Which IT job role is right (or wrong) for you? (free PDF)

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Provided by: TechRepublic
Topic: Tech & Work
Format: PDF
Unless you're very lucky, you probably won't find your ideal position in the IT world the first time out. In fact, you may not even know what your ideal position looks like until you've been laboring in the tech trenches long enough to get a sense of the industry and your own working style, skills, and interests.

So if you're stuck in a job that's not quite right, or you're gearing up to take the next step up the ladder, or you're trying to decide whether to jump headlong in a new direction altogether, take a few minutes to read through the lists in this ebook. Written by IT pros who've seen both successes and failures in each of these job roles, the items listed here will help you rule out career strategies that aren't a good match for you—and help you zero in on the niche that's exactly what you need.

From the ebook:

10 signs you may not be cut out for a data scientist job
The data scientist role has been attracting quite a bit of attention and interest—but if you’re considering a job in that field, be sure you know what you’re taking on.

College students are enrolling in data science majors, and some are even changing their majors to data science because it’s a growing and lucrative field. But data scientists are also a special breed. How do you know if you’ll be happy in a data science career? These 10 clues may help you determine whether a data scientist role is right for you. If you decide yes, be sure to check out the career resources at the end of this article.

1. You don’t like statistics
Data science is packed with statistical analysis. If statistics don’t appeal to you, data science isn’t a good fit.

2. You don’t like to work for long periods of time alone
Data science can require extended periods of concentration. This work is best done alone and with minimal interruption. If you are a highly social person and desire constant interaction, a data science career might be too isolating.

3. You’re not detail oriented
Attention to detail is important in data science, because you’re working constantly with statistics, data, and the mathematical and logical algorithms you develop. Along with this work, you must be continuously analyzing outcomes of your work. An individual with a detail-oriented mind is the best fit for this activity.

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