As soon as you hear the term "Data Science", I know your mind immediately thinks of technical jargon or complicated mathematical analyses or something even more complex...computer programming. It's hard to approach something intricate but extensive and finicky yet fascinating as data science and think you'll actually be able to learn enough to do something meaningful with it. While I do admit that it might be impossible for you to know everything there is to about data science, I would say it's never too late nor too difficult to start attempting to.

Yes, there will always be new advancements in machine learning techniques and modern advancements in artificial intelligence that make it easier for data scientists (or aspiring ones) to leverage the data we have to extract predictive insights. This rapidly emerging field of predictive analytics is fortifying data-driven decision making, which can applied by anybody– businesses, governments, hospitals, NPOs, and schools alike– to determine their course of action and improve their future outcomes. The only way to not be left behind in this domain is to also get an idea of what goes on in the data science world around you, even as you're building your own technical expertise in computing and programming. If you're not sure how to get started, you're at the right place. I've compiled a list of three different ways you can get started right after you finish reading this article.

  1. Reading

In a field that encompasses a myriad of numbers and figures and lines of code at its core, you'd be surprised by the number of literary takes people have on data science subjects. There are several blogs and websites run by data scientists and programmers committed to reduce the high-tech, scholarly patois into comprehensible, informational, and insightful pieces for you. Here are my top recommendations for you to start reading (and start subscribing too):

  • Data Science Central is the best community for you to be in as a current or potential data scientist that combines education (learn about AI, ML, DL, Big Data, DataViz, Hadoop) and social interaction (read blog articles, hear podcasts, attend webinars, join member groups and chat in forums).
  • Analytics Vidhya was started by Kunal Jain and although he studied aerospace engineering in college, he has been at the forefront of business and data analytics for the past ten years. His website is visited by millions every month to cover data visualization tools and techniques, machine learning, and artificial intelligence.
  • Airbnb Engineering and Data Science covers the processes and practices of one of the most successful companies in modern times, specifically how Airbnb has excelled at the intersection of data analytics and business intelligence. Check out these articles to be offered a perspective on how data science can be used to attract and develop a customer base and prescribe other business/management strategies.
  • The Analytics Dispatch is an archive of blog articles about data science curated by Mode, a platform that combines R, Python, SQL, and visual analytics into one. Basically, the team that gathers these articles know what's relevant and captivating. You can place your trust in their weekly newsletter to deliver these picks right into your mailbox.
  • KDnuggets is consistently listed as one of the top Data Science publications on the internet and it's not hard to see why. It's the leading site on AI, Analytics, Big Data, Data Mining, Data Science, and Machine Learning with not only blog articles, but also opinion editorials, datasets, online webinars/events, job postings, and more. Check out the Most Popular and Most Shared sidebar for the best reads of the week.
  • Data Elixir is a data science newsletter reminiscent of its name, a potion that combines the best, magical ingredients. Curating the the top news and resources every week on machine learning, and data visualization/strategy, the team sends a list of the corresponding articles to your email. You can explore previous newsletter archives yourself or join the 30,000 other subscribers today.
  • InsideBIGDATA.com has the slogan of "Clear, Concise Insights on Big Data Strategies". That sums it up pretty well. More focused on how machine learning and data is playing a role in the global technology industry, this website offers many articles that offer a fresh perspective on business and information technology practices.
  • Arxiv Sanity Preserver is not only for researchers, I guarantee you. If you're looking for a scientific, theoretical overview of data science concepts, you can check out this web interface that gathers the top research papers of the day. The full paper should give you an in-depth look but the abstracts themselves are just as fascinating.
  • SmartDataCollective.com covers the key topics of business intelligence, data management, and analytics and delivers them to enterprise business owners, global experts, and data science communities. Written by a wide range of industry professionals, the articles here offer unique, diverse perspectives. Read SmartDataCollective. Get smart.
  • Machine Learning Is Fun encompasses exactly what its name implies. Check out this website written by Adam Geitgey, a software developer with years of working at big-name places like Groupon. His articles are light-hearted reads that approach machine learning from an amusing perspective. Make sure to check out articles that explain how ML can be used to generate Super Mario levels and how deep learning can recognize Will Ferrel and other celebrity faces.

2. Listening

I've said it before and I've said it again. Podcasts are the most underrated form of entertainment, particularly when you're multitasking or stuck in a mundane, effortless activity. Next time you open Spotify, Youtube, or the podcast apps itself, try and tune into these data science podcasts instead. You might be amazed on how easy they are to follow along to and how much you've discovered in forty minutes while cleaning your room and driving on the road.

  • Data Engineering Podcast is perfect for the Python lovers out there. The host, Tobias Macey, also hosts the Podcast.__init__ mentioned in our other article for python programmers. As a technical leader at MIT Open Learning himself, he shares his experiences and knowledge about data infrastructure and management every week: scaling processing pipelines, maintaining data lakes, proper governance, and more.
  • O'Reilly Data Show is a hosted by Ben Lorica, an experienced professional in the data science world. He was the Chief Data Scientist at O'Reilly Media and now the program director of several international conferences. His podcasts cover comprehensive explanations, industry happenings, new trends, and hot topics. O'Reilly offers additional webinars and events branching off topics that arise in podcast discussion.
  • Linear Digressions unfortunately came to an end this past summer but it had a great run as one of the best data science podcasts on the internet. Driven by user passion and interest, the hosts Katie and Ben tailored many of their episodes to what the listeners were looking for. Check out their archive of podcast that cover a slew of topics ranging from policing data during Black Lives Matter to Stein's Paradox to AI-based medical assistants.
  • Data Skeptic distinctly evaluates the major data science topics (statistical analysis, AI, ML etc.) from a critical thinking and scientific method perspective. Like it says in the name, they're initially "skeptical" of the veracity and efficacy of all the emerging data science practices but further pursue these topics in their episodes.
  • Talking Machines is for "Human Conversations About Machine Learning". Listen in for the answers to questions on how machine learning is transforming the world around us. Check out their astute articles on ML/AI industry topics and expert insights while you're on the website too.
  • Data Science at Home brings technology, machine learning, and algorithms discussions to you. Praised for its rich and dense material, the host Francesco Gadaleta delves deep into the story and theory behind modern applications and practices of data science in these episodes.
  • Not So Standard Deviations is a fortnightly data science podcast that doesn't shy away from talking about the random stories and personal experiences of the hosts, along with a discussion of data science in the industry and academia of course. This podcast is for those looking for a fun, less-intense listen that still gives them great exposure to the field.
  • Data Stories has its spotlight on data visualization and storytelling. So not only do you get to discern the techniques behind DataViz, you also come to recognize the importance of effectively conveying your results and insights to your key stakeholders.

3. Following

As part of my "Take Back your Newsfeed" campaign, here are my suggestions for you to customize and declutter your social media feed into something that can you can be entertained by, be educated from, and grow beyond where you are.

Join these Facebook groups/Slack communities to meet likeminded people (from literally all around the world), hear about their data science backgrounds, find helpful tips/advice, and learn about things you never knew existed.

Facebook:

Slack:

Twitter is basically microblogging. So if you don't have time to read lengthy posts and articles elsewhere, you can read noteworthy tweets about data science in your feed interspersed with everything else you like. Get updates from legends of the trade, hear from top-line business and research institutions, and read about all the recent breakthroughs and rages.

Data Mining/Analytics/Visualization: @SebastianThrun, @hmason, @CSurdak, @wesmckinn, @KirkDBorne, @peteskomoroch, @mrogati, @dpatil, @jakeporway, @Strategy_Gal, @KiraRadinsky, @KDNuggets

Artificial Intelligence/Machine Learning/Deep Learning: @GoogleAI, @OpenAI,  @Goodfellow_Ian,  @ylecun, @Karpathy, @deanabb, @NandoDF, @bigdata, @drfeifei, @AndrewYNg

Conclusion

The best way to keep up with the flourishing "big data culture" and upward trend in technological innovation is to not only master the technical skills, but also acquire more than a cursory knowledge of the data science field. Read about its scientific endeavors. Listen to educators that are redefining computer science learning. Observe the businesses that are innovating products and services. And lastly, join its communities of visionaries, professionals, students, hobbyists, and learners.


Experience and practice are best the successors to a strong knowledge base. Check out the free courses and projects at https://thecodex.me/ to start learning and applying new programming skills.