Here is What 8 Experts Advise You to Do to Enter the Data Science World

What should a smart, driven person do to enter the Data Science world? In 2013, when I started my Data Science journey, I had this question in mind. I am one of those people who believes that everything in life has a hack. It’s just a matter of googling the right keywords. So I was eagerly looking for those keywords that would unlock me the door to the Data Science world.

Data Science was quite a challenge to my modus operandi. In this field, the number of skills that are ‘essential’ to be a successful Data Scientist tends to infinity: Machine Learning, Calculus, Programming, Cooking… This was a problem for my modus operandi because it runs in O(2^N). Every keyword increases its execution time by an exponential order of magnitude.  

Everything changed after reading ‘The Start-up of You’. In this book, the authors talk about the I^(We) concept. The key idea behind the I^(We) concept is that our success depends on our individual capabilities (I) and our network’s capability to boost them (We). Notice that ‘We’ occurs as an exponent, precisely to give you the idea that the network can help you achieve an exponential growth.

Limahl
Limahl, The Voice of Neverending Story

I wish I had known about the power of online communities when I started my journey. That would have saved me the hours that I spent seated on my chair, chasing keywords on Google, with ‘The Neverending Story’ soundtrack playing on my mind. Actually, I was so into this loop that, at a certain point, it happened to me that my haircut was becoming more and more like Limahl’s haircut. This process started reversing on the day I found an online community. Now my haircut came back to its usual latino style.

Online communities are a great way to learn a new topic because they provide us with three types of knowledge:

  • Tailored knowledge. Online communities provide answers to your questions. This is knowledge ‘on-demand’. I love to read books, but how often does a book answers specifically to the question that I’ve in mind? Rarely. In a book, the author drives the conversation. In a community, you drive the conversation. It’s a different experience. Ask questions to an online community and you’ll see tons of practical feedback coming from people who have been where you are.
  • Up-to-date knowledge. One of the main challenges nowadays is to keep up with everything that is happening in the field. There’s always a new algorithm, a new framework, a new old way to do the exact same things. Being part of a community is a smart way to overcome this challenge. Why? Because the community will filter the information, spreading only the most important things. You just need to choose the right community.
  • Emotional knowledge. When we are part of a community, we are all on the same path. Some of us can be a little bit more ahead, others can be a little bit more behind. But, we are all on a learning journey. As a result, a supportive learning environment arises. This social interaction works then as an effective mechanism to make you feel inspired and motivated, which is fuel for your learning rocket.

I found my online community on LinkedIn. Due to LinkedIn, I now have access to a wide range of specialists who are eager to help me, I’m constantly updated of what’s coming out, and I feel inspired by people with whom I interact about specific topics of interest. Believe me, when you want to learn something, an online community is like an enhanced Google with feelings.

This is my advice to smart, driven people about to enter the Data Science world: join an online community. It can be LinkedIn, Twitter, Facebook, or any other. It doesn’t matter. Online communities are an underutilized resource by many people that are on a learning journey. Be smarter and take the shortcut. Join an online community.

To practice what I preach, I reached out the most influential people in my online community and asked them: ‘What advice would you give to a smart, driven person about to enter the Data Science world? What advice should they ignore?’

Below you’ll find their answers. Read them carefully because these people know what they are talking about. They are experts and I’ve been learning from them every single day. Now, it’s your time to also benefit from their wisdom. Read the answers and don’t take them in a passive way. Take them as a call to action. It’s never late to begin a new chapter in your life.

Experts interviewed:

  1. Nic Ryan
  2. Randy Lao
  3. Kyle McKiou
  4. Shujian Liu
  5. Kate Strachnyi
  6. Favio Vásquez
  7. Vin Vashishta
  8. Sudalai Rajkumar

Nic Ryan

LI: /nicryandataguy

Nic Ryan is a failed basketball player who has dreams of NBA glory that only grow stronger with each passing year.

Nic once saw a girl that was way out of his league, somehow he started a conversation with this goddess and now he is married to her and has 2 young daughters Auralie and Lucia, and a pug called Amber.

On the wrong side of 35 and nearing 40 years of age Nic has had a second youth including entering surfing competitions, taking up skateboarding, and beginning serious powerlifting.

While not holding on to the last remnants of youth, Nic is a data scientist, an aspiring full stack developer, and someone passionate about data-driven startups, the future of AI and remote working opportunities for data scientists.

What advice would you give to a smart, driven person about to enter the Data Science world? What advice should they ignore?

Firstly I’d say ignore any quick fix advice, so “learn data science in a weekend for $10,000” or similar is just going to be rubbish. The learning in this field is continuous and non-linear, so get comfortable with learning a little bit each day, but also trying to apply that knowledge to real problems.

Try to stay off the dreaded “tutorial wheel” where you just complete one tutorial after another but can’t apply this knowledge on the job. That kind of sucks and is disheartening at the same time.

The other thing is to map out where you want to be and work out how to get there. Look at jobs for data scientists that look interesting, work out where your skills are and plan out how you are going to increase your skills to be of interest to match what people are asking for.

Randy Lao

LI:/randylaosat

claoudml.co

Randy Lao works as a Data Science/Machine Learning Assistant in two data bootcamps: Trilogy Education and Data Application Lab. He also works at a nonprofit organization, IDEAS (International Data Engineering and Science Association), which is creating a data science learning platform to connect data science enthusiasts. Randy is a big supporter of this awesome data community and loves to contribute his resources to help others learn.

What advice would you give to a smart, driven person about to enter the Data Science world? What advice should they ignore?

Well, if you’re smart, congratulations! You’re on your way to an awesome field. My biggest takeaway and advice for anybody interested in Data Science is you MUST have a passion for learning. There is so much to learn in this field and having that curiosity/drive will definitely allow you to stay motivated.

Have a good understanding of databases such as SQL, math concepts from Linear Algebra to Calculus, Statistics and Probability, Communication, and most importantly, empathy. This is a field where you have to talk to people and exchange your ideas with others. You have to be comfortable in doing the work correctly and explaining your results in a simple/meaningful way. No matter how technical you are and how well your output is, if you cannot interpret or explain what you’re trying to say, then there’s literally no point at all.

I think it’s up to the person to decide whether or not to accept or ignore people’s advice. My saying goes: “Everyone you will ever meet knows something you don’t”. The goal is to be humble and listen to everybody who is talking to you. There is always something to take away from a conversation.

Kyle McKiou

LI:/kylemckiou

datasciencedreamjob.com

Kyle McKiou is helping aspiring data scientists master the job search process and break into the field. He has extensive experience in software engineering, applied math, and machine learning. For the last years, he has been working in different companies, always in data science roles. One of Kyle’s distinctive aspects is leadership. Over his professional life, Kyle has been able to build teams and help people developing their skills. Now, you can benefit from his experience joining the Data Science Dream Job course.

What advice would you give to a smart, driven person about to enter the Data Science world? What advice should they ignore?

Advice: Success is about doing the right things consistently until you achieve your goals, so never quit, even when times get tough. To become a data scientist, one key is to build 1-3 great projects that showcase your skills and demonstrate to employers that you are capable of doing the job of a data scientist.

Advice to ignore: Ignore any advice that tells you what qualifications you need (degrees, certifications, years of experience, etc) – the only qualification you need is being able to do the job well.

Shujian Liu

LI: /shujian-liu

Shujian Liu is a data scientist at Solaria Labs, a Liberty Mutual endeavor. His current focus is deep learning and natural language processing. Last year he was an active Kaggler and achieved competition master title. He also holds a doctoral degree from the University of Massachusetts Amherst.  His thesis was on ‘Aeroelastic Simulation of Wind Turbines Using Free Vortex Methods and Strategies for Accelerating the Computation’. You can follow him on his Kaggle account.

What advice would you give to a smart, driven person about to enter the Data Science world? What advice should they ignore?

This is an exciting field. Good luck. Please don’t bother to learn every aspect of data science. Just pick a few of them and become expert.

Kate Strachnyi

LI:/kate-strachnyi-data

TW: @StoryByData

Kate Strachnyi is the author of Journey to Data Scientist; which is a compilation of interviews that Kate herself conducted with over 20 amazing data scientists. — with backgrounds ranging from LinkedIn and Pinterest to Bloomberg and IBM. She is also the creator of Humans of Data Science (HoDS) – a project that works on showing the human side of data science (housed on her ‘Story by Data’ YouTube channel).

Kate is a manager working for Deloitte, currently working in the data visualization & reporting space. She previously served as an insights strategy manager and research analyst, where she was responsible for enabling the exchange of information in an efficient and timely manner.

Prior to working with data, she focused on risk management, governance, and regulatory response solutions for financial services organizations. Before joining the consulting world, she worked for the chief risk officer of a full-service commercial bank, where she was in charge of developing an ERM program, annual submission of ICAAP, and gap analysis of Basel II/III directives. Additionally, she worked as a business development associate at the Global Association of Risk Professionals (GARP).

Kate received a bachelor of business administration in finance and investments from Baruch College, Zicklin School of Business. Certifications include Project Management Professional (PMP) and Tableau Desktop 10 Qualified Associate.

What advice would you give to a smart, driven person about to enter the Data Science world? What advice should they ignore?

Find your own niche in the data science space. Don’t try to learn it all at once. Surround yourself with mentors that can help guide you along your path. Don’t be afraid to ask questions and reach out to people for advice. While you are learning, share what you uncover – helping other data scientists or aspiring data scientists will help you as well. You don’t have to be a “purple unicorn” – a jack of all trades; focus on the areas that you are passionate about and find real problems to solve with data. Communication and storytelling is a really important step; practice translating your data findings to someone that isn’t technically-savvy so you can improve communication skills.

Favio Vásquez

LI: /faviovazquez

Favio Vásquez is a proudly Venezuelan Physicist and Computer Engineer. He did his Masters in Physical Sciences at the National Autonomous University of Mexico. He has a passion for science, philosophy, programming and data science. He is currently working in Data Science, Machine Learning and Deep Learning as Principal Data Scientist at Oxxo and Senior Data Scientist at Raken Data Group.

He is fascinated by new challenges, working with a good team and solving interesting problems. He is part of the collaboration of Apache Spark, helping in MLLib, Core, and Documentation. With his contributions, he applies his knowledge and experience in science, data analysis, visualization and data processing to help the world become a better place.

What advice would you give to a smart, driven person about to enter the Data Science world? What advice should they ignore?

  1. Be patient. This is not easy, and although it is fun and useful to humanity and it is a complete career, that you must study mostly alone. There is no hurry (I know it seems that yes but no). There is still time, many people are starting in the area and they are looking for profiles of all kinds for the field.
  2. Read blogs from other experiences of how people have moved from other careers to Data Science. Here I leave some, three are mine:
  1. Study mathematics. It is important. You do not have to be an expert, but you have to have basic concepts of Calculus, Algebra and a little more advanced Statistics and Probability.
  2. Focus on Python, R, and SQL. You can not study everything at once. My recommendation is to learn Python well since it is a bit more serious language. And learn techniques with R that are very useful, apart from being more complicated with Python (dashboards, shiny, time series).
  3. Have passion. If you are here it is because you have it, so keep it. There are times when it seems that it is not worth spending so much time and money learning this, but if it is what you really like, be strong and go to the end. Do it with responsibility, but have fun learning.
  4. Practice. A lot. Dedicate to download toy datasets and follow the steps for an analysis in Data Science. Also, try to find datasets of your interest, or create them from the web.
  5. Share your knowledge. This is a complicated world but we must help each other. There is no other way, so when you learn something, validate it, and then a way to reinforce it is teaching, both in a simple post, blog, or something like that.

Vin Vashishta

LI: /vineetvashishta

Vin Vashishta has more than 20 years in tech, more than 10 years in leadership roles, and, for the last nine years, he has been dedicating time to nine in investing his time in data science and machine learning. He’s a strategist for companies figuring in the Fortune 100, as well as startups. He built products with revenue streams in the $100’s of millions and saved companies just as much.

He has been published in Fast Company, Silicon Republic, KD Nuggets, and many others. Several Top 10 lists presented Vin as an influencer in data science, machine learning, and predictive analytics. He’s doing executive education sessions around emerging technologies like data science, machine learning, blockchain, and quantum computing.

Vin is an advocate for aspiring data scientists. He believes that data science is one of the most difficult fields to break into as well as one with the highest need for new talent. That’s why the tries to teach and encourage people to learn more about data science.

What advice would you give to a smart, driven person about to enter the Data Science world? What advice should they ignore?

While you’re learning, break every rule given to you and do every “Don’t ever do this.” I’ve found that the best data scientists are rule challengers. You’ll get 2 things out of this process. 1st is a mindset that questions everything. That’ll be a big help in your career. 2nd is a deeper understanding of the concepts around data science. Until you’ve strayed from conventional wisdom, you don’t really understand the field. You get a deeper understanding from seeing failures than you do from always doing what works.

Sudalai Rajkumar

LI: /sudalairajkumar

Sudalai Rajkumar is a Data Scientist with extensive experience in solving real-world business problems across different domains.

Actually, Sudalai is a true problem solver. He participated and won several Data Science and Machine Learning competitions. He is a Kaggle Grandmaster in Competitions and Kernels section. He is also a top solver in Crowdanalytix, and he’s ranked as number two in Analytics Vidhya data science platform.

What advice would you give to a smart, driven person about to enter the Data Science world? What advice should they ignore?

Get your hands dirty on real problems whenever there is a chance. Practical knowledge is much more important than bookish knowledge. Take some open source datasets and try out whatever you have learned in the book/courses whenever you get an opportunity.

Digest the concepts very well because most of the times you need to explain those complex ones to business people in a simple manner. Visualizations help a lot in such situations.

To ignore is, don’t get too much carried away by the hype and enter this field. Enter only if you have a real passion to solve problems using data. If you feel, you have a desire for finding patterns in the data, helping businesses uncover value, mathematics and computer science, then this is the right field for you. All the very best!   

 

4 thoughts on “Here is What 8 Experts Advise You to Do to Enter the Data Science World

  • Hey, Pedro. Thanks for curating this post, very helpful.

    I’ve copied below my key takeaways from this post.

    Nic Ryan – Try to stay off the dreaded “tutorial wheel” where you just complete one tutorial after another but can’t apply this knowledge on the job.

    Randy Lao – “Everyone you will ever meet knows something you don’t”. The goal is to be humble and listen to everybody who is talking to you.

    Kyle McKiou – Success is about doing the right things consistently until you achieve your goals, so never quit, even when times get tough.

    Shujian Liu – Please don’t bother to learn every aspect of data science. Just pick a few of them and become expert.

    Kate Strachnyi – You don’t have to be a “purple unicorn” – a jack of all trades; focus on the areas that you are passionate about and find real problems to solve with data.

    Favio Vásquez – Share your knowledge. This is a complicated world but we must help each other. There is no other way, so when you learn something, validate it, and then a way to reinforce it is teaching, both in a simple post, blog, or something like that.

    Vin Vashishta – While you’re learning, break every rule given to you and do every “Don’t ever do this.” I’ve found that the best data scientists are rule challengers.

    Sudalai Rajkumar – To ignore is, don’t get too much carried away by the hype and enter this field. Enter only if you have a real passion to solve problems using data.

    • m0rd3p

      Excellent summary Akshay! I’d say that we have a pattern here: ‘start now doing something practical’. It’s almost like Arthur Ashe quote: ‘Start where you are. Use what you have. Do what you can.’

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