Name: データサイエンスへの移行方法。経済学からデータサイエンスへ (How to Transition Into Data Science: From Economics to Data Science)
Uploaded: 2021-01-14T10:49:46.000Z
Duration: 9 min 54 s
Description: VoiceTubeの動画で発音を聞きながら英語表現を覚えよう！学べる英語：

Many graduates aren't too sure what they want to do after graduation.

And one of the often overlooked options is data science.

Welcome to this 365 data science series of videos where we discuss how to transition into data science.

Today we will be making the switch from economics and examine the good, the bad and the ugly.

We'll answer some of the most important questions running through your mind like, Can I should I?

And we'll discuss the pros and cons before finding the best way to transition into data science.

Roughly 13% of current data scientists have an economics degree.

For comparison, the most well represented discipline is data science and analysis, which takes up 21% of the pie.

Therefore, economics is a competitive discipline when it comes today to science.

This isn't at all surprising for several reasons.

First, unlike stem disciplines, social studies helped develop great presentational skills, which are essential for any data scientist.

Through presentations and open discussions, students learn how to present a topic as well as argue for or against a given statement.

These activities result in developing a confident, incredible way of showcasing actionable insights.

Moreover, most e con majors deeply care about human behavior and response to different stimuli.

Hints social studies majors can capably servas mediators between the team and management.

Second, economists often have a different approach than CS RD s majors due to their superior understanding of causal relations.

Social studies graduates can add another perspective when looking at the data and the results.

This is extremely important because they're casual.

Inference allows them to think beyond the numbers and extract actionable insights.

Furthermore, economics frequently intertwines with mathematics, finance, psychology and politics.

Therefore, an economist approach is always meant to be entered.

Finally, the technical capabilities of an economist are often quite impressive.

An average economist has a good understanding of machine learning without really referring to it.

As such, linear regressions and logistic regressions are studied in almost all economics degrees.

I think we're pretty convinced about the can I part so let's move to the Should I part well, The answer here is yes, with a very small asterisk next to it.

Now, any economics graduate possesses many of the required skills to transition into data science, but that doesn't necessarily suggest they should do it.

They might be more suited for something else.

For example, an economics graduate with an affinity for political science will most likely thrive better in a policy advisory role in a bank or hedge fund, or even in a government position similarly less coating savvy social studies graduates or a finer fit for data analyst positions where machine learning algorithms are relied upon less frequently.

It's not that either one wouldn't be able to succeed as a data scientist, but their skills are better suited for different career paths.

So let's look at the question like an economist would through the lens of incentives.

The main components of a job ad are the level of education, years of experience and indispensable skills.

We already discussed how popular economics is compared to stem degrees, so you know it's a good choice for a potential career as a data scientist When it comes to economics degrees, 43% of the job ads in our research require a B A and an additional 40% a masters hints.

Due to the interdisciplinary nature of social studies, you don't need to get a doctorate to be successful in the field as four years of experience.

If you're transitioning from another position in business, you've probably had to do some analytical thinking Already.

Usually 3 to 4 years in such a setting are enough to ensure a smooth transition, but this is tightly related to your level of education.

A candidate with an M s will require two fewer years of experience in a business setting due to their additional academic qualifications.

However, if you're trying to make a transition straight out of college, you might want to go for an entry level job in the field.

By the way, when it comes to skills, one of the key parts is understanding statistical results and their implications.

Luckily, economics degrees are often based on statistical study cases and experiments, so you should feel comfortable interpreting the results.

Of course, this expands to understanding the intuition behind ML algorithms and their limitations.

As we already stated, econometrics incorporates linear and logistic regressions, so economics graduates have a great grasp of the intuition behind machine learning models.

Additional skills listed in such job ads include problem solving and strong analytical thinking.

A lot of economics degrees heavily rely on examining study cases, solving practical examples and analyzing published papers, so you probably possess these qualities already.

Of course, communication skills are essential when working in a team.

And as we mentioned earlier, economics graduates often serve as a bridge between the data science team and higher management.

Lastly, anybody making the switch to data science needs a certain coating pedigree.

Knowing how to use such software is a must.

If you're an economist in your twenties, we can assume you have seen some python are our code hints.

You only need to gather more work experience in a business setting.

If you're above 30 and you aren't a CS graduate, you most probably didn't use the computer in your university classes, so you may think your main challenge is the lack of programming skills, But that shouldn't be the case.

Just focus on the technical part programming and the latest software technologies coating has never been easier, and anyone can learn, especially a person from an economics background.

We all know you have seen some very complicated stuff.

After all, a significant part of our team is coming from a Nikon background but transitioned into data science.

In fact, we develop the 365 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're 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 after answering the can and should parts of the discussion, let's dive into the how to part.

There are generally four crucial things you need to do to make the switch.

The 1st 1 is picking your spot as discussed.

There is plenty of room for economics graduates and data science all you need to make sure you're ready to fit exactly that role and demonstrate your strengths.

Employers value your understanding of causal inference.

So you need to highlight that in your application showcased the analytical part of your work mention in science you gained through research or academic work and quote, they're measurable impact.

These bring credibility and provide recruiters with a glimpse of what they'll be getting once they hire you.

Second user social science advantage by knowing how surveys and experiments are constructed, you know where to look when examining the results, you see beyond the data and understand which ML approach should work best in each case.

In contrast, D S and C s graduates off never mind set of How can I pre process the data before I run an ML algorithm?

Instead of looking at the way the data was gathered, your understanding of culinary t reverse cause ality and biases can help you accurately quantify interdependence within data.

Thus, you can have great synergy with the rest of the members on your team.

Third and most crucial change union to make is to adapt your way of thinking, even though the cause and effect mentality will help you settle in your career.

You need to be able to look for other things as well.

The findings of neural networks algorithms can be confusing because they discover patterns rather than causal links.

Hence, you need to be ready to demonstrate flexibility and you're thinking and adjust accordingly.

Of course, this isn't a change that can happen overnight, but rather one that happens gradually with experience.

Last but not least, you'll need to learn a programming language or be AI software.

Lucky for you, programming languages such as Python and our aren't that hard to learn.

And once you're fluent in one programming language, you can easily master another one despite coming from an economics background.

This also falls into the learn as we go area.

So just make sure to be proficient in at least one of either python or are, and your transition into the field should be smooth as butter.

All right, In this video, we discussed that economics 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 economic skills are mandatory for any data science team.

Thus there is no doubt that you dearie Con major could be that person.

If you like this video, don't forget to hit the like or share button.

And if you'd like to become an expert in all things data science subscribe to our channel for more videos like this one.