Data science refers to the activity of analyzing a large amount of data in order to extract knowledge and insight leading to actionable decisions.
Now you might ask what kind of knowledge, insight, and actionable decision are we talking about?
To orient the conversation, let's reduce the scope to three fields of data science:
• Descriptive analytics: Data science is associated with information retrieval and data collection techniques with the goal of reconstituting past events to identify patterns and find insights that help understand what happened and what caused it to happen. An example of this is looking at sales figures and demographics by region to categorize customer preferences. This part requires being familiar with statistics and data visualization techniques.
• Predictive analytics: Data science is a way to predict the likelihood that some events are currently happening or will happen in the future. In this scenario, the data scientist looks at past data to find explanatory variables and build statistical models that can be applied to other data points for which we're trying to predict the outcome, for example, predicting the likelihood that a credit card transaction is fraudulent in real-time. This part is usually associated with the field of machine learning.
• Prescriptive analytics: In this scenario, data science is seen as a way to make better decisions, or perhaps I should say data-driven decisions. The idea is to look at multiple options and using simulation techniques, quantify, and maximize the outcome, for example, optimizing the supply chain by looking at minimizing operating costs.
In essence, descriptive data science answers the question of what (does the data tells me), predictive data science answers the question of why (is the data behaving a certain way), and prescriptive data science answers the questions of how (do we optimize the data toward a specific goal).
Now another question which usually comes to our mind is whether data science is here to stay?
In the last decade, we've seen exponential growth in data science interest both in academia and in the industry, to the point it became clear that this model would not be sustainable. As data analytics are playing a bigger and bigger role in a company's operational processes, the developer's role was expanded to get closer to the algorithms and build the infrastructure that would run them in production. Another piece of evidence that data science has become the new gold rush is the extraordinary growth of data scientist jobs, which have been ranked number one for 2 years in a row on Glassdoor (https://www.prnewswire.com/news-releases/glassdoor-revealsthe-50-best-jobs-in-america-for-2017-300395188.html) and are consistently posted the most by employers on Indeed.
Headhunters are also on the prowl on LinkedIn and other social media platforms, sending tons of recruiting messages to whoever has a profile showing any data science skills. One of the main reasons behind all the investment being made into these new technologies is the hope that it will yield major improvements and greater efficiencies in the business. However, even though it is a growing field, data science in the enterprise today is still confined to experimentation instead of being a core activity as one would expect given all the hype. This has lead a lot of people to wonder if data science is a passing fad that will eventually subside and yet another technology bubble that will eventually pop, leaving a lot of people behind.
These are all good points, but people quickly realized that it was more than just a passing fad; more and more of the projects they were leading included the integration of data analytics into the core product features. Finally, it is when the IBM Watson Question Answering system won at a game of Jeopardy! against two experienced champions, that people became convinced that data science, along with the cloud, big data, and Artificial Intelligence (AI), was here to stay and would eventually change the way we think about computer science.
Now you might ask what kind of knowledge, insight, and actionable decision are we talking about?
To orient the conversation, let's reduce the scope to three fields of data science:
• Descriptive analytics: Data science is associated with information retrieval and data collection techniques with the goal of reconstituting past events to identify patterns and find insights that help understand what happened and what caused it to happen. An example of this is looking at sales figures and demographics by region to categorize customer preferences. This part requires being familiar with statistics and data visualization techniques.
• Predictive analytics: Data science is a way to predict the likelihood that some events are currently happening or will happen in the future. In this scenario, the data scientist looks at past data to find explanatory variables and build statistical models that can be applied to other data points for which we're trying to predict the outcome, for example, predicting the likelihood that a credit card transaction is fraudulent in real-time. This part is usually associated with the field of machine learning.
• Prescriptive analytics: In this scenario, data science is seen as a way to make better decisions, or perhaps I should say data-driven decisions. The idea is to look at multiple options and using simulation techniques, quantify, and maximize the outcome, for example, optimizing the supply chain by looking at minimizing operating costs.
In essence, descriptive data science answers the question of what (does the data tells me), predictive data science answers the question of why (is the data behaving a certain way), and prescriptive data science answers the questions of how (do we optimize the data toward a specific goal).
Now another question which usually comes to our mind is whether data science is here to stay?
In the last decade, we've seen exponential growth in data science interest both in academia and in the industry, to the point it became clear that this model would not be sustainable. As data analytics are playing a bigger and bigger role in a company's operational processes, the developer's role was expanded to get closer to the algorithms and build the infrastructure that would run them in production. Another piece of evidence that data science has become the new gold rush is the extraordinary growth of data scientist jobs, which have been ranked number one for 2 years in a row on Glassdoor (https://www.prnewswire.com/news-releases/glassdoor-revealsthe-50-best-jobs-in-america-for-2017-300395188.html) and are consistently posted the most by employers on Indeed.
Headhunters are also on the prowl on LinkedIn and other social media platforms, sending tons of recruiting messages to whoever has a profile showing any data science skills. One of the main reasons behind all the investment being made into these new technologies is the hope that it will yield major improvements and greater efficiencies in the business. However, even though it is a growing field, data science in the enterprise today is still confined to experimentation instead of being a core activity as one would expect given all the hype. This has lead a lot of people to wonder if data science is a passing fad that will eventually subside and yet another technology bubble that will eventually pop, leaving a lot of people behind.
These are all good points, but people quickly realized that it was more than just a passing fad; more and more of the projects they were leading included the integration of data analytics into the core product features. Finally, it is when the IBM Watson Question Answering system won at a game of Jeopardy! against two experienced champions, that people became convinced that data science, along with the cloud, big data, and Artificial Intelligence (AI), was here to stay and would eventually change the way we think about computer science.
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