A couple of days ago I sent the above tweet and honestly I’ve been overwhelmed by the positive messages ever since. Apart from the congratulatory messages, I’ve also received a number of messages from people asking me about how they too can successfully transition from a technical or non-technical role to Data Science.
David Robinson (The Data Scientist behind one of my favourite Data Science blogs Variance Explained) once said if you get asked for advice 3+ times, write a blog post and I’m duly obliging.
Due to the varied amount of questions I’ve received, In this article I’m going to discuss the following:
Feel free to skip to the section you’re most interested in, simply click on the title and it’ll take you to the appropriate section.
It’s taken me some time to accept it, but ever since I was a child I’ve been a bit of a nerd. My imagination knew no bounds due to the amount of science fiction I watched, from Star Wars, Power Rangers, The Jetsons to Ironman.
These sci-fi movies displayed the most advanced technology, inspiring me to both want to use and build it. My heroes at that age weren’t celebrities, they were scientists, engineers and techies like Einstein, Tony Stark, Stephen Hawking etc. This led to my strong interest in Mathematics and Science.
Fast-forward to University, I was unsure of what career path I wanted to pursue, I just knew I wanted it to be technical. My love for Mathematics and Physics as well as my fascination with flying led me to study Aeronautical Engineering at City University. I never really felt this was the job I wanted to do for the rest of my life, but the obvious path was to work in the aviation industry.
After graduation, I tried to pursue a career in aviation through the engineering side but due to the difficulty and my lack of passion I failed to do so. Instead I ended up entering the aviation industry through operational roles. These operational roles were far from challenging, in fact, my university classes were far more challenging and I soon became disillusioned and dissatisfied.
However, I wasn’t satisfied with this, I wanted a career that would give me the opportunity to not only code but also to apply my (lost and forgotten) Mathematics skills. At that time, Data Science was the hottest career. To make sure it wasn’t just hype and I wasn’t just getting on the bandwagon, I did extensive research (reaching out to Data Scientists and reading several books/blogs) and was convinced this was the career for me.
The first step I took was to leave the aviation industry and take up a Data Analyst role at a Data Centre firm. This role, although not highly technical only served to give me greater motivation to continue down the journey to Data Science.
Initially, I was stuck in Excel but through my initiative, I discussed with my Manager and we came to an agreement that I would work on projects where I could use Python at work. This meant for the first time, some of the Data Science skills I had been learning outside of work could be applied during my working hours. This only served to increase my motivation and programming skills.
Unfortunately, I was the only person on my team with this sort of knowledge, so I had no one to guide or mentor me. I felt my learning wasn’t progressing as quick as I wanted, online courses are great but if you don’t have a great feedback loop i.e. a lecturer/teacher you can interact with then the process becomes harder.
So, I decided to look at bootcamp options, I found the Cambridge Spark Data Science Part-Time Bootcamp and decided to take the plunge. Doing this bootcamp (which operates classes every 2 weeks on weekends from 9am-5pm) meant that sometimes I would work 12 days straight (Mon-Fri: Work, Sat-Sun: Bootcamp, Mon-Fri: Work). For 6 months, I did not have a social life, my friends got tired of me saying “I have to study” or “I have class” on the weekends.
After I finished the bootcamp, I felt I was ready for an entry-level Data Science role. My company at the time had a Data Science team, so I thought it was only a matter of time before I could transition to the team. However, due to some restructuring of the business, it was clear this wouldn’t be possible. The company wanted to offshore most of my department, offering me another job — which was essentially a Data Entry role — but I rejected it and instead chose to leave the company.
I then randomly came across an ad for the Royal Mail Data Science Charity Hackathon, participants would not only get to help out a charity but would also get the chance to interview with the Royal Mail Data Science team if they showed significant technical skill. I jumped at the chance and 2 weeks later, I received an offer to become a Junior Data Scientist for Royal Mail.
There are a 8 key lessons that I attribute my success to:
- Pick ONE programming language and STICK to it. Don’t go back and constantly change your choice of language to study. If you do, you will slow your progress down.
- Be clear about your motivation. The reason this is important because learning Data Science is HARD. VERY HARD! So it’s easy to lose motivation when on the journey. If your motivation is clear and strong, it makes the struggles easier to bear. Write it out in large letters and stick it on all the walls around you. For example, I have a journal and almost everyday I literally wrote “I will become a Data Scientist”. Try and visualise your goal anyway that helps.
- Don’t get stuck in the Tutorial carousel. If you keep doing tutorial after tutorial, it’s easy to deceive yourself into thinking you know what you’re doing. The better approach is to augment studying via tutorials/courses with learning by doing i.e working on projects. Find a project where you can apply Data Science to an interest of yours, for example, mine was to predict the winners of premier league football matches.
- Pick a small set of resources. There are SO many resources out there to learn the fundamentals of Data Science. It’s easy to get started with one resource, find a new and shiny one then switch resources. Try to avoid this at all costs. Instead, pick a set of resources covering different topics (i.e. construct a curriculum) and STICK to it till you’re done with them.
- Immerse yourself in the community. You need to surround yourself with all things Data Science. You can do this by: subscribing to DS newsletters, reading Data Science articles and books, listening to Data Science podcasts, watching Data Science talks on youtube, taking advantage of sites like Meetup and Eventbrite by attending all and any Data Science events. Find online DS communities and join them.
- Go to Hackathons! Don’t wait till you’re “ready” before you go to a hackathon, the benefits outweigh any negatives you think you’ll experience. Hackathons can also be online, for example, Kaggle is essentially a never-ending online Hackathon.
- Find a mentor. This was the hardest part for me initially because I misunderstood what and who a mentor is. A mentor is simply an experienced and trusted teacher/counsellor. You can have more than one mentor and you might not even directly interact with them. My mentors ended up being influential Data Scientists who I interacted with by following them on social media, subscribing to their newsletters, reading their books and listening to their talks/podcast. When I felt I needed advice, I reached out to some of them directly via email and/or social media, not everyone replied but those that did helped me greatly on this journey.
- Be prepared to sacrifice your weeknights and weekends. You have to put in a lot of deliberate practice and spend significant time studying, expect your social life to suffer. Working hard is important but working smart is more valuable, make sure you prepare a timetable detailing courses you’re studying, books you’re reading and projects you’re working on.
Thanks to the internet, we have access to a vast amount of information. It’s very important that we use this to our advantage, I think it’s safe to say that without the resources listed below my journey would’ve been almost impossible.
- Open Source Data Science Masters — Rather than draft your own curriculum, @clarecorthell has done the hard work for us and put together a curriculum covering all the different aspects of Data Science with links to relevant courses, books, etc.
- Class Central — This is Google for online courses. You can find any online course related to any topic along with a brief description and user ratings.
- DataCamp — An EdTech company that teaches data science through interactive online courses.
- Kaggle — Kaggle is a platform for predictive modelling and analytics competitions.
- #100DaysOfCode — This is a challenge where beginner coders attempt to code for at least an hour everyday for 100 days.
- Codewars — Improve your skills by training with others on real code challenges.
- DrivenData — DrivenData bring crowdsourcing to some of the world’s biggest social challenges and the organisations taking them on.
- HackerRank — Practice coding. Compete. Find jobs.
- Machine Learning with Python Cookbook by Chris Albon
- An Introduction to Statistical Learning: with Applications in R
- Hands-On Machine Learning with Scikit-Learn and TensorFlow by Aurélien Géron
- Think Stats: Exploratory Data Analysis by by Allen B. Downey
- The Signal and the Noise: The Art and Science of Prediction by Nate Silver
- Prediction Machines: The Simple Economics of Artificial Intelligence
- How to Lie with Statistics by Darrell Huff
- Automate the Boring Stuff with Python by Al Sweigart
- Data Elixir — Data Elixir is delivered to your inbox each Tuesday with data science picks from around the web.
- Data Science Roundup — The internet’s most useful data science articles. Curated with ❤️ by Tristan Handy.
- FiveThirtyEight — Nate Silver’s popular blog uses Statistical Analysis to tackle Politics and Sports.
- Variance Explained — Data Science blog by David Robinson, the Chief Data Scientist at DataCamp, an EdTech company for teaching data science through interactive online courses.
- Flowing Data — FlowingData explores how statisticians, designers, data scientists, and others use analysis, visualization, and exploration to understand data and ourselves.
- The Pudding — The Pudding explains ideas debated in culture with visual essays
- Datacamp — Data Science blog to help you become a Data Scientist.
- Kaggle Blog — The Official Blog of Kaggle.com
- Machine Learning Mastery — Master machine learning by using it on real-life applications, even if you’re starting from scratch.
- Chris Albon — The Data Scientist behind the popular Machine Learning Flashcards and Author of Machine Learning with Python Cookbook.
- KD Nuggets — KDnuggets™ is a leading site on Business Analytics, Big Data, Data Mining, Data Science, and Machine Learning.
- Analytics Vidhya — Learn everything about Data Analytics.
- Linear Digressions — In each episode, the hosts explore Machine Learning and Data Science through interesting applications.
- Partially Derivative — The everyday data of the world around us, hosted by Data Science super geeks. For the nerdy and nerd curious.
- Data Skeptic — Features interviews and educational discussions of topics related to data science, machine learning, statistics and artificial intelligence.
- This Week In Machine Learning and Artificial Intelligence — Podcast that caters to highly-targeted audience of machine learning and AI enthusiasts.
- Software Engineering Daily — Technical interviews about software topics.
- DataFramed — By DataCamp, focuses on exploring the problems Data Science can solve.
- Talking Machines — Machine Learning is changing the questions we can ask, we explore how to ask the best questions and what to do with the answers.
- Becoming A Data Scientist Podcast — Interviews with Data Scientist or people on the journey to becoming a Data Scientist, to learn about their path.
- AI in Industry — Every week Dan Faggella interviews Top AI and ML executives, investors and researchers.
- 3Blue1Brown — By far the best Math tutorial channel out there. Explains complex concepts in a visual manner.
- Brandon Foltz — My 2nd favourite math channel, focuses mostly on teaching Statistics from beginner to advanced level.
- Computerphile — Videos about computers and computer stuff.
- PyData — PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other.
- Sentdex — Youtuber and programmer who produces high quality Data Science tutorials.
- Siraj Raval — Similar to Sentdex, produces funny and informative Data Science content.
- Two Minute Papers — Explains the latest Data Science Research papers in 2 minutes.
- Enthought — Find talks from popular Data Science conferences like SciPy, etc.
People to Follow
- @BecomingDataSci — Renee Teate, Data Scientist at HelioCampus and creator of the popular Becoming A Data Scientist website and podcast.
- @drob — David Robinson, Chief Data Scientist at DataCamp, and co-author of the Tidytext package and the O’Reilly book Text Mining with R.
- @chrisalbon — Chris Albon, The Data Scientist behind the popular Machine Learning Flashcards and Author of Machine Learning with Python Cookbook.
- @frankchn — Frank Chen, Software Engineer at Google Brain working on TensorFlow.
- @fchollet — Francois Chollet, Deep learning at Google. Creator of Keras, neural networks library. Author of “Deep Learning with Python”.
- @goodfellow_ian — Ian Goodfellow, Google Brain Research Scientist leading a team studying adversarial techniques in AI. Lead author of Deep Learning Book.
- @jakevdp — Jake VanderPlas, Data scientist at University of Washington eScience Institute. Visiting researcher at Google; Author of Python Data Science Handbook.
- @dataandme — Mera Averick, Tidyverse Dev Advocate at Rstudio.
- @math_rachel — Rachel Thomas, Co-founder Fast.ai and Professor at University of San Francisco.
- Python for Data Science — A Slack channel for pythonistas in Data Science.
- FreeCodeCamp Data Science Room — A Freecodecamp Gitter channel for Data Scientists.
- Reddit’s Data Science Subreddit
- Kaggle’s online forum
- #100DaysOfCode — A Slack channel for participants of the #100DaysOfCode Challenge.
- Stack Overflow — The world’s largest developer community.
While I have officially began my career as a Data Scientist, I do not see this as the end point. Due to the rapid pace of development in this industry, I believe I’m on a never ending journey which requires continuous learning. I hope this post has been useful and most importantly, I hope it’s given you the motivation and inspiration to continue your journey. If I can take this step, I’m very sure with enough dedication, hard and smart work, you can do it too.
Please feel free to reach out to me on Twitter @freddieoduks or LinkedIn if you have any more questions, my DM’s are open and I will do my best to reply. Also, I would appreciate if you watched my first public presentation of my Capstone Data Science Project in partnership with Wefarm.