I have been job hunting for a while now. Although I still didn’t meet my professional partner in my data science journey, I have been actively doing interviews with recruiters and companies. I certainly had a huge increase in my profile views by recruiters as well as inmail offers, since I decided to take things seriously and treat with care my LinkedIn profile. Since then, I learned a lot and I want to share a bit with all of you.
For the last months, I did have to screen many job posts on a daily basis. I kept trying to find to way to being noticed by recruiters. As much as you might hate them, they are an important part of the game in many countries. As much I did try, I had no profile views at all besides my curious former colleagues or classmates. I did upload my resume, my portfolio, my profile was “All-starred”, but no clue what I was doing wrong.
So, one day tired of what seemed a non-end game, I decided to take things to the next level. I rewrote my resume, with the help of an English native copywriter who fancied a bit the way I explained things. And then, I started to investigate about the importance of keywords in job boards.
Since that day everything changed. I no longer saw job posts as only a way to apply for a job but also as a source of information. There is plenty of data in them. I hired a premium account for some weeks, and methodically and carefully extracted the skills that are more requested for data analysis jobs in Europe.
⇒ Note: I am pointing out that jobs are within Europe because the context is important while understanding this analysis. AI is winning its way to the top of the mountain, but don’t be surprised if you don’t see it listed in the top ten skills you better have on your profile, as it is still miles away to the top.
Let’s enumerate it.
11 .- Machine Learning.
No surprise at all. Machine Learning models and algorithms is a skill must have for a data Analyst Scientist. My advice is to write machine learning as a skill in your skill set, and if you think proper, to list under your job position or education the algorithms and techniques you have been working with. Contrary to some people thinking, avoid pointing out techniques that you have heard about but you are not enough familiarised to answer a technical question about.
10 .- Java
A huge quantity of data collection nowadays come from the Internet or mobile devices. Java’s versatility to be run in any environment make it a good skill to know. Besides that, some Python or R libraries use Java to build interactive data visualisation or dashboards. Don’t panic if you don’t know how Javanize, but if you know it is good to have in your skill box.
9 .- Business Analysis
Business acumen it is, in my opinion, one of the most undervalued skills when talking about data scientist. While most people focus on just updating technical skills, we do not need to forget that all companies seek business success with their products. Therefore Having a business analyst mindset help in making the right question and therefore learning what to seek while performing data analysis. The saying ““Garbage in, Garbage out”” can also be considered when knowing how to make the right question or data interpretation. In the era of MOOC’s I am pretty sure you can easily find any that solves your gap on that subject (if any).
8 .- Management
It is difficult to be able to defend this skill if you don’t have it because of your professional background. Management of teams or projects is the most common source of this skill. It is not only about making tough decisions, it is about solving problems, taking the shots in no time, assuming your own responsibility and your team’s,… My advice is do not list it if you cannot prove it.
7 .- R
R is an open source programming. Although when I was introduced to it, I struggled a bit in my first week, it is very easy to learn. I followed swirl tutorial from Coursera, as well as DataCamp courses. But the best way to learn it it is doing projects, getting stuck (we, all did) and check with others in StackOverflow if your problem was already been shared by others and resolved (sure, it did!). Of course, you must know the most important packages for machine learning and data visualisation, as you might be asked in job interviews about them.
6 .- Python
Python is a very versatile language and because of that, it is a must-know skill. Even if you find it more difficult than R, let me tell you that ““resistance is futile””. Python is not only the language of Big Data, and online data streaming. It is emerging as the AI language and because of that, it seems that is a programming language that will increase its number of users. All the cloud environments can deal with Python and that is an advantage in front of R. Although its performance is not always the best one, I am quite sure its computation speed will increase with the appearance of new models and libraries. I learned Python mostly through Jose Portilla’s courses at Udemy and thousands of books and other MOOCs. As I wrote before, StackOverflow is your new table “book”.
5 .- Project Management
Don’t be so surprised on seeing project management listed in 5th position. Each Data Science position consists of a bunch of projects to be done within a company. Time management, leadership, proper documentation techniques, managing expectations across stakeholders, Agile methodology, sprints, and many other subskills must be familiar to you if you want to be successful in this field. Being diligent and fulfilling the main objectives of your science with data is a key point in bringing projects to a good end…and keep your job!
4 .- Microsoft Excel
I love seeing Ms Excel in 4th position before than Machine Learning or Python. I must say that shocked me a little bit everytime I saw Excel requirements in the skill requirements of a job post. I have to admit that I, literally, grew up with Excel. I got promoted because I could do things other couldn’t…with Excel, and that made me visible on IT Dept, Sales and even the Executive committee. I was the call to be made when my CEO get stuck when doing numbers. So do not underestimate Excel. It is easy to learn even at an advanced level, and it is useful. Remember that, because historically needs, most people in the organisation understand and works with Excel but not many people understand R or Python. You will need to make interactions with business people. Most of them, probably, in a better position in the organigram. Excel will be the common language and its potential it is not known by many people.
3 .- Data Analysis
This is a generical skill to list. It is empty of meaning specifically because it wraps many other tools listed and not listed here. I do think the reason why this skill is in a 3rd position it is because job posts are posted by Human Resources and recruiters. Generic skills, like this one, are what it is called hygienic, a must have with no discussion. You have it, right? so list it under your profile, please.
2 .- Microsoft Office
Surprisingly the second position is also a generic one. They write Ms Office, but we could have seen here ““learning to write””, or ““use of the internet””. This is the reason on why I listed 11 skills and not 10. It is up to you to have it on your set or not. Microsoft PowerPoint or Microsft Word did not make their way to the top, but surprisingly both are also required in many posts. List it if your skill set is not full, but be ready to erase as soon as you find skills that are more suitable for the specific positions you are looking for.
1 .- SQL
Data extractions are going to be unavoidable. Most databases are built on SQL language, so this is the number one skill. If you don’t know it, you can also catch it up with some courses at Udemy or other free online sources. It is not difficult to have the basic knowledge to be able to manage. Python and R also have some libraries that simulate an SQL engine. This is number one, so run to grab a book or start a MOOC as soon as you finish this post.
Do you agree with this analysis? do you find any other skill unlisted?