Why is it so hard to become a data scientist?

Because of the often technical requirements for Data Science jobs, it can be more challenging to learn than other fields in technology. Getting a firm handle on such a wide variety of languages and applications does present a rather steep learning curve.

What are the downsides of being a data scientist?

b. Disadvantages of Data Science

• Data Science is Blurry Term. Data Science is a very general term and does not have a definite definition.
• Mastering Data Science is near to impossible.
• Large Amount of Domain Knowledge Required.
• Arbitrary Data May Yield Unexpected Results.
• Problem of Data Privacy.

Can statisticians be data scientist?

Like data science, statistics have a broad range of applications. Also, like data scientists, statisticians collect information and use it to perform analyses. Their focus is on analyzing data to provide answers and insights that can inform decision-making.

Are statisticians and data scientists the same?

In summary, statisticians focus more on modeling and usually bring data to models, while data scientists focus more on data and usually bring models to data.

Is being a data scientist a necessary part of the job?

As frustrating as it can feel, it was a necessary part of the job. Following on from doing anything to please the right people, those very same people with all of the clout often don’t understand what is meant by “data scientist”.

Is data science the Sexiest Job of the 21st century?

Yes, I am a data scientist and yes, you did read the title correctly, but someone had to say it. We read so many stories about data science being the sexiest job of the 21st century and the attractive sums of money that you can make as a data scientist that it can seem like the absolute dream job.

Why do companies hire people with no data strategy?

It reeks of a job spec from a company that has no idea what their data strategy is and they’ll hire anyone because they think that hiring any data person will fix all of their data problems). But it doesn’t stop there.

Does your company need a data science team?

Despite this, many companies still have data science teams that come up with their own projects and write code to try and solve a problem. In some cases this can suffice. For example, if all that’s needed is a static spreadsheet that is produced once a quarter then it can provide some value.