Table of Contents

Introduction

The college route

Self-study

Building a foundation

1. Massive Open Online Courses (MOOCs)

2. Textbooks

3. Youtube

Practice makes perfect

4. Kaggle

5. Internships

6. Medium

7. LinkedIn

8. Github

9. Discord

10. SkillCamper

Conclusion

Introduction

Becoming a data scientist can feel like a lot, especially when you're trying to meet all the data science requirements. From learning programming and stats to figuring out machine learning and handling data, there’s a lot to get a handle on. But don’t worry — with the right resources, you can pick up the skills you need without feeling overwhelmed. In this guide, we’ll break down 10 super helpful resources to help you hit those key data science requirements, whether you’re just starting or looking to level up your career.

Data science was conceived when the traditional fields of statistics and math were enhanced with digital advancements. As data science became more accessible and less tedious, more and more fields began to see data science as a viable tool.

Today, it’s never been easier, whether you choose a formal education in data science or learn it yourself.

The college route

Before data science education became a formal college degree, people learned it through a mix of different fields. They’d often start with a background in areas like computer science, math, or statistics. From there, they’d pick up extra skills as needed—learning programming (like Python or R), studying databases, and digging into machine learning as it started becoming a thing.

A lot of people learned on the job, too. They’d be working as statisticians, analysts, or in tech, and over time they started using more data-driven techniques. As the field grew, so did online resources, with things like MOOCs (Massive Open Online Courses), books, and blogs becoming popular ways to teach yourself data science.

Today, colleges have incorporated data science either as minor or major subjects at both graduate and postgraduate levels.

For postgraduate-level courses, most will require an undergraduate degree in an analytical field such as physics, mathematics, accounting, business, computer science, or engineering if not data science itself.

Here is a breakdown of the specialist Data Science degree courses.

Self-study

For those who consider college impractical for whatever reason, self-study provides a flexible, affordable and self-paced alternative. If you find the sheer number of options intimidating, we have you covered. From DataCamp to discord, here are 10 resources to kickstart your data science education and career, plus a bonus! 

Building a foundation 

The first step in your data science education is to build a strong knowledge of fundamental data science concepts.

1. Massive Open Online Courses (MOOCs)

Learning data science through MOOCsis a super flexible and affordable way to dive into the field. Whether you're a beginner or looking to sharpen your skills, MOOCs offer courses from top universities and companies that cover everything from basic stats and Python to machine learning and big data. You can learn at your own pace, choose what interests you, and build up your expertise without the pressure of a formal college setting. Plus, there are tons of hands-on projects to help you apply what you learn in real-world scenarios!

Here are some popular MOOCs from different providers:

i. Coursera - Data Science Specialization (Johns Hopkins University)

• Beginner-friendly, covers data wrangling and machine learning.

ii. Coursera - Machine Learning (Stanford University, Andrew Ng)

• Classic intro to machine learning algorithms.

iii. edX - Data Science MicroMasters (MIT)

• Advanced course series on big data and machine learning.

iv. Coursera - Applied Data Science with Python (University of Michigan)

• Hands-on Python for data analysis and machine learning.

v. Udemy - Python for Data Science and Machine Learning Bootcamp

• Practical Python for data analysis and machine learning.

vi. HarvardX - Data Science Professional Certificate (edX)

• Comprehensive program with R, stats, and visualization.

vii. Udacity - Data Scientist Nanodegree

• Project-based, covering deep learning and advanced topics.

viii. Kaggle Learn - Data Science Courses

• Free interactive courses on Python and machine learning.

ix. Fast.ai - Practical Deep Learning for Coders

• Deep learning with Fast.ai, beginner-friendly with coding experience.

x. Google Cloud - Data Engineering and Machine Learning on GCP (Coursera)

• Hands-on course series focusing on cloud-based data science tools.

2. Textbooks

Learning data science through textbooks is a solid way to dive deep into the subject if you like structure and detailed explanations. Textbooks break down everything from the basics to more advanced concepts like algorithms and the math behind machine learning.

Some popular books to check out:

• "Python for Data Analysis" by Wes McKinney: Awesome if you’re getting started with Python and want to learn data manipulation with Pandas.
• "Hands-On Machine Learning" by Aurélien Géron: A great hands-on guide to machine learning using popular tools like Scikit-Learn and TensorFlow.
• "The Elements of Statistical Learning" by Hastie, Tibshirani, and Friedman: A bit more advanced, but excellent if you’re into the math side of machine learning.
• "Data Science for Business" by Provost and Fawcett: Focuses on how data science is used in business, giving you real-world context.
• "Deep Learning" by Ian Goodfellow: The go-to book if you want to dive into neural networks and deep learning.

Textbooks can be more detailed than online tutorials and might take more time, but they’re perfect if you want a solid understanding of the principles behind data science. Plus, they make great reference material whenever you need to revisit a tricky topic!

3. Youtube 

Learning data science on YouTube is a fantastic way to dive into the field, especially if you prefer video tutorials and want to learn at your own pace. YouTube has countless channels that cover everything from the basics to advanced data science topics, all for free.

Here’s a quick intro to why YouTube is awesome for learning data science, along with some must-watch playlists:

• Free and Accessible: YouTube is packed with free resources, making it easy to learn anytime, anywhere.
• Step-by-Step Tutorials: Most channels offer complete tutorials, breaking down complex topics like Python, machine learning, and data analysis into easy-to-follow steps.
• Variety of Content: Whether you're a beginner or looking to specialize in certain areas (like deep learning or data visualization), you can find videos that fit your needs.

Here are some great playlists to kick off your data science learning journey:

freeCodeCamp Data Science and Machine Learning: FreeCodeCamp offers comprehensive tutorials on Python, data science, and machine learning with full-length courses.

Krish Naik - Data Science End-to-End Projects: Krish Naik’s channel is perfect for practical, project-based learning in data science and machine learning.

StatQuest with Josh Starmer: StatQuest breaks down complex statistical concepts and machine learning models into easy, understandable lessons.

Corey Schafer - Python Tutorials: Corey’s Python tutorials are super detailed and helpful if you’re just getting started with the programming side of data science.

Sentdex - Machine Learning with Python: Sentdex offers in-depth tutorials focused on Python, machine learning, and AI, making it great for intermediate learners.

Practice makes perfect

Data science is a practical field, and theory alone will not make you a data scientist. 

4. Kaggle

Kaggle is the go-to platform for data science practice. Kaggle lets you find and share datasets, build models right in your browser, and collaborate with other data scientists and machine learning pros. You can join competitions to tackle data science challenges, and access tons of datasets, courses, and notebooks to help you learn. The more you dive into Kaggle projects and competitions, the more you’ll grow your skills. Plus, it’s an awesome platform to network with other data science enthusiasts!

5. Internships 

Learning data science through internships is a great way to gain real-world experience while applying the skills you've learned in school or on your own. Internships give you hands-on exposure to how data science is used in different industries—whether it's in tech, finance, healthcare, or marketing. You’ll get to work with actual datasets, collaborate with professionals, and tackle real business problems.

Here’s why internships are valuable for learning data science:

• Practical Experience: You’ll move beyond theory and apply what you’ve learned to real-world projects, which helps you understand how things work in the industry.
• Exposure to Tools: Internships let you use industry-standard tools and platforms like Python, SQL, Tableau, Power BI, and cloud services (AWS, Google Cloud).
• Mentorship: Working alongside experienced data scientists means you can ask questions, get feedback, and learn from their expertise.
• Networking: You’ll meet professionals who can guide you and help open doors to future job opportunities.
• Portfolio Building: The projects you complete during an internship can be added to your portfolio, showcasing your experience to potential employers.

Internships are an awesome way to build your confidence, expand your network, and boost your resume—all while learning in a real-world setting.

6. Medium 

Medium.com is an online publishing platform where people share articles, stories, and ideas on just about any topic. It's user-friendly and open to anyone, whether you're a casual writer or a professional looking to publish your work. What makes Medium stand out is the variety of high-quality content from everyday writers, industry experts, and organizations.

Medium is a great place to learn data science because it offers a huge range of articles from people at different stages of their data science journey. Here's why it's useful:

• Wide Range of Topics: You'll find articles on everything from beginner Python tutorials to advanced machine learning techniques. Whether you're just starting or looking to refine your skills, there’s something for everyone.
• Real-World Insights: Many data scientists share their personal experiences, case studies, and project breakdowns on Medium. It’s a great way to learn practical applications and stay updated on trends.
• Step-by-Step Guides: You can find detailed, step-by-step guides on data analysis, machine learning, data visualization, and more. These often come with code snippets and explanations that make it easy to follow along.
• Diverse Perspectives: Unlike textbooks or traditional courses, Medium offers insights from a wide variety of authors—ranging from students to professionals in the field.
• Collections and Tags: Medium organizes content using tags like “Data Science,” “Machine Learning,” “AI,” and “Big Data,” making it easy to find articles related to specific topics.

Some great publications on Medium to follow for data science content include:

• Towards Data Science: One of the largest publications on Medium, it features articles on data science concepts, tutorials, industry insights, and case studies.
• Analytics Vidhya: Shares a wide range of data science and machine learning tutorials.

Medium’s a great place to expand your data science knowledge, learn from others, and even publish your own insights as you grow in the field.

7. LinkedIn 

Learning data science on LinkedIn is a great way to build skills and grow your network at the same time. Here’s why it works:

• LinkedIn Learning: They’ve got a bunch of courses on data science topics like Python, machine learning, and data visualization. You can go at your own speed and even earn certificates to show off on your profile.
• Networking: Since LinkedIn is all about professionals, you can connect with data scientists, follow industry experts, and join discussions. It’s a great way to stay in the loop with the latest trends and tools.
• Groups and Communities: There are a ton of data science groups where people share resources, answer questions, and post job opportunities. It’s a solid place to learn from others and pick up new tips.
• Posts and Articles: Data science pros regularly share their thoughts, tutorials, and project breakdowns. Following the right folks can help you discover new learning materials and insights.
• Job Opportunities: As you learn, you’ll be able to keep an eye on data science jobs or internships. LinkedIn is perfect for finding roles that can help you put your skills to use.

In short, LinkedIn is not just for job hunting—it’s also a great place to learn data science and connect with people who can help you grow in the field.

8. Github 

GitHub is a game-changer for data science, especially when it comes to working on projects and collaborating with others. Here’s why it’s super helpful:

• Version Control: GitHub keeps track of all your code changes, so if you mess something up, you can easily roll back to an earlier version. It’s like having a backup of every step you take.
• Collaboration: Working with others on a data science project? GitHub makes it easy for multiple people to contribute, review each other’s code, and merge changes smoothly.
• Showcasing Projects: If you’re looking to build a portfolio, GitHub is a perfect place to share your projects. Employers or collaborators can see your code, how you work, and even try out your projects.
• Open Source Resources: GitHub has a massive collection of open-source projects. You can learn from other data scientists by checking out their code, or even contribute to popular libraries like Pandas, Scikit-Learn, or TensorFlow.
• Collaboration Tools: GitHub has features like issues and pull requests that make it easy to manage projects, track bugs, and suggest improvements.

GitHub is a must-have tool for organizing, collaborating, and showcasing your data science work.

9. Discord

Surprised? Discord started as a social networking platform for gamers, but eventually found a myriad of different purposes. 

Learning data science on Discord can be a lot of fun and super interactive, which is why it is my personal favourite. It’s like having a huge group chat with people who are into data science just like you. Here’s why it’s worth checking out:

• Community Vibes: There are tons of Discord servers dedicated to data science where you can chat with others, ask questions, and get tips from people at all skill levels.
• Instant Help: Got a coding problem or a question about a concept? You can get quick feedback because everything’s in real-time. It’s way faster than waiting for an email response or forum reply.
• Networking: It’s a great way to meet other data enthusiasts and professionals. You might find collaborators for projects or just get some cool advice.
• Cool Resources: Many servers share useful resources like datasets, tutorials, and project ideas. It’s like having a mini library of data science goodies at your fingertips.
• Events and Challenges: Some servers host events like coding challenges or workshops where you can test your skills and learn new things in a fun way.
• Peer Learning: You get to see how others tackle problems and get different perspectives, which can really help with your own learning.

So if you enjoy chatting and learning in a community setting, Discord is definitely worth exploring for your data science journey!

10. SkillCamper

Yes, our very own SkillCamper has various courses and blogs that can help you on your data science journey. SkillCamper offers AI-powered courses. From our <free courses> to our specialized <bootcamps>, SkillCamper offers a practical, project-based approach in bite-sized modules. The courses are designed with input from industry experts, so your skills are even more relevant. What’s more, you can interact with mentors and peers for guidance and collaborative learning and undertake regular quizzes and hands-on tasks to assess your progress and understanding. You can take a look at our courses here.

Conclusion

In wrapping up, it's clear that there are tons of great resources out there to help you tackle your data science education and meet all the key data science requirements. Whether you're just starting out or looking to advance your data science career, the right mix of courses, tools, and hands-on practice can make all the difference. From learning programming languages like Python to diving deep into machine learning, these resources will get you where you need to be. Keep exploring, stay curious, and you'll be well on your way to becoming a data scientist!

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