Data science is the study of where information comes from, what it represents, and how it can become a valuable resource for creating business and IT strategies. Harvesting large amounts of structured and unstructured data to identify trends can help control costs for a company, increase efficiency, find new market prospects, and increase the company’s strategic advantage.
Here we present the top 10 data science developers in Canada.
The main advantage of having data science in an organization is the empowerment and facilitation of decision-making. Organizations with data scientists can consider quantifiable evidence based on data in their business decisions. These data-based decisions can ultimately lead to greater profitability and greater operational efficiency, business performance, and workflows. In customer-oriented organizations, data science helps identify and refine target audiences.
Streaming services such as Netflix use data mining to determine what their users are interested in, and use that data to determine which television programs or movies to produce. Data-based algorithms are also used in Netflix to create personalized recommendations based on a user’s viewing history. Shipping companies like DHL, FedEx, and UPS use data science to find the best routes and delivery schedules,
Data science remains an emerging field within the company because the identification and analysis of large amounts of unstructured data can be too complex, expensive, and time-consuming for businesses.
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Differences between data science and big data
Big data is very fashionable. With digitalization still in process, it is, in fact, more fashionable than current. Big data, data mining and data science are often confused, although they are different and complementary techniques.
The big data is the collection process large volumes of data, its storage, and real-time analysis in search of patterns. They are usually structured data of which we know the format. We can compare it to an open oil well.
Data mining includes a series of technical-oriented analysis. It helps us to understand the contents of a database, filter it, debug it, and eliminate what it doesn’t provide. It would be the equivalent of natural gas extraction separating it from crude oil.
Data science is the last link when converting data into information. It uses large quantities and several databases, some derived from data mining processes. In our metaphor, data science is a state-of-the-art refinery.
It happens that the raw material of data science is not just structured databases. It also works with incomplete and messy data, and sometimes combines hundreds of sources. Besides, its objective is not only to extract information but to show it understandable to the user.
Does my company need a data scientist?
In a world of continuous acceleration, having information is an economic resource that should be valued. The figure of the data scientist is usually associated with large companies, but it is SMEs and freelancers who, using their flexibility, can take more advantage of them. In comparison, large companies are slow and have more complicated uses.
But it happens that it is precisely those SMEs that cannot afford to hire a data scientist for their brand. This is where the consultants and their reports come in. In this space, we have highlighted several reports on it: one of the franchises worldwide and another of the five areas benefited by the IoT.
Thanks to these reports, entrepreneurs can catch up on trends in different sectors. The data has already been collected, filtered, and analyzed by others, and appears in simple self-explanatory graphs. Also, it is usually free content. However, it is shallow, with more survey findings that date science as such.
If we look for last-minute information on our business model, sector, or microniche, we will have to resort to specialized magazines. These are usually expensive, but they bring a lot of light on the future of our profession.
In the case of large companies, there is no excuse for leaving data science behind. STEAM professions and in particular the specializations of data scientists and RPA responsible are necessary.
What does the data scientist provide for a brand?
If there is an area in which this role becomes especially relevant, it is marketing segmentation. Not all clients seek the same approach, nor do all sectors use the same communication channel. My clients, what interests them most? An email, an active social network, a good LinkedIn channel?
Data analysis, both in real-time and in anticipation of trends, can provide us with really useful information. A simple example is given by Twitter hashtags. Knowing the keywords hour by hour helps us place ourselves on the crest of the wave of information. But taking the pulse of the market is not as simple as using the keywords in a list.
Another space in which data science is key is the design of product and service strategies. In the classic example of designing an application, do I make it for payment, make it free, and make it freemium (free with premium paid content)? Which of these options is best accepted? Which one do I have more opportunities for success with?
The data scientist can locate the necessary sources for an analysis of this type and other complementary ones. Following the last example, a good way to ‘build a well’ would be to store all comments on competing applications in a single database. This is big data.
Like data mining, we could separate comments that do not contain keywords such as “euro”, “currency”, “payment”, “free”… Now we have a set of useful comments related to our theme. We have got our raw material.
As the last step (here the magic happens), we can program different counters that analyze positive and negative words from the comments. In this way, we can analyze if there is a reluctance to pay or if users are satisfied. This is not easy, it is data science and requires high training.
