you feel highly confident in the key topics of Linear Algebra. It will serve you well.
- An Intuitive Guide to Linear Algebra Better Explained / Article
- A Programmer's Intuition for Matrix Multiplication Better Explained / Article
- Vector Calculus: Understanding the Cross Product Better Explained / Article
- Vector Calculus: Understanding the Dot Product Better Explained / Article
- Linear Algebra Khan Academy / Videos
How can we answer questions with data? Everywhere you look, you'll see methods from statistics. Spend a lot of time here!
- Stats in a Nutshell Book
$46 - Think Stats: Probability and Statistics for Programmers Digital & Book
$34 - Think Bayes Digital & Book
$25 - Probabilistic Programming and Bayesian Methods for Hackers Github / Tutorials
If you're starting from scratch with Python, start with this series.
Set up your computer to use tools locally.
- Installing Basic Packages: Python, virtualenv, NumPy, SciPy, matplotlib and IPython
- For scientific uses: Using Python Scientifically & Command Line Install Script for Scientific Python Packages
Get familiar with using tools to do data analysis. Pro tip: Write out what you're going to do before you do it! When you hit a snag, return to your plan and rechart as necessary.
- pandas tutorials
- Pandas Cookbook Examples
- Data Analysis in Python Tutorial
- Big Data Analysis with Twitter UC Berkeley / Lectures
- Intro to Data Science / Course $0
How does a computer know what to do? Algorithms are instructions with a fancy name. Learn how instructions are encoded, how to think about structuring those instructions, and patterns for making it work in code.
- Algorithms Design & Analysis I Stanford / Coursera
- numpy Tutorial / Stanford CS231N
Courses with many of the topics above included. Be sure you fill in any gaps!
- Intro to Data Science / University of Washington Lectures
- (Short Survey) Doing Data Science: Straight Talk from the Frontline O'Reilly / Book
$50
Choose what is most interesting to you, or most relevant to the work you plan to do.
A branch of statistics that uses graphical models and specialized statistics to describe and model cause and effect.
The imperfect and immensely useful art (science?) of transforming human language into data.
- From Languages to Information / Stanford CS147 Materials
- NLP with Python (NLTK library) Digital, Book
$55 - How to Write a Spelling Correcter / Norvig Tutorial
Human relationships can be modeled as a network or graph. Many other things suit this model, too. Working with graphs
- Social and Economic Networks: Models and Analysis / Stanford / Coursera
- Social Network Analysis for Startups Chapter 1 & Book
$25networkx
This is a huge space with infinite things to learn. For advanced statistical foundation, see The Elements of Statistical Learning.
- Intro to scikit-learn, SciPy2013 youtube tutorials
- Machine Learning for Hackers ipynb / digital book
- Machine Learning Ng Stanford / Coursera & Stanford CS 229
- Programming Collective Intelligence Book
$46
The most persuasive data stories are ones you can see with your own eyes. Make it visual!
- Data Visualization University of Washington / Slides & Resources
- Rice University's Data Viz class Rice University / Slides
- Envisioning Information Tufte / Book
$36 - Interactive Data Visualization for the Web / Scott Murray Online Book & Book
$50
If you have interest in operations management, manufacturing, supply chains, or other real world queuing problems, dig in here.
- Linear Programming (Math 407) University of Washington / Course
- Convex Optimization / Boyd Stanford / Lectures / Book
- Neural Networks Andrej Karpathy / Python Walkthrough
- Deep Learning for Natural Language Processing CS224d Stanford
Learn about how doing science with others and for businesses can work.
In ideal terms, a Data Scientist advises strategic decision-making using data-backed analysis and tested hypotheses. YMMV as this depends on the company needs and the team being supported.
- What Professional Data Scientists Actually Do Video
- The Data Science Handbook: Advice and Insights from 25 Amazing Data Scientists Book
$25 - Required reading: Why might machine learning not always the best approach? Machine Learning: The High-Interest Credit Card of Technical Debt
For a Data Scientist's work to be impactful, they must be effective at communicating their work and findings. In any setting, clear logic and effective business writing are crucial to reaching your audience. And of course, doing Data Science with a team over zoom is different from being in person in an office. There is much more written communication and asynchronous consumption of content in the remote office environment. More than ever, writing and communication skills are crucial to being an effective Data Scientist for yourself and your team.
- LEADERSHIP LAB: The Craft of Writing Effectively UChicago / Video. Recommend watching this twice and taking notes.
In the modern organization, it is very rare that a Data Scientist works in isolation. Communicating the value of the work being done is crucial to getting buy-in from partners whose decisions and operations depend on your work. Those partners might be:
- Product Managment
- Engineering
- Design (User Experience, Research, Product)
- Operations (Project Management, Customer Service Agents, Data Management)
- Marketing
- Finance Operations
- etc.
Typically, the more clearly you are able to communicate the "why", the value of what you are doing, the more these teams will be able to support you and your work in conversations you may not be a part of. Even if others don't understand "how" you do your work (which is very important to you and your manager!), they will be able to understand and repeat a well-communicated "why". This is why we write Specs, to get buy-in and allow for questions or input, before the work starts.
A document conveying the motives, direction, investment, and expected value of the work.
- Goal / "Why" -- What is the point of this work? What decision is the organization trying to make?
- Impact -- What decisions might be made differently as a result of this work? What is the expected value?
- Data -- What evidence will this draw on?
- Assumptions -- What evidence does not exist? What assumptions are necessary or agreed upon?
- Methods / "How" -- Overview methods expected to be used. Analysis, with what tools? Experimentation, with what methodology?
- Results -- (to be filled in as completed)
A slide deck or document with the goal of conveying the results of the work and how the findings support an important decision(s).
Best appended to the Spec, and summarized in a slide deck for easy consumption. Depending on the culture of the group, slides or a short document may be easier to look through to understand the results of the work. In the remote work era, think about how your work will be passed around and make sure your "above the fold" is easy to understand and clearly conveys the "why" and results in particular.
Example: A particularly polished presentation of map quality study results showing higher data quality in US maps on OSM than commercially available alternatives. The impact of this work was a) increased confidence in service reliability and b) enabled the company to decide against buying a commercially available annual license costing ~$10mi/yr.
Choose a meaningful project or dataset to demonstrate what you've learned.
- The very detailed data is plural
- Collection of datasets
Review the earlier reading on The Scientific Process. Formulate a clear, concise hypothesis. This is the headliner of your Spec, flesh that out.
Show the process you used to disprove your hypothesis, preferably in a jupyter notebook. See examples to get a taste of how you can showcase your work.
- Create a document or github repo showcasing the list of courses and materials you completed. Include your project materials. Also recommended: include a personal statement about why you chose this course of study and what you seek to do with it.
- Award yourself a Credential on LinkedIn from The Open Source Data Science Masters, with a link to the documentation you created.
- Congratulations! 🎉
- The Elements of Statistical Learning / Stanford Digital & Book
$90& Study Group - Python Data Science Handbook: Essential Tools for Working with Data Book
$60 - The Manga Guide to Linear Algebra Book
$19 - Mining The Social Web Book
$46 - The Truthful Art: Data, Charts, and Maps for Communication Cairo / Book
$50 - Exploratory Data Analysis Tukey / Book
$81$113 - Mining Massive Data Sets / Stanford Course & Digital Textbook & Book
$58 - Introduction to Information Retrieval / Stanford Digital & Book
$70 - Data Science in IPython Notebooks (Linear Regression, Logistic Regression, Random Forests, K-Means Clustering)
- Probabilistic Graphical Models Stanford / Coursera
- Differential Equations in Data Science Python Tutorial
- Algorithm Design, Kleinberg & Tardos Book
$125 - Tidy Data in Python
- Designing, Visualizing and Understanding Deep Neural Networks Berkeley CS294-129
- Python for Data Analysis Book
$55 - Think Python Digital & Book
$45 - The Visual Display of Quantitative Information Tufte / Book
$27 - Information Dashboard Design: Displaying Data for At-a-Glance Monitoring Stephen Few / Book
$29 - D3 Library / Scott Murray Blog / Tutorials
- SQL Tutorials SQLZOO / Tutorials
- Machine Learning Caltech / Edx
- A Course in Machine Learning UMD / Digital Book
- Restructured ala the 2022 Plan.
- Pruned broken links. It's been a while, and some of these resources have moved -- or worse -- been taken down.
- Pared down links to a more opinionated list.
- Proceeds. Bookshop.org links for all books, which supports independent bookshops with commissions. Since the first commits in 2014, I have donated any related commissions to Planned Parenthood, which was one of the few healthcare providers in my community growing up and is the largest single provider of reproductive health services in the US. Though donations should flow to independent bookshops from now on, my personal commitment to PP remains.
Please Contribute; this is Open Source!
Fearless Maintainer: @clarecorthell
RIP v1.0 commit