Are You a Data Scientist or an IT Job?

When I was in college, I knew that I wanted to be a data scientist. The idea of using my skills to help people understand the world around them fascinated me. And yet, when it came time for me to look for a job after graduation, there were few openings in this field. As more and more companies began investing in big data analytics, however, the demand for graduates who could help them analyze their massive collections of information skyrocketed—and so did salaries for those same jobs!

Here are the top six reasons why data science jobs are not the same as IT.

Here are the top six reasons why data science jobs are not the same as IT.

• Data scientists need to be able to tell a story with their data, not just process it and put it in an Excel spreadsheet. Data is a living thing that changes over time and across different industries, so a good data scientist must be able to analyze, interpret and visualize it in order to understand what’s going on within their organization or industry at large—and then use this information for strategic purposes (i.e., making decisions). This requires creativity; if you’re someone who needs everything spelled out for them before they start working on something new, then this probably isn’t your strength!
• Data scientists work across different departments within an organization—not just IT—so they need strong interpersonal skills as well as technical knowledge when interacting with other people within the company or industry they serve.”

Data scientists can analyze and interpret data in any industry

Data scientists can work in any industry and analyze any kind of data. They can also work with any kind of source, including web APIs and text files. A data scientist might be asked to interpret measurements from a sensor on an industrial robot or analyze customer purchasing habits by looking at purchase history on an e-commerce platform.

Data scientists are experts at interpreting the meaning behind numerical values, but they also need to understand how these values relate to each other—and why certain relationships exist between them. For example: If you have 100 restaurants within a mile radius of your office but only 50% of those restaurants offer delivery service (and perhaps another 25% provide takeout options), then it’s likely that many customers will choose the closest option based solely on convenience rather than price point because it would cost less money overall for most households if both types were available together instead! This kind of analysis requires knowledge about probability distributions, distributions over time periods such as “days,” etc., which makes sense since many businesses rely heavily upon algorithms as well as computers themselves nowadays; after all, we wouldn’t want them malfunctioning during peak hours due poor programming practices!

Data scientists work across different industries and departments

Data scientists work across different industries and departments. They work with many different teams, both internal and external to the organization. They need to be able to communicate effectively with these teams, which means they need strong communication skills and an ability to understand what each person needs from them.

Data scientists also need access to data from different sources—including structured data (like Excel files), unstructured text documents like emails or chat logs, video content (like YouTube videos), social media posts (like Facebook comments), etc.—to create analyses that are accurate and reliable for their clients’ needs.

IT experts specialize in technology, but not in analysis

IT experts are trained to solve problems with technology. Data scientists, on the other hand, are trained to solve problems with data. While both IT and data science require an in-depth understanding of computer science and statistics, each has its own unique focus on solving problems through the use of technology.

Data scientists can be found in many industries—including healthcare, finance, and manufacturing—and they often have a background that includes at least one area outside of IT. Many successful companies have been able to attract top talent by offering flexible work schedules or opportunities for advancement within their organizations (e.g., moving from a junior analyst position straight into a leadership role).

Data scientists need to be able to tell a story with their data

You’re a data scientist if you can tell a story with your data.

Data scientists need to be able to present their findings in a way that is understandable to non-specialists. They also need the ability to communicate with different audiences, including businesspeople, managers, and decision-makers.

Data science is much more than an IT job.

You may be wondering: “What is data science?” Data science is a broad field that covers many aspects of data analysis. It’s not just an IT job, so if you’re looking for a career in data analysis and don’t have any experience in IT, then it might be time to reconsider your options.

Data scientists are not just IT people or coders who happen to be interested in numbers (though they sometimes do code). A good example would be someone like Drew Conway who works as part of Google’s Machine Learning team but also runs his own startup called Databricks; he has both computer science and business degrees from Harvard University—and still manages to work on projects related to both fields simultaneously!

Conclusion

Data science is a unique and exciting field that requires skills beyond what IT experts can offer. If you’re looking to make a career move into this field, it’s important to know the differences between data science and IT jobs so that you can find the right fit for your interests and experience level.

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