Journey of Analytics

Deep dive into data analysis tools, theory and projects

Tag: how to become a data scientist

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.

Data Science Job in 90 days – Book Review

Are you an R-programmer or Datascience enthusiast looking for a break in the datascience field? If so, my latest book “Data Science Jobs – land a lucrative job in 90 days” will help you find one quickly.

Imagine reducing your job hunt time by 2 weeks, or even 4 weeks? The strategies in this book are designed to do just that. [ Amazon book link here.]

As an analytics manager I get countless requests for job search advice, resume feedback and brilliant students who are somehow unable to find a job in this exciting field. There are tons of books on the internet on how to learn the skills to become a data scientist/ data analyst, but none to prepare folks for the frustrating job search.

I repeat the same advice to all these requests and am delighted to say that a dozen people were successfully able to land their dream roles with companies like LinkedIn, Walmart Labs, Comcast and others. I decided to publish the book so others can also benefit from the same advice.

Target Reader Audience

  • Students with solid knowledge of programming in R or Python looking to find a role as a data analyst/scientist or BI developer. A background in computer science or math will help, but not necessary.
  • International student on F-1/OPT visa looking for employment after a graduate degree in analytics.
  • Employed professionals looking to pivot their career, or seeking better pay/manager/location.

Book Summary

The book aims to provide you with creative techniques to get your resume directly in the hands of hiring managers, instead of relying hopelessly on online application systems that rarely produce a response. Don’t be fooled by the length of the book – it is deliberately kept short so that jobseekers can read through quicky and apply these principles in their job search.

The book chapters provide detailed guidelines on these broad themes:

  • Personal Branding – Create an online profile that helps you bubble up when hiring managers look for candidates. Make the jobs come to you! Tips to tweak your resume to achieve the same. For project inspiration, look at learning communities like R-bloggers, Kaggle, etc.
  • LinkedIn – Secret ways to leverage LinkedIn to engage hiring managers. Do NOT simply accept connections or indiscriminately apply to every open job. How to use LinkedIn to improve personal SEO!
  • Strategic Networking – How to actively reach out to the decision-makers who can hire you!
  • Niche sites – Hiring managers understand that the best venue to hire talent are the datascience communities where folks go to learn. The book lists these niches job boards on sites like R-bloggers, Kaggle.com and many others.
  • Upwork – despite popular opinion (about the site’s ineffectiveness), this site is a quick way to earn money and position yourself for your dream role.
  • And many more…

In conclusion, this book is a condensed guide with practical strategies to make the job search process less stressful, and help readers quickly get hired. So get the ebook on Amazon, and get started on a lucrative career!

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