What is a Data Scientist? What Do They Do?
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A professional who is tasked with the responsibility of gathering, analysing, and interpreting exceptionally vast amounts of data is known as a data scientist. The function of the data scientist is an offspring of various traditional technical roles, such as that of the mathematician, the scientist, the statistician, and the computer professional. The employment of sophisticated analytics technologies, such as machine learning and predictive modelling, is a prerequisite for consideration for this position.
In order to establish hypotheses, draw conclusions, and examine patterns in customers’ behaviours and market trends, a data scientist needs access to vast amounts of data. The primary responsibilities include the collection and analysis of data, as well as the utilisation of a wide variety of reporting and analytics tools to identify patterns, trends, and linkages within data sets.
Data scientists generally work together in groups in the business world to mine large amounts of data for information that may be used to the prediction of customer behaviour and discover new revenue prospects. In many companies, data scientists are also in charge of establishing best practices for the collection of data, the utilisation of analysis tools, and the interpretation of data.
Because businesses are increasingly interested in extracting useful information from big data — that is, the massive amounts of structured, unstructured, and semi-structured data that a large enterprise or the internet of things generates and collects — there has been a significant increase in the demand for skills in the field of data science in recent years.
Why is data science important?
Data science is a highly interdisciplinary profession that encompasses a wide range of information and is typically more concerned with the big picture than other analytical fields are. Data science’s purpose in the world of business is to gather information on customers and marketing efforts so that businesses can devise effective strategies to attract the attention of their target demographic and move more goods.
Data scientists are required to rely on imaginative insights when working with big data, which refers to the vast volumes of information that have been gathered via a variety of gathering procedures, such as data mining.
Big data analytics can, on an even more fundamental level, assist brands in better understanding their customers, who, in the end, are a significant factor in determining the long-term success of a business or endeavour. In addition to directing marketing efforts toward the appropriate demographic, data science can also assist businesses in taking command of the narratives surrounding their brands.
Since the subject of big data is one that is expanding at a rapid rate, there are always new tools being released, and those tools require specialists who can quickly learn the uses of those tools. Data scientists may assist businesses in developing a strategy to achieve their objectives that are grounded in empirical evidence rather than just gut feeling alone.
Data science plays a very essential part in the prevention of fraud and the discovery of security breaches because it is possible to sift through huge volumes of information to look for seemingly insignificant data anomalies that may point to flaws in the protection mechanisms of an organisation.
The field of data science serves as the engine behind the highly personalized user experiences that are produced by means of personalization and customisation. Customers can be made to feel like they are seen and understood by a firm through the use of this analysis.
Roles and responsibilities
The concepts of science, mathematics, statistics, chemometrics, and computer science all contributed to the development of the idea of a data scientist. These are some of the most important main technological fields of the contemporary era. Due to the rarity of the combination of personality traits, experience, and analytical skills required for this profession, the demand for qualified data scientists is on the rise.
Glassdoor’s annual ranking of the “50 Best Jobs in America” places data scientists at the top of the list each year for 2016, 2017, 2018, and 2019. The rankings are based on factors such as work satisfaction, the number of job opportunities, and the median base salary. A job opening for a data scientist can alternatively be listed as an opening for a machine learning architect.
Analyzing huge data sets consisting of both quantitative and qualitative information is a fundamental responsibility. These experts are responsible for the process of constructing statistical learning models for data analysis and are required to have previous experience with statistical tools. In addition to this, they need to have the expertise necessary to develop intricate prediction models.
Computer scientists, database and software programmers, subject matter experts, curators, expert annotators, and librarians are some examples of professionals who may work in data science or go on to pursue a career in data science full-time. Other potential data scientists include those who work in curating or archiving information, as well as librarians. In certain cases, job advertisements for data scientists will also use the terms “machine learning architect” or “data strategy architect” to describe the position.
In order to be successful in this position, you will need to demonstrate creativity, along with other soft qualities such as intellectual curiosity, scepticism, and intuition. Because the post requires continuous collaboration with a large number of different teams, possessing strong interpersonal skills is essential to success in the position. A significant number of businesses anticipate that their data scientists will be skilled storytellers who are able to communicate data findings to individuals working at all levels of a company. In addition, leadership abilities are required for them to successfully direct the processes of decision-making in a company that are driven by data. Handling the enormous amounts of data that are necessary for predictive analytics calls on a number of critical skills, including leadership, business knowledge, and the ability to anticipate dangers.
Qualifications and required skills
In general, data scientists need to have sufficient educational or experiential background to be able to successfully accomplish a wide variety of exceedingly difficult planning and analytical activities in real time. The majority of jobs in the field of data science require at the very least a bachelor’s degree in a relevant technical discipline. However, certain jobs may have more specialised requirements than others.
Data science necessitates familiarity with a variety of big data platforms and technologies, such as Hadoop, Pig, Hive, Spark, and MapReduce; as well as a variety of programming languages, such as SQL, Python, Scala, and Perl; and statistical computing languages, such as R.
Hard skills like as data mining, machine learning, deep learning, and the ability to integrate organised and unstructured data are necessary for the position. A significant portion of the tasks require previous experience with various statistical research methods, including modelling, clustering, data visualisation and segmentation, as well as predictive analysis.
The following are examples of talents that are usually listed as required in job advertisements:
competence in all phases of data science, from initial discovery to cleaning, model selection, validation, and deployment; familiarity of common data warehouse formats and an understanding of how they work.
familiarity with the application of statistical methods to the resolution of analytical issues;
a strong understanding of the most popular machine learning frameworks; previous experience working with public cloud platforms and services;
understanding of a wide range of data sources, such as databases, public or private APIs, and standard data formats, such as JSON, YAML, and XML;
capability of identifying new opportunities to apply machine learning to corporate operations in order to increase such processes’ effectiveness and efficiency;
the capacity to build and implement reporting dashboards that are capable of tracking critical company KPIs and providing actionable insights;
expertise in methods for qualitative and quantitative analysis, as well as the capacity to communicate findings from both types of analysis in a manner that is easily digestible to the target audience.
the ability to design and conduct validation tests; a knowledge of machine learning techniques such as K-nearest neighbours, Naive Bayes, random forests, and support vector machines;
a graduate degree, preferably with a concentration in statistics, computer science, data science, economics, mathematics, operations research, or similar quantitative subject;
experience with data visualisation tools such as Tableau and Power BI; coding skills such as R, Python, or Scala; the ability to aggregate data from a variety of sources; the ability to conduct ad hoc analysis and present the results in a manner that is understandable to the audience.
Education, training and certifications
Obtaining a graduate degree in a relevant field, such as statistics, data science, computer science, or mathematics, is often required for a career as a data scientist. This position is eligible for a variety of certifications, such as the Certified Analytics Professional credential, the Dell EMC DECA-DS certification, the MCSA: Various SQL/Data Engineering Options certification, and the Microsoft MCSE Data Management and Analytics certification.
Data scientist salary
The wage of an abstract worker on a vast scale should be more than double that of a data analyst because of the added obligations and expectations that come along with the job. As of October 2019, the average income for a data scientist in the United States was 117 345 dollars, as reported by Glassdoor.
Data scientists vs. citizen data scientists
The following are some of the distinctions that may be drawn between data scientists and citizen data scientists:
- Education. A bachelor’s degree in mathematics, data analytics, computer science, or statistics is typically required for entry-level work in the field of data science. On the other hand, citizen data scientists can come from a wide variety of educational backgrounds. However, they typically have experience working with a variety of analytical tools and software, which enables them to create models and carry out complex analyses despite the fact that they do not have a formal education in the fields mentioned previously.
- Code. In order to carry out conventional studies, citizen data scientists typically rely on software packages that provide prebuilt modelling tools, drag-and-drop features, and user-friendly algorithms. A citizen data scientist using these tools will not be prevented from uncovering significant patterns or data points. Data scientists who work professionally are capable of developing intricate algorithms tailored to their specific needs and of approaching data analysis in novel and original ways.
- Salary. Data scientist is one of the job titles that offer the best salaries, and there is a great need for qualified experts who are able to fulfil the many tasks that come with the role. On the other side, citizen data scientists could be people who are just interested in the field as a hobby or people who are volunteering their time, or they could be people who are receiving some form of payment for the work that they perform for huge corporations.
What are the six major areas of data science?
The following are the six primary subfields that fall under the umbrella of data science:
- Investigations involving multiple disciplines. Data scientists look at huge, complicated systems that are made up of many interrelated parts, and they collect large volumes of data using a variety of different approaches.
- Models and approaches for data. Data scientists are required to rely on their experience and intuition when determining which modelling techniques would work best for their data, and they are also required to continuously alter those modelling techniques in order to narrow in on the insights that they are looking for.
- Pedagogy. It is the responsibility of data scientists to collaborate with businesses and their clients in order to find the most effective philosophies to implement in the process of data collection and analysis pertaining to the businesses’ customers and products.
- Doing computations with the data. The size of the information pool that they are working with is so massive that it is necessary for them to use tools and software in order to analyse the involved algorithms and statistics. This is the single most important thing that all projects involving data science have in common with one another.
- Theory. The theory behind data science is a dynamic and complex professional field that has a wide variety of potential applications.
- Tool evaluation. Data scientists have access to a wide variety of tools that can be used to alter and analyse massive amounts of data; however, it is essential that they continually assess the usefulness of these tools and continue to experiment with new tools as they become available.
Industries that rely on data science
The following list of industries and sectors, among others, are heavily impacted by the work of data scientist professionals. However, this list is not exhaustive:
- agriculture big data
- digital economy
- fraud detection
- Healthcare personnel and staffing resources
- Analytics for the IT marketing sector
- optimization of marketing efforts
- public policy
- risk management
- manufacture based on robots, machines, and translation
- informatics in the medical field
- social science
- recognition of spoken language
History of data science
The majority of data science can be classified as a subfield of computer science. Peter Naur, an early innovator in the field of computer science, is credited as being the one who first coined this term in the year 1960. In the book that he wrote in 1974 titled “Concise Survey of Computer Methods,” he explained the fundamental components of the methods and approaches that are employed in the field of data science.
The phrase “data science” was first used during a meeting held by the International Federation of Classification Societies in the year 1996. In his article titled “Data Science: An Action Plan for Expanding the Technical Areas of Statistics,” which was first published in the year 2001 in the International Statistical Review, a computer scientist by the name of William S. Cleveland formally introduced the field of data science as a distinct academic field. Over the course of many years, it mutated and expanded into the quickest, most in-demand method of conducting study in contemporary technology.
Recently, the Office of Personnel Management (OPM) for the United States government agencies authorised the agencies to use a parenthetical of (data scientist) along with the occupational title for positions that perform data science work as a significant portion of the job. This authorization was issued for positions that perform data science work as a significant portion of the job. The Office of Personnel Management (OPM) has concluded that employment related to data science can be located in a variety of occupational series. These occupational series include jobs in epidemiology, actuarial science, operations research, statistics, and information technology, among others. The Center for Optimization and Data Science provides assistance to data scientists working for the Census Bureau and promotes the agency’s leadership in adaptive design, data analytics, and machine learning for the benefit of other federal agencies.
Although it is consistently ranked as one of the greatest careers in consistent yearly polls, being a data scientist still has some of the same challenges as being a statistician or working in a role that is quite comparable. Although they are frequently recruited to make sense of massive information systems, they are not necessarily always given specific questions to ask or directions to take their research. This is despite the fact that making sense of large information systems is a common reason for their employment. Rather than spending the money necessary to hire an entire data science team, many businesses instead ask their staff to conduct data science work. They also occasionally deal with data that is either erroneous or disorganised, which is referred to as “dirty data.” This can improperly distort the conclusions of their models.
Data scientist vs. data analyst
Data analyst and data scientist are terms that are frequently interchanged with one another. However, while many of the skills are similar to one another, there are also some notable distinctions amongst them.
In general, the duties of a data analyst include the collection of data, the processing of that data, and the performance of statistical analysis making use of the basic statistical tools and techniques. However, the specific duties of a data analyst can vary depending on the firm. In addition to this, analysts look for patterns in the data and draw correlations between different aspects of the data in order to find new areas where corporate procedures, products, or services can be improved. In some circumstances, data analysts are also responsible for the design, construction, and maintenance of relational database systems and large data systems. According to Glassdoor, the national average compensation for a data analyst in the United States in October 2019 was $67,377.
Data scientists are accountable for all of these responsibilities as well as many others. It is required for these experts that they have the research background necessary to design new algorithms that are tailored to specific situations. These professionals are equipped to examine big data utilising advanced analytics technologies. It’s also possible that they’ll be entrusted with exploring data without being given a particular issue to tackle. In this scenario, they need to have a sufficient understanding of the data as well as the business in order to formulate questions and deliver insights back to the executives of the business, with the intention of improving the operations of the business, its products, services, or its relationships with its customers.
Difference between structured and unstructured data
The capability of data scientists to perform analysis on unstructured data is one of the primary characteristics that set them apart from traditional statisticians and mathematicians. Information that can be examined, mapped out, and imported into databases, spreadsheets, and other organised systems is referred to as having a structured format. Unstructured data, on the other hand, has a more natural feel to it and necessitates the use of inventive methods, such as coding, in order to be loaded into analytics models.
For instance, if a weather channel publishes 45 new weather-related videos on their website within the span of one month, the structured data may include information regarding the number of times each video was uploaded, the length of each video, and the keywords that were associated with each video. The analysis of unstructured data, which is typically qualitative in nature, could include anything from sentiment analysis (such as determining whether the presenter’s tone was upbeat) to determine how well the video fit the brand of the weather channel.
That data might be able to be mapped in a graph database, but it might also be able to be allocated codes and processed like quantitative information instead. In a similar vein, it might be simple to acquire quantitative findings depending on how individuals responded to each video if it featured some type of positivity metric, such as a favourites button. This would make it possible to track how people liked different videos. But a data scientist would need to go deeper into some qualitative study if they wanted to collect data regarding public reactions to it that went beyond those who gave comments on it.
Somewhere in between structured data and unstructured data is where you’ll find semi-structured data. Data is said to be semi-structured if it is capable of being placed into extremely specific categories and subcategories, but it is not already arranged into compartments that can be easily manipulated.
Common methods used in data science
Learning either through machines or through statistical analysis. The fields of artificial intelligence known as machine learning and statistical learning involve the ability of systems such as computers to improve their accuracy and efficiency at tasks over the course of time by employing algorithms and statistical models on their own, without the assistance of a human programmer. Machine learning and statistical learning are subfields of artificial intelligence.
Signal processing. Any method that is used to evaluate and improve digital signals is considered to be signal processing.
Data mining. Data mining is the process of collecting large amounts of information about websites, users, software, or other stakeholders in a digital process and storing it in databases. This is typically done with the intention of gaining knowledge about customers or product users in order to improve business practices and sales.
Databases. Large collections of information that have been compiled for the aim of organising and analysing data are referred to as databases.
Data engineering. Data engineering, which is very similar to data science, is the process of manipulating data in a variety of different ways with the goal of either gaining new insights or enhancing operational efficiency.
Visualization. Viewers don’t need to be involved in the granular parts of an investigation if large volumes of data are instead presented in the form of charts or models that can be rapidly comprehended by the audience.
Data preparation. Any technique that is used to integrate, acquire, organise and structure data in a manner that is either visually appealing or easily edible is referred to as data preparation.
Modelling that is predictive The act of building charts and models to test various scenarios and, by applying statistics and mathematics, trying to make the most educated guess as to the event that is most likely to occur is what is known as predictive modelling.
The Hot Job of the Decade
The head economist at Google, Hal Varian, is credited with making the following prediction: “The sexiest job in the next ten years will be statisticians.” People think I’m joking, but who would have thought that working as a computer engineer in the 1990s would have been the sexiest career available?
If the definition of “sexy” is possessing unique attributes that are in high demand, then data scientists already fit the bill. Because of the intense competition in the market for their skills, it is tough and expensive to recruit them, and it is also challenging to keep them. They possess a rare combination of scientific training, and analytical and computational prowess, which means there aren’t many people who can compete with them.
Data scientists of today are analogous to the “quants” who worked on Wall Street in the 1980s and 1990s. During that time period, individuals with a background in physics and mathematics flocked to investment banks and hedge funds in order to develop whole new algorithms and data methods. These professionals were able to do so because these institutions offered lucrative compensation packages. After that, a number of educational institutions began offering master’s degrees in financial engineering, which resulted in the production of the second generation of talent that was easier for mainstream businesses to recruit. This cycle was repeated later on in the 1990s with search engineers, whose specialised abilities were swiftly incorporated into the curriculum of computer science degree programmes.
This leads to the question of whether or not certain businesses would be better off waiting until the second generation of data scientists arrives, at which point the candidates will be more numerous, less expensive, and easier to evaluate and absorb in a corporate setting. Why not give companies like GE and Walmart, whose aggressive business plans compel them to stay at the forefront of their industries, the responsibility of tracking down and taming unconventional talent?
The flaw with that line of thinking is that the development of big data shows no signs of slowing down in the foreseeable future. If businesses choose to wait out the early stages of this trend because they lack the necessary talent, they run the danger of slipping behind as their rivals and channel partners develop advantages that are nearly impossible to overcome. Imagine that big data is an amazing wave that has been building up and is just beginning to crest. It is necessary to have persons that are able to surf in order to catch it.