Trying to break into data science?

I get this question sliding into my InMail a couple times a week (sometimes more). Occasionally accompanying questions about the best tools, programming languages, frameworks, and whether something in data science is "dead".

"Can I hop on a call and pick your brain for just 15-20 min?"

Nothing wrong with asking but after 50+ messages I figured it was time to set the record straight (and to get ahead of the next 50 messages).

Question 1: How Did You Become A Data Scientist

Answer:

Honestly, years of hard work, tears, sweat, more tears, and boot camps. From the time I definitively decided in 2017 to when I received what I considered my first "real data scientist" offer (as opposed to a hybrid "data analyst/data scientist" role) it took at least two years of attending a boot camp (Springboard), a mentoring program (Data Science Dream Job), and many, many hours in between of reading, studying, coding, and projects.

You can get a quick overview in this Springboard post featuring my story.

Question 2: Can you give me advice or mentoring?

Answer:

At this point in time, I am overloaded on activities and can't balance out any more students that aren't part of an associated program or additional consulting opportunities. And to be honest, 99.7% questions tend to fall along the lines of:

• Data Science How-To Get Started: "how-to get started", "can I become a data scientist?", etc
• Technical (tool, programming language, library, etc based): "What do you think of Tensorflow vs. X/Y/Z?", "What's the best GPU?"
• General Jobs Search/Career Oriented: "How should I structure my resume?", "How do I network effectively?"

For a majority of these questions, I'd recommend the following:

Question 2A: Recommendations For Data Science How-To Get Started's?

Answer:

• Read my three medium posts that specifically cover how I approached my data science career:

1. ??Becoming a Data Scientist- The Hair Salon to Data Science (Ch.1)??: Pre-Data Science(aka “Why I Became a Data Scientist”) — This section covers my background and my early professional career. Key Takeaway: It’s not about the degree! ??
2. ??Becoming a Data Scientist- The Hair Salon to Data Science (Ch.2)??: The Uphill Climb to Upskill (aka “How I Became a Data Scientist — sort of…”) — This section covers my experience up skilling and attending a boot camp. Key Takeaway: Commit to investing in you! ??
3. ??Becoming a Data Scientist- The Hair Salon to Data Science (Ch.3)??: The Hunt Begins (aka “How I Got a Job as a Data Scientist”) — This section covers my experience job hunting, from applications to the onsite interview. Key Takeaway: Swing for the fences and dig deep! ??

• The podcast interview I did with my friend Harpreet Sahota, a data scientist who runs The Artists of Data Science podcast series & leads mentoring for Data Science Dream Job. Interview: You ARE Going to Struggle But It Will Make You Better | Mikiko Bazeley 

• The recent Women in Data Science career panel hosted by General Assembly I sat on along with these wonderful ladies of data science ( Maliha Tariq, Anne Kim, Bethany Poulin, Layla Bouzoubaa) . We talk about how we became data scientists, what our day-to-day responsibilities look like, and what we wish we had known.

Question 2B: Recommendations For Technical (tool, programming language, library, etc based) questions?:

Answer:

Google it.

This isn't meant to be mean-spirited but the reality is I don't keep up with every new advancement in data science, machine learning and AI.

After a few years of losing class time to gaming in college, I made a promise to myself to not get lost in either the hedonistic treadmill or the attention treadmill. My goal is to curate and scope down the information I interact with on a daily basis to the bare bones. Essential information I need to live a healthy, fulfilling and creative life with my loved ones, inside and outside of my career as a data scientist.

And with each role I've needed to adapt to a completely new stack as I want to add value (as opposed to just adding more tools, which in general leads to a decrease in adoption of any 1 tool).

Consequently, my response will almost always be: "Not sure, haven't used it" or "I've only used X, not it's competitors".

Google will almost always give you the answer you need, components of the answer you need, or nothing. If nothing then "Congrats!", you've stumbled on a new problem.

Question 2C: Recommendations For General Jobs Search/Career Oriented:

Answer:

This is an interest area because the reality is that most people have absolutely no clue what job searching looks like in the current day and age, nevertheless job searching and career crafting as a data scientist.

My biggest recommendation is to join a career program like Data Science Dream Job (or Kyle's newest offering, Dream Job Academy for a more general career) or Ramit Sethi's Find Your Dream Job. The reality is most people need a lot of help (and not just on resumes and applications!). One of the key take-away's that I learned from Kyle was how our external reality reflects our internal reality. What you believe will make it so. And the most valuable and sustainable method of changing your environment is to change your perception.

Question 3: Can you help me navigate the quarantine experience & help guide me in up skilling?

Answer:

This one I consider both a variant of "how to up skill" and general help requested. To be honest, I am really the wrong person to ask on how to make the experience of quarantine suck less, because I'm also having trouble adjusting successfully.

I'm definitely not at the same level of happiness before quarantine happened in SF as my schedule had just settled and I was starting to get back into boxing, dance classes, working on personal projects and getting involved with the local data science community. There were workshops that I was looking forward to doing in person like Reforge's Experimentation and Testing Deep-Dive, I was going to speak at my first invited conference ever, and Kartik and I were hoping to go travel.

So needless to say, I am one unhappy bundle of cantankerous gremlin right about now.

With that being said, up-skilling during quarantine is no different than up-skilling during any other point in time.

And at any point in time my general advice is:

1. Have a study plan...
2. ... that involves a project...
3. ...that's connected to a meaningful goal...
4. ...and execute one step at a time.

Meaning:

• Although the common song-and-dance right now is "you can learn anything you've ever wanted to!", there's no reason why the increase in available time suddenly changes the stack rank of learning needs. And for maximal stickiness, learners who have a reason for learning a particular topic will stick with courses and lessons for the long haul.
• A data scientist's value scales with the kinds of projects they work on, both personally and professionally. Knowledge is useful when you're able to implement what you've learned to solve a problem.
• Even though every learning platform is offering some kind of deal or sale, pick 1-2 to do at a time. There's a real cost in time and energy to context switching and having to re-build the momentum every time means you're losing out on the efficiency of continuing on a path as opposed to switching gears every time.

Question 4: Are there specific learning platforms you'd recommend?

Answer:

I've used all of these and they're all great:

• EdX - For classes & specializations
• Coursera - For classes & specializations
• DataCamp - For tutorials
• Udacity - For programs
• O'Reilly - For books, live-workshops, & conference recordings (subscription is completely worth it)
• LinkedIn Learning - For videos

For coding practice:

• Code Wars
• Hackerrank
• LeetCode
• HackerEarth

For competitions or data sets:

• Kaggle
• DrivenData

Question 5: You don't have X/Y/Z on the learning list, does that mean it's bad?

Answer:

Nope, just means I haven't used it. :)

My Final Comments

Hopefully by this point I've answered a good number of your questions and pointed you to some valuable resources. I would strongly recommend reading and listening to them if you have any questions (as I'll just re-direct everyone to this exact linkedin post if they haven't ??).

Interested in following along with my Data Science journey? One of the easiest ways is by connecting through LinkedIn. Feel free to send me an invite and let me know what you thought about my medium series (I encourage you to clap and leave a message or else I'm going to assume you haven't read it, at which point I will just redirect you to the post ??) ! 

You can also find me on GitHub or at Kyle’s Data Science Dream Job mentoring program. 

Has this advice been helpful? If so, let me know or ? consider buying me a coffee! https://www.buymeacoffee.com/mmbazel ?.