By: Michael Korovkinaaaa.png

Data science isn’t a new concept — for years, corporations have utilized data science teams, consisting of statisticians and computer scientists, to provide consistent solutions to constantly evolving problems such as market trends, customer profiling, and predictive business analytics. However, in the past few years, the demand for data scientists has been growing significantly.

Corporations today collect more information than ever before — they practically run on customer data. Take, for example, Google, which tracks your online interactions, searches, and times of usage to improve their search result curation algorithms. Even non-tech-based businesses, such as Kroger, are beginning to favor data science approaches over traditional statistics due to the increased robustness and accuracy of data science’s core methods.

What is Data Science?

Data science is a branch of computer science and mathematics which revolves around the aggregation, simplification, and model-building from enormous amounts of noisy data. A data scientist, therefore, specializes in building computational and mathematical models from such data in order to predict future data collected.

Today’s work with big data warrants the use of deeper mathematical techniques called “machine learning,” which data scientists specialize in.

Machine learning is the ability of computer systems to “learn” numerical trends based on user-provided data. They “learn” trends through complex mathematical formulas which they construct through repeated trial-and-error analysis of given data, ultimately providing the user with a model capable of representing trends in the data. These models, then, take variable inputs such as the ones used to train them, and provide back a scaled output.

One of the most common algorithms used in machine learning is called “gradient boosting,” which employs multivariate derivatives called gradients to optimize the model’s accuracy in regression or classification problems.

So what do business use data scientists for other than machine learning?

  • Identifying and refining target audiences
  • Decision-making with quantitative evidence​
  • High level statistical and mathematical analysis
  • Low-risk validation of statistical inferences

Why Should I Care?

​Decades ago, one might have grouped business analysts with data scientists and vice versa. But now, with the spotlight constantly being trained on Big Data throughout the past few years, more businesses than ever are turning to the aid of data scientists to help make sense of their vast amounts of collected data. Whereas the label “proficient in Excel” was highly sought-after a few years ago, today’s business world is turning to mathematics- and computer science-heavy analytics through machine learning and other code-oriented predictive analysis.

While it’s unlikely that data scientists will ever completely take over higher level analytics positions such as those of statisticians and other analysts, it is fair to admit that they will certainly overtake a significant fraction of higher level positions due to their higher versatility and solution-related robustness.

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