What sort of data is analyzed at a Data Scientist’s day job? What are the academic qualifications of a Data Scientist? Is it any different from a Data Analyzer? What is it that they exactly do? All your questions shall be answered in this article.
If you’re looking at job title that reads “Data Scientist,” let us bring you up to speed as to what does this job really include and what skills are needed to become a data scientist. A data scientist is a qualified person with the multiskilling set of analytical, programming and statistical methods. A data scientist’s skills help him or her get solutions strategically for business facing challenges.
What is Data Science? Understanding the Concept…
The pillars of data science are algorithm development, data inference, and technology. As mystifying as it sounds, at the core of the whole process is data. Data science is used to decode data and find ways to add value to the business. A company’s previous numbers are crunched, the industry’s ongoing values are estimated, and the marketing strategies are looked into. After running quantitative data analysis and algorithm solutions on production and operational values, we get substantial business solutions.
Data science exposes a company’s hidden risk factors, predicts the future sales and company value based on current business pattern and values. It is not rocket science; it is a methodological process. For instance, Procter and Gamble’s data analysts use time series models to evaluate future product demands. This helps in understanding consumer behavior along with the pros and cons of increasing production scale.
“Discovery of data insight” is about diving into the numerical level to decipher trends, patterns, customer behavior, and complex inferences. It is about discovering the unknown insights to help companies make more informed decisions than ever. Another utilization of data science that piques your interest is that Netflix uses movie viewing pattern to understand what their viewers dig. Based on the result of this factor, Netflix decides, which kind of series or movies to produce or as we call it “Netflix Originals.”
Data insights or “data warehouses” help analysts consult while strategizing business models. Data scientists become analysts and provide companies with strategic guidance on how to enforce the findings in the current business scenario. It’ll interest you to the knowledge about the analysis method of the famous departmental store Target. The consultants aka data scientists at Target focus on customer behavior and various customer divisions within its operational base. Based on the findings of this analysis, they market diversely for distinct market audiences.
Data Science is a consolidated term for the operation of mathematical expertise, technological know-how, and acumen for business. At the core of it is the art of viewing all data from a numerical or quantitative eye. What us ordinary beings wouldn’t realize that at the bottom of all that data lay a solution to our persistent problems that require grinding with analytical models. You go in with hardcore math and come out glorious with the underlying mechanics of it all.
Fun fact: A lot of the datasets can be operated with a matrix rather than your classical statistical formulae. In other words, you have to have a deeper understanding of math to be a successful data scientist.
It is essential for a company to store their data aka data warehousing. This data enables companies such as Amazon to recommend other similar products for you to choose from. Netflix uses the same method to recommend a list of movies to the viewer based on previous selections. Spottily recommends you a list of songs based on the genre of music you most commonly listen to.
Who is a Data Scientist?
The person carrying out the algorithms, statistics and does the magic on the data input is a Data Scientist. The end product of his job description is to help a company make informed decisions based on the findings of the detailed analysis. A data scientist’s skills invariably include wrangling the provided data and pave out factional ways to grow a business.
Data scientists usually are people with a bachelor degree in mathematics, computer science, economics or statistics. Yes, statistics is a huge part of their profile. But we, non-specialists assume that classic statistics are the driving force here. It is a combination of inferential techniques, logic, business strategy, and algorithm machine learning that helps them derive thorough results.
These magicians run through large datasets and come out with essential insights. On numerous occasions, this includes meshing together fields of data that don’t usually formulate together to find effective results. A data scientist knows how to deduce value from data input using algorithms. They apply applicable treatments, conditions, and filters to the data variables to protect the business from all sorts of possible risks.
What does the Job Description of a Data Scientist Entail?
A data scientist is the means to finding business solutions via math and computer science. They gather data input, analyze trends, crunch numbers, take into consideration the current industry prices and make evaluations. These evaluations are nothing but the key to a successful business run based on previous trends and numbers.
The person applying to this job should be avid at technical qualifications such as coding languages, statistical and algorithm machine learning, database warehousing and reporting technologies. The job is to enlighten and inform the production, sales, strategy and management teams about the findings and work accordingly.
They’re also expected to be adept at providing tips for better working and productivity thus enabling the business to grow. Testing different solutions as tentative courses of action and choosing the best one is a major role of a data scientist.
Data scientists are expected to work alongside company stakeholders to use data inputs as leverage for better-driven business opportunities. Mining data and computing numbers to help predict methods for sales optimization, product development, marketing strategies, and business techniques is another one on the task list of a data scientist.
They innovate new ways to analyze datasets as and when felt necessary and applicable to evaluate most effective and detailed output. These newly developed models are then applied to future data sets to gain insight on possible loopholes or business opportunities.
If the new data gathering model is effective or not, then needs to be found out by the analyst too. Taking up this newly drafted model across all functions of the company and implement a suitable course of action for each department across the organization.
Having said this, an analyst has to be there to monitor the outcomes of this novel model and its effect on the company and its working. Using predictive models before implementing changes helps minimize errors.
Constant development of newer and more efficient models and data gathering techniques takes up the lion’s share of a data scientist’s job description. The problem solver aka the data scientist commonly is known to have flair at computer languages such as SQL, Python and R. This assists in filtering through a large number of data sets and minimizing unnecessary data inputs.
A data scientist is expected to who find value in piles of data. This magician proactively gathers information from across the organization and through the years. He, then, analyzes the gathered data for a proven method on how the company performs and builds tools, models, and databases. This enables in an automated process within the company.
Since it is a job that would require on to coordinate between cross-functional departments, a data scientist’s qualifications aren’t restricted to great mathematics, algorithms and machine learning. A decent oratory skill, strong verbal and written communication along with a mind for spotting real-time drawbacks and opportunities are the basic needs for this job.
Regression, simulation, decision tree learning, clustering, model development, scenario analysis, and neural networks are their work buddies. These terms denote common methodologies to derive the value of extensively large data. A data scientist, with time and experience, develops a knack for business development strategies. With ease, he’s able to tell a faulty decision to a good one regarding the future of the business.
Data science is a comparatively new and upcoming area that scholar institutions have not had the time to develop a new consolidated academic curriculum dedicated to data scientists. No one yet, technically, holds a degree on “Data Science.” Then where does this level of vocational training come from? The unmatched intellect and curiosity of analysts drive them to be inspired autodidacts, pushed to learn the right skills on their own, guided by their fueled determination.
Hiring personnel who carry this creative mix of distinct talents is easier said than done. The reason data scientists are paid the way they are is due to the unfulfilled demand of their breed. So, when you one does manage to hire a data scientist successfully, they must be nurtured and engaged.
Trusting them with the autonomy to architecture in their way through problems is the way to do it. This boosts them in the company to be extremely driven problem solvers at your disposal to steer your way out of difficult analytical problems.