In this guide, we’ll share 65 free data science resources that we’ve hand-picked and annotated for beginners.

To become data scientist, you have a formidable challenge ahead. You’ll need to master a variety of skills, ranging from machine learning to business analytics.

However, the rewards are worth it. Organizations will prize alchemists who can turn raw data into smarter decisions, better products, happier customers, and ultimately more profit. Plus, you’ll get to solve interesting problems and master new, impactful technologies.

If that sounds like a career you’d enjoy, then bookmark this page and read on because we compiled this list just for you.

Data Science Resources

  1. Foundational Skills
    • Programming and Data Wrangling
    • Statistics and Probability
  2. Technical Skills
    • Data Collection
    • SQL
    • Data Visualization
    • Applied Machine Learning
  3. Business Skills
    • Communication
    • Creativity and Innovation
    • Operations and Strategy
    • Business Analytics
  4. Supplementary Skills
    • Natural Language Processing
    • Recommendation Systems
    • Time Series Analysis
  5. Practice
    • Projects
    • Competitions
    • Problem Solving Challenges

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*Note: Advanced, Niche, or Industry-Specific Skills

Certain roles might require other skills, such as:

Deep Learning, Big Data, Optimization, Anomaly Detection, Graph and Network Models, Quantitative Finance, Research Leadership, Project Management, Product Design, Software Engineering, Spacial Data Analysis, etc…

In this guide, we’ll only be covering the skills that are most frequently demanded across the industry.

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1. Foundational Skills

Foundational skills form the basis of true understanding, which will in turn allow you to discover novel solutions, build more accurate models, and make better decisions. Before getting to other topic-specific data science resources, it’s important to lay the groundwork first.

1.1. Programming and Data Wrangling

First, you’ll need to know at least one scripting language well enough to wrangle datasets, prototype models, and perform analyses.

We strongly recommend choosing between Python or R, as they are both open-source (free), widely adopted, and supported by active communities. They each have their own strengths, but we recommend picking just one at the start.

  • Python is more common in software startups, large tech firms, and adTech. Python tends to be more flexible because it’s a general purpose programming language. It’s also better for deep learning and processing data.
  • R / RStudio is popular in research, finance, and analytics. R is a statistical programming language that has mature libraries for econometrics, statistics, and machine learning.
  • We’ve also written a more detailed comparison of Python vs. R for data science.

If you’re still on the fence, we’d recommend starting with Python due to its breadth and flexibility (and it’s a bit more beginner-friendly).

Tip: Each resource link below opens in a new tab, so you won’t lose your place.

Python Resources:

R / RStudio Resources:

1.2. Statistics and Probability

A strong statistics foundation helps you fully understand machine learning, conditional probability, A/B testing, and many other core skills. It also helps you “think like a data scientist,” which include spotting biases, efficiently iterating on predictive models, and knowing how to extract insights from data.

Plus, learning the common probability distributions (especially Gaussian, Binomial, Uniform, Exponential, Poisson) is critical for implementing many real-world applications, such as multi-armed bandits, market-basket analyses, and anomaly detection programs.

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2. Technical Skills

Data science is all about converting raw data into insights, predictions, software, and so on. Therefore, you’ll need to be comfortable working with data.

Core technical skills include collecting, cleaning, managing, and visualizing data, plus the big umbrella of applied machine learning.

2.1. Data Collection

Everything hinges on the quality and quantity of your data. Just as a chemist needs the right chemicals, you’ll need relevant data.

There are 4 common ways to collect data:

  1. Internal Data. This is proprietary data that your company collects through its operations or through partnerships with other providers. This is usually the most relevant data.
  2. Searching Online. Need a labeled set of 8 million videos? There’s a webpage for that… Seriously, you’d be surprised at what you can find out there. Online datasets allow you to prototype before investing in proprietary data.
  3. API’s. API’s allow you to programmatically (and legally) access datasets that other companies collect. You can find anything from Twitter feeds to weather data to financial data.
  4. Web Scraping. Web crawling and scraping is a powerful tool that you must use responsibly. It opens a whole new world, but make sure to respect terms of services.

API Resources:

Web Scraping Resources:

2.2. SQL

SQL is the lingua franca for database management and querying, and you should be able to write complex queries.

Learning SQL also gives a better understanding of relational data in general (i.e. data in “table” format), which will improve your data analysis skills in any language.

2.3. Data Visualization

Data visualization is important for exploratory analysis and for communicating your insights, and no list of data science resources would be complete without this topic.

Raw data can be difficult to interpret, so you’ll need to investigate trends and distributions with plots and charts.

2.4. Applied Machine Learning

Machine learning is a broad umbrella term that contains many sub-tasks. In a nutshell, it’s about teaching computers how to learn patterns and models from data.

To some people, machine learning is synonymous with data science, but we consider it a separate field that heavily overlaps with data science. There’s no doubt that machine learning is a powerful toolset, and it’s the meatiest skill on this list.

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3. Business Skills

Business skills and soft skills are sometimes overlooked in data science curricula, but they are supremely important, and employers will look out for them. Data science is never performed in a vacuum. You’ll need to anticipate business needs, think creatively about solutions, and communicate your insights clearly.

As machine learning libraries mature and algorithms become easier to use “out-of-the-box,” businesses will value people who can work with data and work with people. This section of our list of data science resources will help you stand out.

3.1. Communication

If a tree falls in a forest but no one is around to hear it, does it make a sound? If data is analyzed but no one can explain the results, does it really matter?

Effective communication skills are universal, but data scientists have the added challenge of discussing highly technical or mathematical topics.

During data scientist interviews, you’ll often be asked to “explain a technical concept to a layperson” or “describe a previous project you’ve worked on.” Employers will specifically look for clarity, conciseness, and organization.

  • The best stats you’ve ever seen (TED Talk) – This is an iconic TED talk and a fun display of storytelling with data.
  • Think Fast, Talk Smart (Video) – This is a workshop at the Stanford Graduate School of Business on how to overcome anxiety and speak spontaneously. Not only will this help you for the rest of your career, but it will also allow you to stand out during your interview.
  • 7 Tips for Improving Communication (Video) – Simple, practical tips on how to communicate effectively on a daily basis.
  • How to Win Friends and Influence People (PDF), (Free Audiobook Version) – This is a book we’d recommend for anyone, data scientist or not. While some of the verbiage is a bit dated, the teachings about interpersonal relationships are timeless.
  • Practice teaching a technical concept to a friend – This will help you solidify your understanding of the concept while getting valuable communication practice. Try explaining an interesting machine learning algorithm, including its strengths, weaknesses, and proper use cases.
  • Practice describing projects that you’ve completed – This will help you practice organizing the many moving parts of data science into coherent narratives.

3.2. Creativity & Innovation

Data scientists are hired to build new products, perform complex analyses, and invent valuable ways to use data.

In fact, they rarely solve the same problem twice. Even if you can apply the same methods to an adjacent dataset, you’ll need to be creative about feature engineering, supplemental data, and business implications.

You’ll naturally become a better creative thinker as you gain more experience, but the following resources can help jumpstart your problem-solving and innovation skills.

3.3. Business Operations and Strategy

Here’s a question you should ask yourself every day: “What are some ways I can improve this business?”

At the end of the day, companies don’t hire you to analyze data… they hire you to help them grow or become more profitable. This means that you should have an understand how data can help make better decisions and build better products.

3.4. Business Analytics

Business analytic skills are critical for data scientists in operational roles. Python and R will allow you to perform more complex analyses than Excel can, thanks to the flexibility of programming languages.

After you master the technical tools, building strong domain knowledge will lead to greater business impact.

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4. Supplementary Skills

Supplementary Skills are more situational depending on the role, but they help you become a well-rounded data scientist. Here are data science resources for NLP, recommender systems, and time series analysis.

4.2. Natural Language Processing (NLP)

Natural Language Processing (NLP), or Text Mining, is an exciting sub-field of machine learning for extracting structure, grammar, and insights from text.

Famous applications include Sentiment Analysis, Article Classification, and even teaching a Neural Network to write Shakespeare.

4.3. Recommendation Systems

Recommendation Systems, or Collaborative Filters, are one of the great success stories of data science, especially in e-Commerce. No list of data science resources would be complete without touching on them.

They power many amazing websites and apps, including Amazon, Yelp, Netflix, and Spotify. In a nutshell, recommendation systems find other users who have similar tastes to you to make better recommendations for you. This produces a huge win-win by improving user experience while driving up revenue.

4.3. Time Series Analysis

Time Series Analysis deals with data series that are indexed by time. For example, stock prices, precipitation amounts, and Twitter hashtags by hour would all be considered time series. Time series analysis is commonly used in Finance, Forecasting, and Econometrics.

While much of machine learning deals with “cross-sectional data” (data without regard to differences in time), there are also models specifically designed to handle time series.

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5. Practice

Practice projects have two main purposes:

  1. They help you solidify concepts and practice pulling together all the moving pieces of data science.
  2. They arm you with something tangible to show employers. If a picture is worth 1000 words, a project is worth a million…

By nature, projects are personal undertakings, and you should pick topics you’re interested in. Here are a few places to find project ideas:

And that’s a wrap!