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  • Looking for the best ways to transition into data science?

  • Well, some degrees can give you a massive advantage.

  • And a degree in C-S certainly qualifies you for this rewarding and challenging career.

  • Welcome to this 3-6-5 Data Science series of videos where we discuss how to transition

  • into data science.

  • Today, we'll be making the switch from Computer Science and explore the steps you need to

  • take to enter one of the hottest career fields.

  • We'll answer some of the most important questions that go through your head, like:

  • Can I”, “Should I” andHow can I” make this switch.

  • We'll discuss the pros and cons and give you some tried-and-tested tips to transition

  • into Data Science.

  • Let's start withCan 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-savvy professional with strong analytical thinking,

  • and a knack for creative tech solutions - which makes you the top choice of data science employers.

  • Professionals with that degree are skilled in mathematics and problem solving.

  • Not to mention they are already proficient in several programming languages and tools.

  • No wonder 18.3% of current data scientists have majored namely in Computer Science!

  • So, let's explore in detail the major points Computer Science helps you score :

  • The first and the most important advantage a C-S background gives you is spectacular

  • problem-solving skills.

  • Computer Scientists thrive in challenging situations.

  • And solving complex issues is just a regular part of their lifestyle!

  • Basically, what they do on a daily basis is identifying a problem, translating it to the

  • computer, and finding the smartest way to deal with it.

  • Over and over again.

  • A C-S graduate rushes in and finds solutions where others fear to tread which makes them

  • a leading figure in any data science team.

  • Second - writing a code that's reusable and understandable by others.

  • This is one of the most precious skills for everyone working in data science.

  • Why is that?

  • For one thing, it saves a lot of time for everyone involved.

  • If your code is very hard to follow, no one will want to use it.

  • Especially in a fast-paced business environment where data science teammates should work like

  • a well-oiled machine.

  • On the other hand, writing readable code that complies with the best practices speaks volumes.

  • It shows you're good at explaining your way of thinking to others, which is undeniably

  • crucial for a data scientist working within a cross-functional team.

  • As a C-S person, you obviously know how to do that, so this box is ticked!

  • And thirdhaving a super-versatile toolbox.

  • Data scientists rarely fly solo.

  • That said, your ability to work with TTD or version control systems, like Git, for example,

  • is indispensable to managing the code: including past changes, speed of execution, and development

  • of the project.

  • A data science team needs someone who knows how 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.

  • Alright.

  • We believe now you know transitioning into data science from Computer Science is not

  • a question ofCan I?”

  • rather thanShould I?”

  • Well, every person is different and so are their career choices.

  • Data Science has been recentlydiscoveredand giving it a worldwide meaning seems to

  • be a problem.

  • Because of that, understanding the data science industry is a tough job.

  • We might say that in most places being a Data Scientist will require you to work in a chaotic,

  • continuously developing and challenging environment.

  • And, yes, 20 years ago, there wasn't a Data Science job

  • And you may askWhy?”

  • The main reason is that there wasn't that much data to work with.

  • But this is not the case now.

  • There are 2.5 quintillion bytes of data created daily and businesses are in dire need of people

  • working on it to improve our lifestyle, health and more

  • In fact, the demand for data science professionals is 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 base salary and why reports like Glassdoor's

  • 50 Best Jobs have consistently named Data Science the winner for the past few years.

  • Consider thisdata science today is very close to how computer science was perceived

  • back in 2005.

  • Actually, D-S and C-S are very similar in that they are following the same demand and

  • supply laws

  • But only with a 20-year difference.

  • So, you might as well take advantage of that before the market gets overcrowded with highly

  • trained data scientists and salaries start to plateau.

  • So, how to do that?

  • Knowing how to code has already put you on the fast track to the DS role.

  • What you might miss in terms of knowledge is:

  • StatisticsComputer Scientists boast a deterministic mindset.

  • This compels them to want to have all possibilities covered.

  • 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 - events follow distributions and there are probabilities

  • associated with each possibility.

  • So, that's a whole new way of thinking to adapt to.

  • Machine and deep learningyou guessed right -usually, these aren't covered in

  • the C-S curriculum.

  • But it is namely sharp predictive modeling skills and advanced deep learning techniques

  • that will give you a huge competitive edge.

  • Fortunately, there are plenty of post-graduate qualifications and online trainings that will

  • help you get there.

  • Reading research papersMath, Statistics, and D-S majors are very science-oriented.

  • so reading, understanding and applying the technical methods in said paper is no challenge

  • for them.

  • But these don't come naturally to a C-S graduate.

  • That said, being able to apply concepts from papers is the number 1 skill demanded in top

  • companies, so adding research to your reading list is certainly worth the effort.

  • Data Visualizationrepresenting a whole data 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 software tools like Tableau, Power BI, and Excel are

  • a must for any data scientist.

  • Overlooking these could be the biggest mistake of C-S graduates.

  • Rememberin the business world, sometimes it is about completing a task in 5 minutes

  • and not about writing the most parameterized code.

  • Okay.

  • So, if you've set your sight on making the switch, 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 around the world and are committed to continue doing

  • so.

  • If you are interested to learn more, you can find a link in the description that will also

  • give you 20% off all plans if you're looking to 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'll face is working efficiently with both C-level

  • executives and team members with various backgrounds and fields of expertise.

  • So, if you think that employers are only looking for top technical talentyou're wrong.

  • A data scientist should also be a great team player.

  • According to an internal study ran by Google, the most inventive and effective teams within

  • the corporation weren't the ones full of top scientists.

  • Instead, their best performers were interdisciplinary groups with employees who brought strong soft

  • skills to the table and enhanced the collaborative process

  • Which brings us to Leadership.

  • As a data scientist, you will not only plan projects, and build analytic systems and predictive

  • models.

  • You will also be the leader of a data science team.

  • 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 teach others or be taught yourself, regardless of

  • their level in the hierarchy.

  • So, keeping an open mind to new and challenging ideas is a must.

  • But don't worry if you don't feel you're cut out to be a leader just yetas long

  • as you have empathy, integrity, and the desire to listen to your team's needs and concerns,

  • you can grow to become an outstanding Lead Data Scientist.

  • Alright!

  • In this video, we discussed that Computer Science majors can, and should, try to pursue

  • a career in data science because they have the necessary skills and there is high market

  • demand.

  • Surely, programming skills are mandatory for any data scientist.

  • Thus, there is no doubt that you, dear C-S major, could be a successful one.

  • Good luck!

  • If you liked this video, don't forget to hit thelikeorsharebutton!

  • And if you'd like to become an expert in all things data science, subscribe to our

  • channel for more videos like this one.

  • Thanks for watching!

Looking for the best ways to transition into data science?

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データサイエンスへの移行方法:コンピュータサイエンスからデータサイエンスへ (How to Transition into Data Science: from Computer Science to Data Science)

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    林宜悉 に公開 2021 年 01 月 14 日
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