In a world awash with oceans of data, comprehensive data collection is undoubtedly crucial to any organization’s commercial viability. Yet, it’s what happens with the data that’s perhaps the most vital element.
What do you do with it once you’ve collected it? What commercial, political, economic or medical, etc., meanings are ciphered within that mass of numbers? How can you turn those deciphered meanings into productive actions?
It’s one purpose of an advanced data science degree to get to the bottom of questions like that.
Here’s a deep dive into why such degree specialism is so valuable in today’s data-saturated world.
Why study data science?
Data analytics vs data science
The “understanding” and the “action” referred to above roughly corresponds to the distinction between data analytics and data science.
Data analytics has become a necessity in the age of data overload: the sheer quantity of digital data generated every second for every person on the planet has been estimated to be 1.7 megabytes. Put bluntly, amounts like that – we’re in the age of so-called Big Data – overwhelm merely human powers of computation.
To understand what’s happening right now, you need a grasp of the fundamentals of how to organize and find meaning in the mountain of data that’s in front of you. That’s essentially data analytics.
But to understand why the data is taking shape the way it is, why trends are beginnings to gather momentum, and how all that’s likely to influence the future, you need knowledge of data science.
Just what is data science?
In a single sentence, data science goes beyond “what is” to what you can do about it, how you can extract benefits from it. Let’s elaborate on that.
As noted above, the commercial world is now awash with colossal volumes of data. But to extract advantages from that raw data, someone has to make sense of it, have the requisite vocabulary to navigate through it, and the know-how to write value-extracting algorithms capable of processing expansive seas of raw information swiftly. Plus, the ability to interpret the results for data-rooted decision-making options.
To arrive at intelligible meanings from raw data, data scientists must first know how to clean it (to eliminate data “noise” of zero relevance and value), structure it into organized datasets, and pull out the meanings hidden within them.
Qualified data scientists have the following core proficiencies to bring to the table:
- They craft hypotheses for testing against the data
- They devise and conduct experiments to collect relevant data
- They check the quality of the data collected
- They clean and streamline datasets
- They organize and structure such worked-up data for further analysis
Collection and analysis of Big Data require competency in coding languages such as SQL and R because the algorithms or “recipes” for analysis and testing are composed of them. Well-crafted algorithms can catch trends and signals that human minds would simply miss.
Here’s an example: data scientists at the Massachusetts Institute of Technology devised an algorithm with an astonishing medical application. It was designed to pick up differences between three-dimensional medical images such as MRI scans. When put to the test on actual clinical results, it did so over a thousand times faster than human clinicians could. This assists clinicians rather than replacing them because it allows them to respond to patients’ problems quickly and save more lives.
Harvard academic Dustin Tingley, Faculty Director for the prestigious university’s Initiative on Learning and teaching, emphasizes the deep interconnections between the human and the “machine (computer) dimensions of data science, stating:
“With this new world of possibility, there also comes a greater need for critical thinking.
“Without human thought and guidance throughout the entire process, none of these seemingly fantastical machine-learning applications would be possible.”
More real world-examples of Data Science’s contribution to businesses
Clear insights into customer preferences
Data about a business’s customers can yield valuable information about their habits, demographic membership, tastes, activities, ambitions, and many other characteristics. Data science can play a critical role in bringing these data sets to the fore and making sense of them for decision-making and marketing purposes.
“Data wrangling” refers to the methods data scientists use to find the signature connections about customers within the mass of data – how often they visit a business’s website or physical store, the occasions when they add an item to their cart, make a purchase, open an email or share a post on social media. Linkages can then be established (e.g., linking a customer’s email address with their credit card information, noting their social media handles and what they seem to like buying). When such data is aggregated, it becomes possible to discern their behavioral trends – all crucial for the timing, nature personalized relevance of the marketing aimed at them.
Making manufacturing “leaner”
Data science can be used to root out inefficiencies in manufacturing: put simply, eliminating waste, whether of materials, by-products or time, boosts productivity and improves the “bottom line.” Machines used in manufacturing collect masses of data during operation, often far too much for even the most mathematically gifted human minds to analyze manually.
But data scientists can craft an algorithm capable of sorting the valuable and informative “data wheat” from the noisy chaff in this data (a process known as “data cleaning”). The algorithm will then sort it into distinct data sets before interpreting it swiftly and precisely to unearth valuable insights into where waste can be stripped out from the process.
Forecasting market trends
Research giant Nielsen recently found that 81% of consumers strongly want companies to protect the environment. Leading clothing retailer Patagonia used data science to embrace this growing trend vigorously and launched its “Worn Wear” site, dedicated to helping its customers recycle its used items. Data science allows firms to make decisions that keep them well ahead of the trend curve of customer preferences.
How to become a data scientist
For the mass of people considering a career upgrade in adulthood, you don’t have to attend a university campus for years to obtain an advanced data science degree. More universities are offering people with family and financial obligations the option to study for prized qualifications online.
Highly rated universities like Kettering, for example, offer a fully online data science master’s degree. Suitable especially for candidates with backgrounds in science, Data Analytics, Data Science, Mathematics, Physics, Engineering, or Computer Science, it’s also open to candidates who come from different backgrounds (provided they have strong statistical and mathematical skills).
And the beauty is that this advanced data science degree can be completed within 24 months.
