Big data is a very common buzz word in the market for a decade or so. It has great value for the business, if processed properly. How can this data be processed? Data science is the answer to this question. It is a multi-discipline combined concept which includes scientific methods to handle raw data, process it through mathematical algorithms and computer programs and extract some valuable insight from that heap of raw data. Data science is extensively used in different industries, especially in finance, insurance and media sectors.
A few years back, BBC Labs in collaboration with Imperial College developed ‘Neurotech’ to get an emotional response from the audience. It is one of the major moves to adopting the hidden power of data science.
This entire process is very complex and requires domain knowledge, qualifications, experience and acumen. This is the reason that data science salary to deal with the complex processes is very high in the competitive marketplace. According to Northeastern University, about 2.5 Exabytes of data is created daily in 2016.
According to Internet Live Stats, over 3.5 billion searches are processed by Google every day. Billions of telephone and video calls, billions of Facebook posts and emails, about 680 million tweets and hundreds of thousands of videos are posted on a daily basis. Every search, post or message by a user has a business value, which the enterprises would like to grab. That value can be in the forms of user interest, need, location or purchase pattern. Data scientist helps business to skim that value and use it in business strategies and policies.
Deere & Company saved more than $1 billion in its different business processes by utilizing multiple data analysis tools based on the power of data science during 2002 through 2005, as cited in the Wikipedia information. There are much other banking and financial institutes that have reduced their credit risk by applying the modern tools based on the data science.
Examples of data science implementation in businesses
British Broadcasting Corporation has recently established a research and development partnership with major 8 universities in the UK for a better understanding audience, content, content personalization and future content strategies. This is one of the major ventures in the media and research organizations in the recent months, which will be helpful for BBC to create more personal services of the BBC for better outreach and penetration.
According to Mashable article published a few years back cited that New York city ambulance response time decreased more than 1 minute by adopting data science-based analytic tools. This was a big achievement the New York healthcare sector a few years back. The present impact of data science-based tools is even more pervasive. The personalization of accurate weather forecast for a particular location based on government information and GPS location was achieved by DARK SKY by applying the power of data science.
According to eAdvancer research, Dickeys Barbecue, London Transportation and German Football Association (GFA) are big examples that implemented the data science in their operations/processes and achieved tremendous results.
Dickeys Inc uses Smoke Stack, which reads the data on all POS of the restaurant outlets and helps plan incentives and special offers for those food items that are slow in certain areas. Similarly, Siemens SAP helped GFA in 2014 to learn the players’ reactions, mindset, and body behavior in certain situations of the game by taking short clips from the live matches. This helped Germany to win the FIFA world cup. London transportation is heavily benefiting from the power of data analytics, data mining and other processes.
Difference between Similar Looking Terms
There are many terms used in data science other than just what is data science or the data science definition. All those terms look similar in one way or the other, but they are very different from each other.
Data science is a combination of different activities like collecting raw data, cleaning it, processing data for valuable information and developing strategic insight from the information extracted. Database analysts, data scientists and data analysts are some of very popular data science careers, which are in great demands nowadays. The field of data science has gained much more importance in recent decades as compared to old day needs. According to a Mashable predictions about the most lucrative technical careers, the data scientist careers stands on the top of the list. This is a very clear indication of the growing importance of data science in the modern businesses. If you look at the salary data of those jobs, you would find the data scientist salary is growing very fast. The average salary for a data scientist in the USA market is about $150,000 per annum, as cited in the Mashable article.
Data analysis is a unified process of getting useful information and data patterns from the raw data collected from the desired source with the help of different data analysis approaches. The major ways used in data analytics include mathematical algorithms, software tools and traditional methods used in manual data analysis. The importance of data analytics can be effectively understood by reading an article published on BBC website. This article clearly predicts the name of the Oscar-winning film in 2016, many months ahead of its announcement. This prediction was entirely based on data analytics tools. It declared ‘The Revenant’ as the winner of the Oscar award and the result was also same after many months. Data analyst salary is one of the major attractions for the people to choose this domain of data science; because the salary of a data engineer stands at $148,000 per annum. The big data analytics is one of the promising businesses in the future. According to IDC, in 2020 the big data analytics market will gain $203 billion.
Data analysis is different from data analytics, although both look very similar. Data analysis is the process of analyzing the data. Data analysis is one the many components of data analytics like data collections, data refining, data cleaning, information collection and reporting. Any person that does the data analysis is called the data analyst or data engineer. Nowadays, the entry-level data analyst salary is more than $64K in the USA. And the salaries for the senior position with over 5 years of experience are much higher than the normal programmers. The average salary for a mid-level career stands more than $150K in the West Coast areas of the USA.
According to Wikipedia, data mining is a more comprehensive and technical approach based on artificial intelligence to discover useful patterns from big sets of data through database systems, machine learning, statistics and other mathematical approaches. In this process, multiple methodologies, techniques and tools along with computer processing power is utilized to dig into the big heaps of data to find out some useful information and data patterns for future business use. The major objective of this process is to skim knowledge from raw data stored in databases.
Larger data sets that cannot be processed or handled through traditional data analysis and data mining tools and software programs are known as big data. The big-data needs specialized tools, approach and techniques to deal with. According to a New York Times book review, data is a limitless oil produced by the wells known as human being. But, this is not limit of big data; big data is produced by many other factors that originate from other factors but pass through human into the data centers. So, big data is bigger than the big data.
Machine learning is one of the fundamental components of artificial intelligence. Machine learning provides machines/computers the ability to learn like the human beings do without any specific programming code for that particular action. This is completely based artificial intelligence in which the computers learn from the patterns of the past big data of the similar kinds and react accordingly. According to a Wall Street Journal article, machine learning or artificial intelligence is going to be one of the top three most influential technologies of 2018 that will substantially influence the day to day life and businesses in the world.
Basic qualifications of a professional data scientist
The role of data scientist is comparatively new in the marketplace. So, generally speaking, the qualifications of a good data scientist should be a combination of the university degree and the domain experience along with the use of multiple methodologies. According to a Mashable article, the understanding process of data sets and the rules to deal with that data set is the fundamental qualification of a good data scientist. It will automatically include the essence of academic studies and power of domain experience.
Generally speaking, a data scientist is a person that deals with the data through software tools and codes on one hand and statistics on the other hand. So, he/she should be an expert in programming and statistics, which are the fundamental qualification to become a data scientist.
According to KDnuggests information, more than 88% of data scientists have Master’s degrees and 46% have Ph.D. degrees. The ratio of education fields is mathematics/statistics (32%) and computer science (19%) followed by engineering, which is about (16%). Other than education, data scientist qualifications should also include:
- Expert level command over Python and SQL database programming
- Command over Hadoop big data platform
- Expert level knowledge of unstructured data
- Strong mathematical/statistical experience
- Knowledge of numerous data analysis tools
- Strong communication skills
- Intellectual curiosity with business acumen
- Creative and deep thinker
What does a data scientist career look like?
According to the Oxford University research regarding artificial intelligence and process automation published on Mashable website, more than 47% of the present day jobs will be replaced by the machine automation, mostly powered by the artificial intelligence. So, the large landscape of present-day job profiles will either vanish or change substantially. The data scientist is one of the most promising careers in many years to come.
Data scientist job is the sexiest job in the 21st century, if we look from the media reports perspectives. It is very attractive in terms of salaries and fringe benefits that a data scientist gets. If we talk about the data scientist career in terms of technical perspective, then it will look like a mathematician with strong coding skills always in search of something from the heaps of data pieces. As far as the job satisfaction is concerned, the data scientist will remain highly content with the payment they get for many years to come. At the same time, the pressure of increasing competition and workload will also haunt them regularly. So, a healthy and progressive thinking is a must for better job satisfaction.
A personal approach toward the career is very imperative for a data scientist. It is a combination of technology and business. If you keep yourself strictly limited to technical analysis and coding, then you can become a senior data scientist and even CTO of a company at later stages. Similarly, by putting extra focus on the outcome of the data science processes, you can move to become a manager and senior executive or even CEO at later stages of the career. It depends on the way you pursue your career. There is no question that it is one of the promising career tracks!
How much data scientists earn?
Data scientists earn very attractive salaries nowadays in the marketplace. The salaries of data scientists are high in the entire global market; they are increasing very fast and consistently. According to Wiki salaries, the average median salary for a data scientist in the USA earns $92,943 per annum. It is big less in European countries and Australia. But, overall salaries of a data scientist are very high in all areas of the globe.
According to PayScale, the average salary of an entry-level data scientist is more than $91K per annum and hourly rate ranging from $20 to $74 per hour.
In such a competitive marketplace, it is very difficult for small and medium-sized businesses to hire and manage dedicated data scientist to support their business with innovative strategies powered by data analysis information. Outstaffing is one of the best options for SMBs to hire a third party data scientist to help improve their business processes.