100+ Best Data Science Resources to Learn Online in 2024

Hey guys, I have collected some best Data Science resources, Machine Learning resources, Deep Learning resources, Python resources, SQL resources, and Statistics resources, from all around the internet. If you also want to contribute to the community with your resources, contact me.

Best Data Science Resources:

Find all the following resources here.

  • 151 Data Science Interview Questions and Answers
  • 164 Data Science Interview Questions and Answers
  • 120 Data Science Interview Questions and Answers
  • Complete 30 Days Data Science Interview Preparation by iNeuron
  • Data Science Interview Questions and Answers
  • Data Science Interview Questions
  • Data Science cheatsheet
  • Data science’s main formulae
  • 15 In-depth Interviews with Data Scientists

Best Machine Learning Resources:

Find all the following resources here.

  • 50 Machine Learning Interview Questions and Answers
  • Mathematics for Machine Learning
  • Machine Learning Cheatsheet
  • Machine Learning cheatsheet Mini
  • Machine Learning course
  • Machine Learning for Everyone
  • Supervised Learning cheatsheet
  • Supervised Machine Learning
  • Applying Unsupervised Learning
  • Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Big Data

Best Deep Learning Resources:

Find all the following resources here.

  • Visual Introduction to Deep Learning
  • Calculus for Deep Learning
  • Deep Learning
  • Deep Learning by Andrew Ng
  • Deep Learning Essential Notes by Andrew Ng
  • Optimizers used in Deep Learning by Let the Data Confess
  • Deep Learning Tips and Tricks

Best NLP Resources:

Find all the following resources here.

  • 100 NLP Questions and Answers

Best Python Resources:

Find all the following resources here.

  • All about Python
  • Beginners python cheatsheet
  • Top 100 Python Interview Questions
  • Top 40 Top Python Interview Q&A
  • Python cheat sheet for data science
  • 100 Numpy Exercises
  • Pandas cheatsheet
  • Pandas Exercises
  • EDA using Python
  • Python for Data Analysis

Best SQL Resources:

Find all the following resources here.

  • Full SQL ebook 221 pages
  • SQL notes 166 pages
  • SQL cheatsheet 12 pages
  • SQL cheatsheet 3 pages
  • SQL Joins cheatsheet 3 pages

Best Statistics Resources:

Find all the following resources here.

  • Statistics cheatsheet
  • The Cartoon Guide to Statistics
  • Analysis of Variance (ANOVA) by Let the Data Confess

Best Probability Resources:

Find all the following resources here.

  • Probability cheatsheet

Git Resources:

  • Git cheatsheet

Other Resources:

Find all the following resources here.

  • Speech Recognition With Python
  • Azure ML cheatsheet
  • Data Engineering cheatsheet
  • R programming cheatsheet
  • How to Choose the Right Data Visualization
  • Visualization cheatsheet

Case Studies:

Free Courses from Google:

BONUSES:

A visual guide on how to choose the right Database:

With so many options around, choosing the right datastore can be confusing. In this diagram, we can see a selection choice for a datastore based on a use case.

Data can be ๐˜€๐˜๐—ฟ๐˜‚๐—ฐ๐˜๐˜‚๐—ฟ๐—ฒ๐—ฑ (๐—ฆ๐—ค๐—Ÿ ๐˜๐—ฎ๐—ฏ๐—น๐—ฒ ๐˜€๐—ฐ๐—ต๐—ฒ๐—บ๐—ฎ), ๐˜€๐—ฒ๐—บ๐—ถ-๐˜€๐˜๐—ฟ๐˜‚๐—ฐ๐˜๐˜‚๐—ฟ๐—ฒ๐—ฑ (๐—๐—ฆ๐—ข๐—ก, ๐—ซ๐— ๐—Ÿ, ๐—ฒ๐˜๐—ฐ.), ๐—ฎ๐—ป๐—ฑ ๐˜‚๐—ป๐˜€๐˜๐—ฟ๐˜‚๐—ฐ๐˜๐˜‚๐—ฟ๐—ฒ๐—ฑ (๐—•๐—น๐—ผ๐—ฏ). In the case of structured, they can be relational or columnar, while in the case of semi-structured, there is a wide range of possibilities, from key-value to graph.

Credits – Satish Chandra Gupta

A visual guide on how to choose the right Database - Data Science resources

NOTE – All the credits for these resources go to their respective owners. None of this work/resources belongs to me.

Online Courses and Tutorials

One of the most popular avenues for learning data science is through online courses and tutorials. Platforms like Coursera, Udacity, and edX offer a wide range of courses catering to all levels of expertise. From introductory courses covering basic concepts to advanced specializations in machine learning and artificial intelligence, there’s something for everyone. Additionally, YouTube channels and dedicated websites provide free tutorials and walkthroughs, making it accessible to anyone with an internet connection.

Books for Data Science

Books remain a timeless resource for delving deep into the fundamentals of data science. Whether you prefer traditional textbooks or practical guides, there’s a plethora of options to choose from. From classics like “Introduction to Statistical Learning” to more recent releases focusing on specific tools and techniques, building a comprehensive library can provide invaluable insights and reference material.

Blogs and Websites

Staying updated with the latest developments in data science is essential for professionals in the field. Blogs and websites dedicated to data science offer a wealth of information, including case studies, tutorials, and industry news. Platforms like Towards Data Science, KDnuggets, and DataCamp’s blog provide a steady stream of content curated by industry experts, making them essential resources for continuous learning.

Open Source Tools and Libraries

Open-source tools and libraries play a crucial role in the data science ecosystem. From programming languages like Python and R to libraries such as TensorFlow and scikit-learn, these resources enable data scientists to analyze data, build models, and deploy solutions efficiently. Contributing to open-source projects also offers opportunities for collaboration and skill development within the community.

Communities and Forums

Engaging with data science communities and forums is another effective way to learn and grow in the field. Platforms like Reddit’s r/datascience and Stack Overflow provide spaces for asking questions, sharing insights, and connecting with fellow enthusiasts. Joining local meetups and online forums allows individuals to network with professionals and gain valuable perspectives from diverse backgrounds.

Data Science Competitions

Participating in data science competitions offers a hands-on approach to learning and problem-solving. Platforms like Kaggle host a variety of competitions ranging from beginner-friendly challenges to advanced tasks sponsored by leading companies. Competing in these competitions not only hones technical skills but also provides exposure to real-world datasets and scenarios.

Data Science Podcasts

For those who prefer auditory learning, podcasts offer a convenient way to stay updated on the latest trends and discussions in data science. Podcasts like “Data Skeptic” and “Talking Machines” feature interviews with industry experts, discussions on cutting-edge research, and practical tips for aspiring data scientists. Listening to podcasts during commutes or downtime can complement other learning activities and provide valuable insights.

Data Science Conferences and Events

Attending data science conferences and events is an excellent opportunity for professionals to network and stay abreast of industry developments. Conferences like the International Conference on Machine Learning (ICML) and the Strata Data Conference feature keynote presentations, workshops, and networking sessions covering a wide range of topics in data science and artificial intelligence. Virtual conferences have become increasingly popular, allowing participants to engage with speakers and attendees from around the globe.

Data Science Certifications

Earning certifications is a valuable way to demonstrate proficiency in data science concepts and tools. Programs like the Google Data Analytics Professional Certificate and the Microsoft Certified: Azure Data Scientist Associate credential provide structured learning paths and assessments to validate skills. Certifications not only enhance credibility but also increase job opportunities and earning potential in the competitive job market.

Social Media Channels for Data Science

Social media platforms serve as valuable resources for connecting with like-minded individuals and staying updated on industry trends. Following data science influencers and organizations on platforms like LinkedIn, Twitter, and GitHub provides access to curated content, job postings, and networking opportunities. Engaging in discussions and sharing insights on social media can help build a strong personal brand and expand professional networks.

Data Science Projects and Datasets

Hands-on projects and real-world datasets are indispensable for gaining practical experience in data science. Platforms like Kaggle and GitHub offer a diverse range of datasets and project ideas for individuals to explore and work on. From predictive modeling and data visualization to natural language processing and deep learning, completing projects allows data scientists to apply theoretical knowledge to real-world problems and showcase their skills to potential employers.

Mentorship and Coaching

Seeking mentorship and coaching from experienced professionals can accelerate career growth and skill development in data science. Mentorship programs, online coaching sessions, and networking events provide opportunities for individuals to learn from industry veterans, receive personalized guidance, and navigate career challenges. Building meaningful relationships with mentors fosters continuous learning and opens doors to new opportunities in the field.

Career Resources and Job Portals

Navigating a career in data science requires access to resources and job opportunities tailored to individual goals and aspirations. Career development platforms like LinkedIn Learning and Data Science Central offer courses, articles, and job postings specifically curated for data science professionals. Job portals like Indeed, Glassdoor, and LinkedIn provide access to a wide range of job opportunities across industries, allowing individuals to find roles that align with their skills and interests.

Conclusion

In conclusion, the field of data science offers a wealth of opportunities for individuals passionate about leveraging data to drive innovation and solve complex problems. Access to quality resources, including online courses, books, blogs, and communities, is essential for continuous learning and professional growth. By actively engaging with these resources and participating in hands-on projects and networking events, aspiring data scientists can build the skills and knowledge needed to thrive in this dynamic field.

FAQs

How do I get started with learning data science?

Begin by exploring online courses and tutorials covering fundamental concepts in data science. Practice coding and data analysis with real-world datasets to solidify your understanding.

What programming languages are essential for data science?

Python and R are two of the most commonly used programming languages in data science due to their versatility and extensive libraries for data manipulation, analysis, and visualization.

Are certifications necessary for a career in data science?

While certifications can enhance credibility and job prospects, they are not strictly necessary. Focus on building a strong portfolio of projects and practical skills through hands-on experience and continuous learning.

How can I stay updated on the latest trends in data science?

Follow industry blogs, subscribe to newsletters, and participate in online forums and communities dedicated to data science. Additionally, attending conferences, webinars, and networking events can provide valuable insights and connections.

What are some common career paths in data science?

Data scientists can pursue various career paths, including roles such as data analyst, machine learning engineer, data engineer, and AI researcher. The choice of career path often depends on individual interests, skills, and professional goals.

Check out my other machine learning projectsdeep learning projectscomputer vision projectsNLP projectsFlask projects at machinelearningprojects.net

Abhishek Sharma
Abhishek Sharma

Started my Data Science journey in my 2nd year of college and since then continuously into it because of the magical powers of ML and continuously doing projects in almost every domain of AI like ML, DL, CV, NLP.

Articles: 520

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