Four Useful Questions About Starting a Career in Data Science, and Their Answers
By: Khan Muhammad Saqiful Alam
Tags: Big Data, Analytics, Skills, Career Paths, Data Science

Photo by Emily Morter on Unsplash

There are countless discussions about getting started in a career in data science, and many young professionals and students are setting up their career path in this field. In fact the search term “Data Science Advice” gives about 1.2 billion results in Google! So this article, instead of giving direct advice, focuses on answering a few specific questions that I have repeatedly answered over the past few years being a data science enthusiast.

So, without much ado, let’s start!

Photo by Jon Tyson on Unsplash

Question 1 — What are the requirements/skills/pre-requisites to be a Data Scientist?

Ans: The term data scientist is used quite broadly these days. There are data scientists in business, medical sciences, genetic engineering, computer science etc. As such, one major requirement will be based on what goal you are targeting. Based on which field you want to apply data science to, the first thing you will need is Domain Knowledge. This is an advice I share based on my experience in this field. We have many tools in Data Science for understanding how different variables impact results and which variables are important for a model, but time and again, in many use cases (actually, almost all), I have found it is the in-depth understanding of the domain that sets apart a really good model and analysis!

The next thing is a good conceptual idea on Statistics and Differential Calculus. Initially, when you are going to do exploratory data analysis, patterns, dimensionality reduction, imputing missing data, and pre-processing data for machine learning models, and when carrying out statistical analysis such as regression, you will need a good idea on Statistics. When you advance further, Calculus will help as well — for optimization problems, ensemble models, gradient boosting and many other advanced methods.

Finally, in terms of software skills, you can start with PowerBI or Tableau, for data intelligence and insights, and quick dashboards. But then in order to actually venture into data science, you will need to learn to code. For that you will need either R or Python. There is a big debate on which one to start with, and there are pros and cons to both the languages, but frankly, I think understanding one will help you understand the other, and I end up working interchangeably between the two, based on the availability of the right package!

Question 2: Does Data Science have any international certification course? How much does it cost?

Ans: There are many. There are online courses, diplomas, and masters and undergraduate degrees. And the cost varies a lot.

But here, I will again share my humble opinion — in the end the degree will not determine your outcome to be a data scientist. Data Science is a very applied field, and as such, for you to be employed as a data scientist or make a mark in the market, you need to have projects. It doesn’t only mean work projects. There are loads of data available in Kaggle, Google Database Search, UCI Machine Learning repository, and just take a data set, and work on it, trying to find patterns and trying to make predictions, measuring accuracy of prediction models etc. In fact, I am a huge proponent on trial and error learning — keep on trying different projects, and you will fail do initially do what you wanted to do, and then learn how to do it, and that learning will stick!

Photo by Max Chen on Unsplash

Since Data Science is an ever changing field, the contents and the tools are also ever changing. So to be very honest, no university curriculum or online course will be able to cover everything, especially the topics which are on the fringe or new topics. We try to keep us updated through blogs, new methods coming out, academic research and by these data projects. You can create these projects and store them in GitHub. When I am checking the ability of a data scientist applicant, I will actually prefer to go through her GitHub Repository or Kaggle Discussion!

Question 3: How much does it take to become a Data Scientist?

Ans: Again, no clear answer. Can’t even suggest a number range for the monetary costs. It completely depends on what path you chose. If you go for a master’s, then the costs will be higher, but you will have an accredited certification. If you base on online courses and finding materials yourself, the cost will be much less, but the value of certification will be less as well. But remember my answer to the second question — what matters more is the projects you have.

When looking at cost from the perspective of time, it’s quite high. For me, I was a full time lecturer in a university, in a related topic, and so it took me almost 1 to 1.5 years, to slowly learn and dive very deeply into both the concepts and the coding. It depends on how much time you can give. I used to spend 4/5 hours per week at least. The time commitment also varies based on whether you know how to code (in which case it will be less than the time span i mentioned) and whether you have some basic background knowledge in statistics and algebra (if you don’t, the time span will be a little bit higher!)

Photo by Antoine Dautry on Unsplash

But a very important point here is that the learning never ends — I end up learning new stuff almost every other day, and have a certain time in the week dedicated for learning!

Question 4: Where can I get proper guidelines regarding Data Science?

Ans: Similar to my answer to the second question, unfortunately there is no single institution, group, platform that holistically covers all of the things that you need to know about Data Science. And that, in my opinion, is the fun with this field! You end up wandering around, and at times, you find something better!

Regardless, there are many groups in social media and in places like, GitHub and Kaggle, and in Medium, where you can always interact and keep abreast with the new information. I think the best advice here is to learn and collect information from many sources. Medium has some very good articles and publications. DataCamp blog is quite good. Also Kaggle’s blog and following Kaggle competitions help a lot! Finally, GitHub has access to some really good codes, libraries and data projects.