You are travelling from your client’s office to airport with the help of Google Maps. You notice that the app routes you to small side streets instead of directing you to take the more direct freeway route. This is so because the app calculates the quickest and shortest possible route in real time based on your current conditions. It determined that there is heavy traffic on the direct freeway route and it directs you to the alternate route. This is just a simple daily life example on how data science is applied in real time. There is a lot that the data science community can do to derive social and economic value from big data.
According to the New York Times-“Data Science is a hot new field that promises to revolutionize industries from business to government, health care to academia.”
What is Data Science?
Big Data, Predictive Analytics and Data Science are the most hyped words - right from White House hiring its first Data Scientist DJ Patil to United Nations forecasting bombings on educational institutions using Predictive Analytics. Big Data has boomed due to – explosion of mobile apps market, enhanced processing power at economical storage cost and a couple of other innovations that are providing data to businesses.
Having big data and knowing what to do with the big data are two sides of the same coin. In an ideal world of fantasies, Big Data would miraculously arrange itself into useful insights but in reality, there is need for a process to train, categorize and implement this big data for businesses- this is what Data Science is. Data Science has become a necessity among businesses to maintain the competitiveness in an exponentially increasing data-rich environment.
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The word “Data Science” was first used in 1996 at International Federation of Classification Societies in Kobe, Japan during the Biennial conference but it has become very popular in the past few years.
According to Mike Driscoll, Metamarket CEO-“Data science, as it’s practiced, is a blend of Red-Bull-fuelled hacking and espresso-inspired statistics. But data science is not merely hacking—because when hackers finish debugging their Bash one-liners and Pig scripts, few of them care about non-Euclidean distance metrics. And data science is not merely statistics, because when statisticians finish theorizing the perfect model, few could read a tab-delimited file into R if their job depended on it. Data science is the civil engineering of data. Its acolytes possess a practical knowledge of tools and materials, coupled with a theoretical understanding of what’s possible.”
The watchword in “Data Science” is not “Data”, it is the “Science” behind it which can be used to answer a business analytic question using the data. It is relatively easier to say “My business has data bigger than yours” or say “I can code in Python, can you?” than saying “I can answer this really difficult question with my data”. The impact of data science is measured by the complex questions that can be answered using data. Data Science has garnered the status of a “competitive differentiator” for organizations across various industries.
The art of turning data into actions can be termed as “Data Science”. This can be achieved by developing data products that provide meaningful and actionable information without revealing underlying big data analytics to the decision makers. Data Science uses raw data and algorithms to predict consumer behaviour in order to enhance customer experience. Data Science requires extracting actionable information from disparate data sources to drive data products.
Some examples of the outcome of data science i.e. the data products are-
- Friend Recommendations on Facebook
- Music Recommendations on Spotify
- Product Recommendations on Amazon
- Dynamic Learning and Customized Assessments at Knewton Academy
- Trading Algorithms, Models and Credit Ratings in Finance.
- New government policies based on data.
- Predicting Flu Trends in Health
- Targeted Advertising
How does data science work?
Data Science is a complex field of study but it is not rocket science - it’s something better.
“Data science requires a combination of technical expertise and deep, domain-specific knowledge. “- CEO of Correlate Thomas Hallaran
Data Science has 3 important components-
1) Organising Data-The physical location, structure and format of data is planned and executed. This component of data science incorporates the best practices of data management.
2) Packaging Data-Logically manipulating and merging the raw data, performing statistics, building new prototypes and creating visualizations.
3) Delivering Data-The message that the data has, is accessed by its end users. The story is told and the value is obtained for business.
Data Science process is iterative as various components combine with each other slowly. However, data science process can be regarded as a 7 step process-
1)The foremost step in any data science process is to define the ‘question of interest’ of business analytics.
2)Once the ‘question of interest’ is defined, the data governing the ‘question of interest’ is collected.
3)The next step is to clean the data obtained from disparate sources.
4)Explore the data.
5)Fit in the statistical and machine learning models to the data.
6)Communicating the results in an understandable and visualized form.
7)The ultimate step is to reproduce the analysis again and again.
Data Scientists must have a clear vision on the output of a data science process.
DIKW Pyramid -Secret of Success in Data Science
The most valuable data products can be developed through data science by following the DIKW approach to a real time analytics application-
The fundamental block of an application is product’s data that will help customers achieve what they are trying to do. Data is an asset for real time applications even if it is not refreshed regularly. Businesses should not undervalue the economic benefit of providing customers with data.
The next level in the DIKW pyramid is Information and businesses should hire data scientists who can do a lot better than just data because customers do not want to spend their valuable time in finding out what the data means. For instance, when you turn on the Google Maps App, it displays your current location and the route to next destination but you also expect your GPS to guide you on when to take left turn or right turn. This is the kind of in-depth information customers need.
Information products are out-dated; businesses can do better by adding knowledge to products through data science. Data Science allows businesses to connect the various related dots from different data sources to draw insights and bring awareness amongst customers about novel products and services that would not be possible otherwise.
The business is said to have achieved wisdom if the application becomes really valuable and interesting to customers. Knowledge will help businesses predict the correct course of action depending on current conditions; wisdom will help businesses predict the correct course of action based on future conditions. Effective data science approach for an organization is to apply advanced analytic models to take the product or service to a supreme level.
For businesses to have customers for life they need to be competitive with their data science process. DIKW pyramid framework should be used to gradually enhance their data science products and services to reach the top.
Big Data is paving way for Emerging Job Opportunities in Data Science
“We hear it every day from customers: They need people who can make sense of data and apply it to make better business decisions.”- said Emily Baranello, director of the SAS education practice.
Data Science has opened up opportunities for a new job role in Big Data analytics – Data Scientist. Data Scientist is a practitioner of data science who is a business domain expert, computer programmers, statistician, mathematician or an amalgamation of all these skills. Data scientist works collectively with business analysts, marketers and data owners. Data scientists are professionals who bridge the gap between analyzing Big Data and using the findings of analysis to produce outcomes that align with goals of an organization. A good Data Scientist is one who can differentiate between an organization that understands data science discipline and an organization that only practices data analytics.
According to a McKinsey report, the job market for data scientist is anticipated to grow by 15% from 2012 to 2022. This growth rate is faster than the average rate for all other occupations. Data Scientist has been ranked #15 by Glassdoor among the curious jobs that are in high demand and ranks #9 among the “Best Jobs in America for 2015” with 3433 job openings on Glassdoor as of February 2015 with an average base salary of $105,395.
“If you want to get a job quickly, figure out how to become a data scientist,” says Jim Davis, executive vice president and CMO at SAS.
Data science is a progressively growing discipline that turns information into gold and is changing how decisions are made across businesses in all industrial sectors across the world. The companies that can’t incorporate data science in business will fall by the wayside.
We have launched Data Science in Python to help you start your career in Data Science.
Are you into data science? What advice will you give to individuals or organizations trying to program their way into data science? Let us know in comments below.