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What Is the Right Path to Get into the Data Field?

My personal answer to this question, based on my own experience

In the last months, I’ve seen a trend: more and more people want to get into the analytics field, but they need to be guided in choosing the right path.

First of all, I’d like to publicly thank all of the folks who spent some time sending me a direct message on Linkedin or an email: I’d really like to thank you for reaching out, and asking for my opinion. Also, I’d like to thank all of the people who wrote me messages of encouragement to keep writing: this is really "gasoline for my engine."

In this article, I’d like to tell you my personal answer to a typical question I get asked: what is the right path to get into the Data field?

Of course – but let’s say it again – this is just my opinion, based on my experience.

1. You do not need a Ph.D. or a Master’s Degree

Let’s start easy (and with a denial): you do not need a Ph.D. or a Master’s Degree.

Read it again.

Well, of course, having an advanced degree will help you, but you do not need it.

I believe the fact that people think they need a Ph.D. to get into the data world is because, until recent times, data-related jobs (especially, the role of the Data Scientist) were got by people in the research field. The fact is that, in the last few years, these kinds of jobs have exploded by the side of demand, but there is a lack of skill in the job market, by the side of the supply; and this is where we arrive at the point that you do not need a Ph.D. or a Master’s Degree. Also, consider a basic fact: not every firm has an R&D department, nor do they make research: most of the firms today just need data to be analyzed to gain insight, or they need predictions (i.e. forecast on sales) and these jobs can be performed with the "right" knowledge and the "right" practical competencies, surely without a Ph.D. or a Master’s Degree.

Of course: you need knowledge and, moreover, you need practice; but today, practice is more important than degrees. Well, saying the truth, we are living in a time when it seems the practice is more appreciated than the degrees, generally speaking.

So, if you want to get a Ph.D. or a Master’s Degree it’s up to you (and good luck with your studies!), but you really do not need them to get into the field.

2. "Good; so, where do I start?"

The second question I usually get asked is: "where do I start?"

Or: "all of these things to study make me overwhelmed: what should I focus on? Which learning path should I follow?"

These are the hardest questions to answer, and I tell you why.

There are two "difficulties" :

  1. the field of data is quite immature, and it often happens that one should do "transversal" job tasks.
  2. even if you get a job in which you have defined tasks to perform, you have to understand what you like to do.

The second one is the most difficult one. Let’s repeat it again: you have to understand what you like to do.

Let me tell you a thing: if you want to get into the data world just because there are high-paying jobs and the possibility of remote work, you won’t get it.

Believe me.

The path is hard and you have to like it: otherwise, you won’t go anywhere.

So, my personal answer to this question is to take your time (yes, months!), so that you begin loving the learning path, and you start understanding what you like to do. If you want to read my recommendations on how and why enjoining the Data Science learning path, you can read the following article:

Why (and how) enjoying the learning path in Data Science

I know, there is a lot of stuff to study, you need to practice a lot, and you feel overwhelmed. I understand your struggle, and this is why my recommendations are the following:

  • Start with Python. Python is a very flexible programming language and it’s one of the most requested in the world, for different industries. Even if, in the end, you’ll choose to be a Data Analyst or a Business Analyst, having Python knowledge is becoming more and more important. So, get a Python course and go on. I haven’t a particular Python course to suggest to you, but, in this article, I wrote my recommendations on how to study Python for Data Science. Summarizing: learn the basics, go on with Data Analysis with Python and, if you feel comfortable, study some Machine Learning.
  • Go on and learn SQL basics. If you want to work in the data field, regardless of the role, you’ll need to extract data from a database. So, learning SQL basics is very important. If you want to start for free before taking a course, so that you’ll understand if you like it and how to deepen the concepts, you can start here.
  • Learn Tableau/powerBI. These are the most used tools for data visualization. There is no need to learn both; my opinion here is that you have two possibilities: 1) you really want to work for a particular firm; in this case, you have to understand which of the two they use and learn it. 2) just learn one of the two; the truth is that the important thing is to gain experience in visualization, regardless of the tool; the mindset you’ll develop is more important than the tool you use: you can learn another tool on the going of your job when you get it.
  • Deepen your Excel expertise. I know, almost everyone uses Excel but when it comes to analyzing data there are some interesting functions to know. Here my recommendation is to take some YouTube videos on data analytics techniques in excel and make some exercises.

So, apart from this list of things to study and practice, the point here is to understand what you like, as I said in the beginning. Read the various job descriptions in job search portals (and on Linkedin), see the required hard skills, and try to understand if you’d like the job; also, while networking, talk to people and ask them what they do in their job.

Let’s simplify just for a better understanding:

  • A Data Analyst spends most of the time in databases, creating dashboards with tableau/powerBI. Do you like doing that the whole day?
  • A Data scientist spends most of the time analyzing data and finding the ML model that best fits the data. Do you like doing that the whole day?

And, furthermore, another fact to understand is that your working day will be different if, for the same job position, you’ll work for a startup or for a corporation. Simplifying:

  • Startups are generally small firms where you do not have a pre-defined job: this means that your job will probably be "transversal"; I mean that you may grab the data if you do not have them, analyze them, make some predictions with ML, make some dashboards, and so on; so, the question is: do you like discovering new things with no borders, or does it make you feel overwhelmed?
  • In big corporations, you’ll generally have a predefined job; if you want to be a Data Analyst, for example, in a big corporation your job will probably be focused the most on databases and dashboards; do you like exploring new things but inside certain borders, or does it get you bored?

So, those are the reasons why it takes months: because you need to understand which hard skill you’d like to develop and in which kind of environment you’d like to work.

Also, if you work full-time and want to change your career to get into the data field, you may find it hard to study while working full-time. Let me tell you that I truly and deeply understand you: I completed my Master’s in Data Science while working full-time and having a family. So, if you want my recommendations on how to study Dat Science even if you work full-time read my article here:

How to Study Data Science even if you Work (or Study) Full Time

3. Bonus: how to accelerate your career to get into the data field

As I said, the path to get into the data field is long and hard, but there are some ways to accelerate it:

  1. Networking. Networking is a gold mine because knowing people and building relations with them will really boost your career; you can’t imagine how often it can happen that a person you met can refer you for a job.
  2. Writing. Writing can boost your career because more and more firms need people to write articles on their AI products; also, sometimes they may need articles to inform people about ML and AI; this is a typical example of a freelancing job (and you can even do it on the side of your current employee job).
  3. Create projects dedicated to a particular firm. If you find the perfect firm to work for, a good idea is to create a personal project in which you show your skills helping them gain some results with your data abilities. For example: do you want to work in a marketing firm? Showing them how you can use ML to cluster customers can get you the job!

If you are a newbie and you want to learn Python and Data Science, then consider that I can coach you on this beautiful learning path. Contact me and let’s talk about that (see my contacts below).

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