Journey of Analytics

Deep dive into data analysis tools, theory and projects

Author: anu - Journey of Analytics Team (page 1 of 4)

Top 10 Most Valuable Data Science Skills in 2020

The first month of the new decade is almost at an end. It’s also “job-hunting” time when students start looking for internships and employees think about switching roles and companies, in search of better salaries and opportunities. If you fall into one of these categories, then here are the Top 10 skills your resume absolutely needs to include, to get noticed by employers and land your dream job.

Data Science Skills for 2020


I looked at 200 job descriptions for jobs posted on LinkedIn in 7 major US/Canada cities – San Francisco, Seattle, Chicago, New York, Philadelphia, Atlanta, Toronto. Let’s face it – LinkedIn is the go-to platform for job seekers and recruiters. So looking at any other site seems a waste of time.

The job listings included many of the top Global brands in tech (Microsoft, Amazon, etc.), product (AirBnb, Uber, Visa), consulting (Deloitte, Accenture), banks (JP Morgan, Capital One) and so on. I only considered jobs with the title “Data scientist” or “Data Analyst”, with 150+ in the former. It took a while, but doing this manually also allowed me to exclude repetitive postings, since some companies post same role for multiple locations.

Ultimately, this allowed me to quickly identify patterns and repeated skills, which I am presenting in this blogpost.

I’ve categorized the skills into 2 parts: Core and Advanced. Core skills are the absolute minimum you should have, recruiters and automated job application systems will simply disqualify you without them. Advanced skills are those “preferred” competencies that make you look more valuable as a candidate, so make sure to highlight them with examples on your resume. So, if you are trying to transition to a career in Data Science, then I would highly recommend learning these first, and then jumping into the others. Needless to say, everyone working (or entering) this field needs to have a portfolio of projects.

Disclaimer – having all the 10 skills does NOT guarantee a job but vastly improves your chances. You’ll still need to do some legwork, to get considered and my book “Data Science Jobs” can help you shorten this process. The book is also on SALE for $0.99 this weekend, Jan 25th to Jan 28th, at a 92% discount.

Core Skills:

Minimum qualifications for Data Scientist roles

[1] Programming (R/Python): This is a no-brainer, you need to be an expert in either R/Python. Some jobs will list SAS or other obscure languages, but R or Python was a constant and mandatory requirement in 100% of all the jobs I parsed.

I am not going to argue the merits of one over the other in this post, but I will emphasize that R is still very much a in-demand skill. Plus, for most entry level roles, a candidate with only Python is not going to be considered more favorably (or declined!) over someone who knows only R. In fact, at my current and previous 2 roles, R-programming was the preferred language of choice. If you’d like to know my true views on the R vs Python debate, read this post.

[2] SQL: Most colleges and bootcamps do not teach this, but it is inordinately valuable. You cannot find insights without data, and 99% of companies predominantly use SQL databases of some kind. Fancy stuff like MongoDb, NoSQL or Hadoop are excellent keywords to add to your bio, but SQL is the baseline. You don’t need to know stored procedures or admin level expertise, but please learn basics of SQL for pulling in data with filters and optimizing table joins. SQL is mandatory to thrive as a data scientist.

[3] Basic math & Stats: By this I mean basic high-school stuff, like calculating confidence intervals and profit-loss calculations. If you cannot distinguish between mean and median, then no self-respecting manager will trust your numbers, or believe your insights have excluded those pesky outliers. Profits, incremental benefits in $ are other useful formulae you should know too, so brush up on your business math.

[4] Machine Learning Algorithms: Knowing to code algorithms is expected, but so is knowing the logic behind them. If you cannot explain it in plain English, you really don’t know what you are talking about!

[5] Data Visualization: Tableau is the preferred technology, although I’ve seen people find success with Excel charts ( Excel will never die! ) and R libraries, too. However, I definitely see Tableau dominating everything else in the coming years.

Advanced Skills:

Advanced Data Science Skills that make you indispensable!

[6] Communication skills: A picture is worth 1000 words; and being able to present data in meaningful, concise ways is crucial. Too many newbies get lost in the analysis itself, or hyper-focused on their beautiful code. Most managers want to see recommendations and insights that they can apply in practice! So being able to think like a “consultant” is crucial whether you are entry-level or the lead data scientist.

Good presentation skills (written and verbal) are important, more so for any dashboards or visualization reports, and I don’t mean color palettes or chart-types. Instead, make sure your dashboards are not “data-vomit”, a very practical (and apt!) term coined by Avinash Kaushik. If users cannot make head or tail of the dashboard without your handholding, or if the most important take-away is not obvious within 5 seconds, then you’ve done a poor job.  

[7] Cloud services: Most companies have moved databases to AWS/Azure, and many are implementing production models in the cloud. So, learn those basics about Docker, containers, and deploying your models and code to the cloud. This is still a niche skill, so having it will definitely help you stand apart as most companies make the move towards automation.

[8] Software engineering: You don’t need to become a software engineer but knowing basic architecture and data flow Qs will help you troubleshoot better, write better code that is easily moved to production. Some Qs to start – what is the data about, where (all) is it coming from? Learn about scheduler jobs and report automation, these have helped me automate the most boring repetitive tasks and look like a superstar to my managers! The infrastructure teams do extremely valuable work (keeping things running smoothly) so learn about “rules” and expectations, and make sure your code conforms to everything. I always do, and my requests are treated much better! ?

[9] Automated ML: This is slowly getting popular, as companies try to cut costs and improve efficiencies with automation. and DataRobot are just 2 names off my head, but there are many more vendors in the market. If possible, learn how to work with those, as they can reduce your time for analysis and speed up production deployment. They won’t replace good data scientists, but they do magnify the disparity between someone who is mindless copy/pasting code and the truly efficient data scientists. So make sure your “core” skills are impeccable.  

[10] Domain expertise: Nothing beats experience, but even if you are new to the company (or field) learn as much as you can from senior colleagues and partner teams. Find out the “why/how/what” Qs – who is using the analysis results, why do they truly want it? How will it be applied? How does it save the company money or increase profits? How can I do it faster while maintaining accuracy, and also adding to the bottom line? What metric does the end user (or my manager) really care about?

As Machine learning software add more automation and features, this blend of technology and domain expertise will ensure you are never a casualty of layoffs or cost-cutting! I’ve put this at the end, but really you should be thinking about this from DAY ONE!

For example, my current role involves models for credit card fraud prediction. However, once I learned the end-to-end process of card customer lifecycle (incoming application, review, collections, payments, etc.) my models have become much better. Plus, I have deeper understanding of Fair banking and privacy laws which can prevent many demographic variables from being used in modes. Similarly, a friend working in the petrochemical industry realized that his boss cared more about preventing true negatives (Overlooking or NOT maintaining end-of-life or faulty sensors that can potentially cause leaks or explosions ) than false positives (unnecessary maintenance for good sensors), even though both models can give you similar accuracy.

So build these skills, and see your career and salary potential sky-rocket in 2020!

Who wants to work at Google?

In this tutorial, we will explore the open roles at Google, and try to see what common attributes Google is looking for, in future employees.


This dataset comes from the Kaggle site, and contains text information about job location, title, department, minimum and preferred qualifications and the responsibilities of the position. Using this dataset we will try to answer the following questions: You can download the dataset here, and run the code on the Kaggle site itself here.

  1. Where are the open roles?
  2. Which departments have the most openings?
  3. What are the minimum and preferred educational qualifications needed to get hired at Google?
  4. How much experience is needed?
  5. What categories of roles are the most in demand?

Data Preparation and Cleaning:

The data is all in free-form text, so we do need to do a fair amount of cleanup to remove non-alphanumeric characters. Some of the job locations have special characters too, so we remove those using basic string manipulation functions. Once we read in the file, this is the snapshot of the resulting dataframe:

Job Categories:

First we look at which departments have the most number of open roles. Surprisingly, there are more roles open for the “Marketing and Communications” and “Sales & Account Management” categories, as compared to the traditional technical business units. (like Software Engineering or networking) .

Full-time versus internships:

Let us see how many roles are full-time and how many are for students. As expected, only ~13% of roles are for students i.e. internships. Majority are full-time positions.

Technical Roles:

Since Google is predominantly technical company, let us see how many positions need technical skills, irrespective of the business unit (job category)

a) Roles related to “Google Cloud”:

To check this, we investigate how many roles have the phrase either in the job title or the responsibilities. As shown in the graph below, ~20% of the roles are related to Cloud infrastructure, clearly showing that Google is making Cloud services a high priority.

Educational Qualifications:

Here we are basically parsing the “min_qual” and “pref_qual” columns to see the minimum qualifications needed for the role. If we only take the minimum qualifications into consideration, we see that 80% of the roles explicitly ask for a bachelors degree. Less than 5% of roles ask for a masters or PhD.

min_qualifications for Google jobs

However, when we consider the “preferred” qualifications, the ratio increases to a whopping ~25%. Thus, a fourth of all roles would be more suited to candidates with masters degrees and above.

Google Engineers:

Google is famous for hiring engineers for all types of roles. So we will read the job qualification requirements to identify what percentage of roles requires a technical degree or degree in Engineering.
As seen from the data, 35% specifically ask for an Engineering or computer science degree, including roles in marketing and non-engineering departments.

Years of Experience:

We see that 30% of the roles require at least 5-years, while 35% of roles need even more experience.
So if you did not get hired at Google after graduation, no worries. You have a better chance after gaining a strong experience in other companies.

Role Locations:

The dataset does not have the geographical coordinates for mapping. However, this is easily overcome by using the geocode() function and the amazing Rworldmap package. We are only plotting the locations, so some places would have more roles than others.  So, we see open roles in all parts of the world. However, the maximum positions are in US, followed by UK, and then Europe as a whole.

Responsibilities – Word Cloud:

Let us create a word cloud to see what skills are most needed for the Cloud engineering roles: We see that words like “partner”, “custom solutions”, “cloud”, strategy“,”experience” are more frequent than any specific technical skills. This shows that the Google cloud roles are best filled by senior resources where leadership and business skills become more significant than expertise in a specific technology.



So who has the best chance of getting hired at Google?

For most of the roles (from this dataset), a candidate with the following traits has the best chance of getting hired:

  1. 5+ years of experience.
  2. Engineering or Computer Science bachelor’s degree.
  3. Masters degree or higher.
  4. Working in the US.

The code for this script and graphs are available here on the Kaggle website. If you liked it, don’t forget to upvote the script. 🙂

Thanks and happy coding!

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