Journey of Analytics

Deep dive into data analysis tools, theory and projects

Tag: data science career

How to Become a Data Scientist in 2020

Despite the spike in the interest related to Data Science and Machine Learning roles and courses, it is still possible to become a fully functional data scientist with minimal resources.

Some caveats, (1) be committed to invest hours of effort building your expertise. (2) The job market has gotten quite competitive, so be mentally prepared to work strategically and accept that finding a job will require sweat equity.

Note, the title of this post is “Data Scientist” but the steps below are true even if your aim is to become a data analyst, data engineer, analytics consultant or machine learning engineer.

data science job types
Data Science job types

Steps to Data Science Expertise

At its core, becoming a data scientist will require three steps (in sequence):

  1. Learn the skills
  2. Build your portfolio
  3. Apply to jobs strategically.

Step 1 – Learn the skills.

The list of skills below are mandatory.

  • Programming in R or Python.
  • Programming in SQL. Most courses never talk about SQL, but it is critical.
  • Machine learning algorithms. Know the code and also which one fits for what use case.
  • If you search Google, you will find free courses and books on all the above topics. Or go for a low-cost option from Udemy. Essentially you can learn the skills for <$100, even now in 2020.

Step 2 – Build your portfolio.

  • You can add 100 certifications, but you also do need to showcase the learning by way of projects. Use Github to host your projects or create a free wordpress website. If you have the capacity, explore low cost website hosting from Wix or Squarespace.
  • The project should be unique to you. Pick any free public dataset, and apply your perspective to slice and dice the data, and extract insights. This is what will set you apart from the 10,000 other candidates who completed the same free bootcamp or Coursera class. Sample project idea list here., based on beginner or advanced skill levels.
  • Free tutorials are available on many sites, including my own journeyofanalytics.

Step 3 – Apply to jobs strategically.

  • The job market is getting heated up, as people enter this field in thousands. Getting job leads is hard, getting to interview stage is even harder.
  • Make sure your profile on LinkedIn is “all-star”, with at least 500 connections.
  • You can significantly improve your odds by leveraging niche job sites, and hunting on LinkedIn content tabs and Twitter. Both are highly manual, which is why they work! No one else wants to pursue those methods! 🙂 A detailed how-to guide, full list of niche job boards and interview question sets are all available in my job search book which I keep updating every quarter. These strategies work, hence the blatant plug-in!
  • Be prepared to face a lot of rejections, especially for landing the first job. In the beginning, don’t be afraid to accept a low-paying job or work internships. It is easier to get a job when you are already hired!
  • Initially you may be hired as a “data analyst” – accept! A lot of companies are using the terms analyst and scientist intermittently, or use the “data scientist” title to designate more experienced hires.
  • Note, there are other job types in the data science domain apart from “data scientist” so check if you can leverage your previous experiences for other role types.

Book Offer:

Note, I realize a lot of students are graduating soon and the global pandemic is making it hard to find jobs. Some employers are already reneging on confirmed offers, which increases pressure on students. Hence I’ve reduced my ebook price to $0.99 for the month of May 2020.

Note, the book will NOT be marked free to deter folks who just download books and guides but do not intend to put in any effort!

All the best for an exciting new career!

This question was previously answered (by me) on Quora under the question – “Is it possible to become a data scientist in 2020 with only a few resources?

How to Become a Data Scientist

This question and its variations are the most searched topics on Google. As a practicing datascience professional, and manager to boot, dozens of people ask me this question every week.

This post is my honest and detailed answer.

Step 1 – Coding & ML skills

  • You need to master programming in either R or Python. If you don’t know which to pick, pick R, or toss a coin. [Or listen to me, and pick R – programming as it is used at Top Firms like NASDAQ, JPMorgan, and many more..] Also, when I say master, you need to know more than writing a simple calculator or “Hello World” function. You should be able to perform complex data wrangling, pull data from databases, write custom functions and apply algorithms, even if someone wakes you up at midnight.
  • By ML, I mean the logic behind machine learning algorithms. When presented with a problem, you should be able to identify which algorithm to apply and write the code snippet to do this.
  • Resources – Coursera, Udacity, Udemy. There are countless others, but these 3 are my favorites. Personal recommendation, basic R from Coursera (JHU) and Machine learning fundamentals from Kirill’s course on Udemy.

Step 2 – Build your portfolio.

  • Recruiters and hiring managers don’t know you exist, and having an online portfolio is the best way to attract their attention. Also, once employers do come calling, they will want to evaluate your technical expertise, so a portfolio helps.
  • The best way to showcase your value to potential employers is to establish your brand via projects on Github, LinkedIn and your website.
  • If you do not have your own website, create one for free using wordpress or Wix.
  • Stumped on what to post in your project portfolio?
  • Step1 – Start by looking in the kernels portion on the site www.kaggle.com there are tons of folks who have leveraged free datasets to create interesting visualizations. Also enroll in any active competitions and navigate to the discussion forums. You will find very generous folks who have posted starter scripts and detailed exploratory analysis. Fork the script and try to replicate the solution. My personal recommendation would be to begin with titanic contest or the housing prices set. My professional website journeyofanalytics also houses some interesting project tutorials, if you want to take a look.
  • Step 2 – pick a similar datasets from kaggle or any other open source site, and apply the code to the new datasets. Bingo, a totally new project and ample practice for you.
  • Step3 – Work your way up to image recognition and text processing.

Step 3 – Apply for jobs strategically.

  • Please don’t randomly apply to every single datascience job in the country. Be strategic using LinkedIn to reach out to hiring managers. Remember, its better to hear “NO” directly from the hiring manager than to apply online and wait in eternity.
  • Competition is getting fierce, so be methodical. Books like “Data Science Jobs” will help you pinpoint the best jobs in your target city, and also connect with hiring managers for jobs that are not posted anywhere else.
  • Yes, I wrote the book listed above – this is the book I wished I had when I started in this field! Unlike other books on the market with random generalizations, this book is written specifically for jobseekers in the datascience field. Plus, I’ve successfully helped a dozen folks land lucrative jobs (data analyst/data scientist roles) using the strategies outlined in this book. This book will help you cut your datascience job search time in half!
  • Upwork is a fabulous site to get gigs to tide you until you get hired full-time. It is also a fabulous way of being unique and standing out to potential employers! As a recruiter once told me, “it is easier to hire someone who already has a job, than to evaluate someone who doesn’t!”
  • If your first job is not at your dream job, do not despair. Earn and learn, every company, big or small, will teach you valuable skills that will help you get better and snag your ideal role next year. I do recommend staying at roles for at least 12 months, before switching, otherwise you won’t have anything impactful to discuss in the next interview.

Step 4 – Continuous learning.

  • Even if you’ve landed the “data scientist” job you always wanted, you cannot afford to rest on your laurels. Keep your skills current by attending online classes, conferences and reading up on tech changes.
    Udemy, again is my go to resource to stay abreast of technical skills.
  • Network with others to know how roles are changing, and what skills are valuable.

Finally, being in this filed is a rewarding experience, and also quite lucrative. However, no one can get to the top without putting in sufficient effort and time. So, master the basics and apply your skills, you will definitely meet with success.

If you are looking to establish a career in datascience, then don’t forget to take a look at my book – “Data Science Jobs‘ now available on Amazon.

Facebook
LinkedIn