Understanding the term DATA SCIENCE:
We all are familiar with the term data, which is defined as the raw facts and figures which need some processing for the final output. In the modern world, with the huge accumulation of various types of data in organizations and industries, its handling and management became an immediate need.
With the development of frameworks like the Hadoop (an open-source software framework for data storage), GitHub, etc., the task of storage became easier. Processing of this stored data became an issue which brought data science into the picture.
Data science course involves a detailed study of the enormous amount of data present in the organization’s repository. It is a multifunctional blend of data interference, (the reckless damaging of electronic/computerized data and information without rightful ownership, and introduction of viruses into the data stream) algorithm development, (simplifying the steps for a better understanding to solve a complex problem) and technology.
Role of a data scientist:
To manage such huge amounts of data with proper execution and handling of outcomes, data scientists take up the charge. Data scientists are responsible for discovering insights from large amounts of structured and non-structured data to meet the desired goal set for different business/corporate projects.
Data analytics course is a combination of “data” and “analytics”, which when summed up concludes to the systematic computational analysis of raw facts and figures or statistics for making better decisions and is useful where data and information recording are important jobs. It includes employing technical equipment with an increased computational capacity to read and analyze data. It is used in corporate business and scientific research for a more informed and reasonable decision on the various aspects of the business (product processing, delivery management, etc.) and science (as to verifying or disproving theory, hypothesis or a scientific model). The role of a data scientist has become increasingly important as more industries today rely on “data analytics” for improved decision making and product surveying for a profitable outcome with an increased consumer count. The businesses today are looking forward to automation and machine learning in an attempt to revolutionize their IT and marketing strategy.
Data Scientist requirements:
The following are the kinds of analysis a data scientist is required to perform:
Data analysis of business data to help improve efficiency, lessen production errors, increase consumer count, etc
Data scientists help improve E-commerce businesses by studying the ongoing trends and introducing newer marketing techniques to lure in consumers by developing and improving services and products.
Data scientists take up data on accounts, credit and debit transactions, analyzing the process for safe trade, and fighting fraud.
Security and protection of the data stored by government organizations is a major concern for data scientists today.
Data scientists help researchers analyze, share and interpret experimental data for science work and researches.
6. Social Networking:
Data scientists help study the present-day scenario in the virtual world by studying blogs, growing demands on social networking sites and the consumer inclination to improve the social networking data and services provided to customers.
Nowadays, doctors maintain an electronic record of patient information. The data scientists help manage these records amongst the pile of other sets of records, avoiding confusion and misuse. They also help in improving services and acknowledging newer trends for better medication and diagnosis.
Data scientists help the telecommunications sector analyze and store consumer data in a protective manner, improving services by delivering the required features and providing a bug-free output.
Skills of a data scientist:
1. Programming skills
Programming helps analyze large data sets into simpler ones for easier processing and develops tools for better handling of data.
2. Quantitative analysis of data
Quantitative analysis helps implement machine learning and improve the data managing strategy.
3. Product management
A piece of good knowledge about the product helps perform quantitative analysis and predicts the system behavior along with debugging skills.
4. Communication skills
It is an important soft skill required across many industries for a smooth interaction between the service providers and consumers.
5. Team coordination
For a data scientist to prosper in his field, he needs to coordinate with his teammates and pass on the information for better understanding and outcomes.
The role and importance of data science:
A data scientist breaks down large chunks of data into simpler forms for a better understanding of the complex situational problems. A large number of organizations today are recruiting in data scientists to manage and handle large amounts of data (in gigabytes/terabytes), for a better insight into the cutting-edge technologies.
Handling and managing the big data can be completed by three different types of people involving different skill sets:
- Data scientist:
The data scientist examines which question needs to be solved, the results obtained are then forwarded to the authorities to help analyze both the structured and non-structured data.
- Data analyst:
Data analysts take up complex situational problems, break down the solutions through technical analysis to qualitative items to make the product deliverable as per the latest trend of the market.
- Data engineer:
A data engineer focuses on the development, management, deployment, and infrastructure to transfer data to the data scientists for solutions.
Data science is taking an insight into the strangled sets of data composed of complex situational conditions. It is all about uncovering the findings from the unorganized data set (both structured and non-structured).
It is bringing out the hidden intricate details of the strangled set to the surface for the companies (be it the IT sector, the government organizations, or the entertainment industry) to make smarter decisions for an overall profit. The following are a few examples of how data science has been helping the industries to prosper:
- One of the most famous media-services providers, Netflix, digs deep into the viewing patterns of users to develop and produce shows of similar interests for higher profits and increased viewership.
- Proctor & Gamble analyzes the trends in the past year regarding the product usage and its demand amongst the consumers based on which it continues with the production and quantity of the product.
Data exploration is an important part of data extraction and drawing out results for future usage. The data scientists investigate the characteristic pattern within the data set to reach out to conclusions for better decision making.
- Data product development
A data product is a technical asset that accepts data as input, processes these raw facts and figures out how to give out a meaningful output. An example would be the recommendation engine which takes in user data as input and analyzes the data to make personalized recommendations.
Examples of data products:
- The online shopping websites like Amazon, Flipkart, etc. use the recommendation engine to suggest items for shopping to the buyers as per their interests and needs.
- All the incoming messages on Gmail are analyzed by Gmail’s spam filter which is also a data product to check for junk and viruses.
- The machine learning algorithm used in self-driving cars helps detect traffic signals and give directions through computerized vision (a data product).
The algorithms involved in building these data products are well analyzed and developed by the data scientists. The data scientists work as a technical asset for the industries helping in testing algorithms and carrying out experiments on data handling for giving out better outputs.
Data science is an important tool for a company that wants to expand its kingdom and area of influence. The huge amounts of data contained by these companies hold the secret key to their success, and the right move could take them to the top of their game. Employing the data scientists to manage this data and draw out meaningful conclusions after processing of the raw recorded data helps those in senior decision-making positions of the company to move in the right direction through righteous understandings. Data science projects give multiple returns to the company on its investments in terms of guidance through data readings and insights along with the development of data products.
Though the demand for data scientists is high in the industries, there are not enough of them available due to lack of knowledge. Thus, hiring data scientists is a real tough task, so the companies, when they manage to hire a few, are advised to nurture them and allow them to grow in their own space by managing and learning to develop their algorithms to solve data problems and situations. They must be given autonomy and made to work in an employee-friendly environment with little restrictions on the timing and work. This helps them get more comfortable with their work and makes them highly motivated problem solvers ready to tackle the various kinds of analytical challenges.
Hence, data science is a crucial need by the industries across the globe to handle and manage the large sets of data for a useful and profitable outcome by employing computational techniques, machine learning, and other developed technologies.
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Author bio: Senior Data Scientist and Alumnus of IIM- C (Indian Institute of Management – Kolkatta) with over 25 years professional experience ,Specialised in Data Science, Artificial Intelligence, and Machine Learning.
ITIL Expert certified
APMG, PEOPLE CERT and EXIN Accredited Trainer for all modules of ITIL till Expert
Trained over 3000+ professionals across the globe
Currently authoring book on ITIL “ITIL MADE EASY”
Conducted myriad Project management and ITIL Process consulting engagements in various organizations. Performed maturity assessment, gap analysis and Project management process definition and end to end implementation of Project management best practices.
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