Looking for the best ways to transition intodata science? Well, some degrees can give you a massiveadvantage.
And a degree in C-S certainly qualifies youfor this rewarding and challenging career.
Welcome to this 3-6-5 Data Science seriesof videos where we discuss how to transition into data science.
Today, we’ll be making the switch from ComputerScience and explore the steps you need to take to enter one of the hottest career fields.
We’ll answer some of the most importantquestions that go through your head, like: “Can I”, “Should I” and “How canI” make this switch.
We’ll discuss the pros and cons and giveyou some tried-and-tested tips to transition into Data Science.
Let’s start with “Can I make the switch?” Well, if you can’t, then no one else can.
A degree in C-S prepares you to be a code-savvyprofessional with strong analytical thinking, and a knack for creative tech solutions – whichmakes you the top choice of data science employers.
Professionals with that degree are skilledin mathematics and problem solving.
Not to mention they are already proficientin several programming languages and tools.
No wonder 18.
3% of current data scientistshave majored namely in Computer Science! So, let’s explore in detail the major pointsComputer Science helps you score : The first and the most important advantagea C-S background gives you is spectacular problem-solving skills.
Computer Scientists thrive in challengingsituations.
And solving complex issues is just a regularpart of their lifestyle! Basically, what they do on a daily basis isidentifying a problem, translating it to the computer, and finding the smartest way todeal with it.
Over and over again.
A C-S graduate rushes in and finds solutionswhere others fear to tread which makes them a leading figure in any data science team.
Second – writing a code that’s reusableand understandable by others.
This is one of the most precious skills foreveryone working in data science.
Why is that? For one thing, it saves a lot of time foreveryone involved.
If your code is very hard to follow, no onewill want to use it.
Especially in a fast-paced business environmentwhere data science teammates should work like a well-oiled machine.
On the other hand, writing readable code thatcomplies with the best practices speaks volumes.
It shows you’re good at explaining yourway of thinking to others, which is undeniably crucial for a data scientist working withina cross-functional team.
As a C-S person, you obviously know how todo that, so this box is ticked! And third – having a super-versatile toolbox.
Data scientists rarely fly solo.
That said, your ability to work with TTD orversion control systems, like Git, for example, is indispensable to managing the code: includingpast changes, speed of execution, and development of the project.
A data science team needs someone who knowshow to monitor timelines or check if the code is labeled properly.
Not many people are highly skilled at that, but a C-S graduate has the know-how that certainly gives them an edge.
We believe now you know transitioning intodata science from Computer Science is not a question of “Can I?” rather than “Should I?” Well, every person is different and so aretheir career choices.
Data Science has been recently “discovered”and giving it a worldwide meaning seems to be a problem.
Because of that, understanding the data scienceindustry is a tough job.
We might say that in most places being a DataScientist will require you to work in a chaotic, continuously developing and challenging environment.
And, yes, 20 years ago, there wasn’t a DataScience job… And you may ask “Why?” The main reason is that there wasn’t thatmuch data to work with.
But this is not the case now.
There are 2.
5 quintillion bytes of data createddaily and businesses are in dire need of people working on it to improve our lifestyle, healthand more… In fact, the demand for data science professionalsis so high that it will be hard for the supply to catch up for many years to come! That also explains the $100, 000+ median basesalary and why reports like Glassdoor’s 50 Best Jobs have consistently named DataScience the winner for the past few years.
Consider this – data science today is veryclose to how computer science was perceived back in 2005.
Actually, D-S and C-S are very similar inthat they are following the same demand and supply laws… But only with a 20-year difference.
So, you might as well take advantage of thatbefore the market gets overcrowded with highly trained data scientists and salaries startto plateau.
So, how to do that? Knowing how to code has already put you onthe fast track to the DS role.
What you might miss in terms of knowledgeis: Statistics –Computer Scientists boast adeterministic mindset.
This compels them to want to have all possibilitiescovered.
And that’s great, but, to be a data scientist, you need to shift to a statistical or even better – a probabilistic mindset.
Why? Well, because of how data science works – eventsfollow distributions and there are probabilities associated with each possibility.
So, that’s a whole new way of thinking toadapt to.
Machine and deep learning – you guessedright -usually, these aren’t covered in the C-S curriculum.
But it is namely sharp predictive modelingskills and advanced deep learning techniques that will give you a huge competitive edge.
Fortunately, there are plenty of post-graduatequalifications and online trainings that will help you get there.
Reading research papers –Math, Statistics, and D-S majors are very science-oriented.
so reading, understanding and applying thetechnical methods in said paper is no challenge for them.
But these don’t come naturally to a C-Sgraduate.
That said, being able to apply concepts frompapers is the number 1 skill demanded in top companies, so adding research to your readinglist is certainly worth the effort.
Data Visualization – representing a wholedata research on just a few graphs and tables is a major component of a data scientist work.
And it’s not an easy task.
So, while you may prefer to code, adding softwaretools like Tableau, Power BI, and Excel are a must for any data scientist.
Overlooking these could be the biggest mistakeof C-S graduates.
Remember – in the business world, sometimesit is about completing a task in 5 minutes and not about writing the most parameterizedcode.
So, if you’ve set your sight on making theswitch, we’ve got you covered.
We developed the ‘3-6-5 Data Science Program’to help people of all backgrounds enter the field of data science.
We have trained more than 450, 000 people aroundthe world and are committed to continue doing so.
If you are interested to learn more, you canfind a link in the description that will also give you 20% off all plans if you’re lookingto start learning from an all-around data science training.
But even with these skills under your belt, data science is no easy street.
In fact, one of the biggest challenges you’llface is working efficiently with both C-level executives and team members with various backgroundsand fields of expertise.
So, if you think that employers are only lookingfor top technical talent – you’re wrong.
A data scientist should also be a great teamplayer.
According to an internal study ran by Google, the most inventive and effective teams within the corporation weren’t the ones full oftop scientists.
Instead, their best performers were interdisciplinarygroups with employees who brought strong soft skills to the table and enhanced the collaborativeprocess… Which brings us to Leadership.
As a data scientist, you will not only planprojects, and build analytic systems and predictive models.
You will also be the leader of a data scienceteam.
And managing a team of other data scientists, machine learning engineers, and big data specialists requires more than drive and vision.
In a data science team, you can always teachothers or be taught yourself, regardless of their level in the hierarchy.
So, keeping an open mind to new and challengingideas is a must.
But don’t worry if you don’t feel you’recut out to be a leader just yet– as long as you have empathy, integrity, and the desireto listen to your team’s needs and concerns, you can grow to become an outstanding LeadData Scientist.
Alright! In this video, we discussed that ComputerScience majors can, and should, try to pursue a career in data science because they havethe necessary skills and there is high market demand.
Surely, programming skills are mandatory forany data scientist.
Thus, there is no doubt that you, dear C-Smajor, could be a successful one.
Good luck! If you liked this video, don’t forget tohit the “like” or “share” button! And if you’d like to become an expert inall things data science, subscribe to our channel for more videos like this one.
Thanks for watching!.