Post writting for Karlijn Willems
Are you looking for resources to learn data science with R or Python? Data science resources you haven’t considered (yet) – The best projects, tutorials, talks, podcasts, webinars, books, and much more to learn data science.
The Meaning of «Sexy»: No Real Answers (Yet)
Even though it’s still hard to agree on a precise definition of data science or the role of a data scientist, the interest in the field keeps on rising: numerous blogs prescribe how to “really” learn data science, hot topics in forums such as Quora deal with discussions that relate to “becoming a data scientist”. Naturally, these recommendations and discussions boil down to two essential questions: what is data science exactly and how can one learn it?
Leaving the first question for what it is at the moment, DataCamp wanted to focus on the second one in this post.
Because maybe right now, you don’t have the need to hear yet another definition of what data science is and what it can mean to you.
Maybe you want to learn about it and get your first job or to switch your career.
You also don’t want just another guide that lists 50+ resources to check out.
You want a list of resources you possibly haven’t considered yet!
This post contains links to projects, news sources, books, talks, podcasts, webinars, tutorials, community pages and courses that you need to check out to learn data science.
Learn Data Science With The Mystic Square of Resources
With the popularity of the field comes a whole variety of recommendations from all sides: beginners as well as experts, all with different backgrounds, give their view on what it means to actually learn data science.
In the end, considering all these resources and how they might fit your learning style is the key to learning data science. It’s about puzzling together the existing resources and making them fit for you.
That’s why DataCamp presents to you the mystic square of resources to learn data science: we already hand you some pieces of the puzzle that you can use to make your learning complete.
The best thing about this mystic square is that it contains resources that you might not have considered.
That means that the mystic square includes resources that are all complimentary to the ones that you have already encountered and registered to, as learning data science doesn’t limit itself to just one resource.
Data Science Projects
Even though the initial search interest for projects was already high to begin with, the demand for data science projects has been particularly high this year. Many users are looking to put their knowledge into practice or to advance their skills even further.
When you want to get into projects, Github is definitely a resource that you should consult. This site slowly finding its way into the list of resources that every beginner should know. The best Github projects that you can work on as an aspiring data scientist are:
- The Data Science IPython Notebooks: this repository is one of the qualitative resources that an aspiring data scientist can encounter. Like its name already gives away, this repository is filled with IPython notebooks that cover different topics, going from Kaggle competitions to big data and deep learning.
- The Pattern Classification repository is ideal for those of you who are looking for tutorials and examples to solve and understand machine learning and pattern classification tasks.
- For Deep Learning In Python, this repository is the way to go!
Tip: if you’re looking for data to start a project, don’t hesitate to check out data.world. This open data community is perfect for those of you who want to join forces to solve data science problems together or easily find data. Additionally, you can also add new data and share it with the community.
DrivenData hosts challenges where data scientists compete to come up with the best statistical model for difficult predictive problems that make a difference. You already can’t wait to get started? Then click here.
You can also apply to become a volunteer at DataKind to boost your project experience: the timespan of your adventure you can pick, ranging from networking and quick consultation to long-term projects. Through DataKind, you have the opportunity to tackle unexplored data and huge social issues like poverty, global warming, and public health at the same time.
For projects that have already been finished, consult the high-quality reports of the Master of Information and Data Science graduates’ capstone projects. Pay attention to the way each project reports their findings and constructs the narrative to passively strengthen your storytelling skills.
If you’re looking for people that have some real life experience working on projects, try joining one of your local Meetup groups. These meetings not only bring you into contact with people from the industry, but you also get to build up your knowledge through the presentations that are given at those events or share knowledge yourself.
Note that, maybe contrary to what you might believe from the previous paragraph, Meetup groups are not only perfect for those who already have some experience, but also for those who are just starting with data science!
Some of the Meetup groups also organize boot camps, workshops, hackathons, extra social events, and much more. Meetup groups attract those who are looking to either expand their knowledge or professional network or to deepen their skills in certain data science topics; And let’s not forget that these types of events are an awesome way to perfect your soft skills!
You can subscribe to receive newsletters with the newest events or install the app to stay up to date every moment of the day.
Data Science News
The news is maybe not the first thing that beginning data science learners are aware of, but it is certainly worth taking into account…
As a beginner, subscribing to one of the newsletters can give you certain advantages: newsletters offer you the possibility to stay up to date with the latest news, the newest case studies, and projects or job offerings.
And, if you’re also a big believer in language baths to learn a language, you will also understand that really “bathing” yourself in the data science world is necessary for you to learn quickly and to make your learning as qualitative as possible.
Besides the newsletters that you might already know and receive on a regular basis, such as the bi-monthly KDNuggets newsletter or the weekly Data Elixir newsletter, we have listed some others for you to keep an eye out for:
- Data Science Weekly: this weekly newsletter brings you up to date with the latest news, articles, and jobs.
- Data Science Central is a handy resource for those who are interested in big data. The site tries to give you an all-round community experience, which includes, among other things, webinars, links to job offers, blog posts, an editorial platform and the latest news, trends, and much more.
For more language-specific newsletters, you can check out:
- Python Weekly is a free weekly newsletter that features the latest news, articles, new releases, jobs, and much more. But, as the name suggests, all of these things are related to Python, of course.
- For your daily dose of Python tips that won’t let you down, you should subscribe to Python Tips.
- For R, you might consider subscribing to the RBloggers daily update to get to know what’s going on and what articles have been published.
There are also some blogs that give you regular updates (and some extras):
- Make sure to also check out the Center for Data Innovation blog, in which you can find data visualizations, weekly updates, and datasets!
- FiveThirtyEight provides all types of content, ranging from light-hearted and interactive to in-depth, and is famous for offering examples of how data can be made accessible and applicable to everyday life.
- The Yhat blog is a good source for those who are looking for the most interesting blogs on machine learning, data science, and engineering.
- You still haven’t found what you’re looking for? Consider checking out this Github Repository, which contains a huge list of all the data science blogs.
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