What is Data Science?
Data Science is study of raw data, thus providing meaning to the complex or big data. Data is collected through different channels and is used not to execute rather learn and develop new business capabilities. Data Science Course is the most recent wave in the new upcoming technologies in today’s scenario. In a study, it has been observed that increase in profit and return on investment on data science is increasing day by day. Companies apply data science to everyday activities to bring value to their customers. Most of the industries, such as banking institutions, are counting on data science for fraud detection successes. Companies like Netflix also use algorithms to analyse user preferences and therefore to determine what to deliver to its users.
Data science is a separate field and is close to computer science. It involves creating programs and algorithms to record and process data. Data science covers all types of data analysis which may or may not use computers. Data Science is quite closely related to the statistical science, which includes the collection, organisation, analysis, and presentation of data.
Data Science covers the entire scope of data collection and processing.
As increasing amount of data becomes more accessible, large tech companies are no longer the only ones needing a Data Scientist. There is a growing demand of data scientists which is not equal to the supply of the data scientists, hence, there is shortage a of the qualified candidates available to fill the open positions. So, choosing Data Science as a career option has a lot of scope and potential and will remain so in the near future.
Data science is evolving and its application will continue to bring change. Data science may save money and improve efficiency of business process, but these technologies can also destroy business value. The risk of inability to identify and manage data can lead some managers to delay the adoption of the technologies and thus preventing them to realize their full potential.
Data science in risk management has always been a matter of measuring; it quantifies the frequency of loss and multiplies it by severity of the damage. Any forward thinking organisation asses and tracks its risk factors and tackles complex challenges using Data Science as it provides analytical tools.
So a separate vertical is required to manage and use data science.
Generally speaking, the data science workflow looks like this:
- Ask a question;
- Gather data that might help you to answer that question;
- Clean the data;
- Explore, analyze, and visualize the data;
- Build and evaluate a machine learning model;
- Communicate results
Scope of Data Science
The demand for Data Scientists has increased manifold over the period of time and there is a huge scope for Data Scientists who want to make a career in this field. As Data Science is being used in almost every sphere of society, be it industries, education, entertainment, health etc. It makes this a very promising career.
10 applications of DATA Science in various domains or fields is as below:
- Fraud and Risk Detection
- Internet Search
- Targeted Advertising
- Road Travel
- Website Recommendations
- Advanced Image Recognition
- Speech Recognition
- Fraud and risk detection:- Banking and Financial services industry has a separate segment for data analysis. The earliest application of Data Science was in finance. Data Science was brought in order to rescue the organisations out of losses. It helped them to segment the customers on the basis of past expenditure, current credits and other essential variables to analyse the probability of risk and default. It also helped them to push their financial products based on customer’s financials.
- Healthcare:- Healthcare database of individuals who have been using healthcare systems for a long time helps in identifying and predicting disease and personalized healthcare recommendations. For e.g. some individuals are diagnosed with diabetes and a subset have developed the complications. Data Science becomes useful in drawing patterns of the complications and probability of the complications therefore advising the necessary precautionary steps.
- Targeted Advertising:- Thedigital advertisement get a higher click through ratings rather than traditional advertisements. It is targeted based on user’s previous behaviour. Automating digital ad placement is the reason the wife sees an apparel advertisement and the husband sees a real estate deal advertisement at the same place and same time.
- Internet Search:- We have many search engines such as Yahoo, Bing, Ask, AOL, and Google of course. All these search engine use data science algorithms to deliver the best results and it is their responsibility to verify the resource and deliver the correct result.
- Website recommendations:- E-commerce provides a personalised digital mall to everyone. Using data science, Companies have become intelligent enough to push and sell products as per customer’s purchasing power and interest through previous product searches or purchases. In Amazon, we get suggestions about the similar products that we had earlier looked for.
- Road Travel:- A perfect example is Google maps in which Google uses the road maps data to update the app. The biggest challenge is to keep the map updated on real time basis as it has to be updated as per the traffic in the particular area as well as any ongoing construction, road blocks, bad weather etc with an alternative route.
- Government:- Government is maintaining the records of the citizens in their database including the photographs, fingerprints, addresses, phone numbers etc in order to maintain law and order in the country. This data helps the government in taxation, passing on financial benefits to the needy, and even tracking down the lost people.
- Advanced Image Recognition:- When we upload an image on Facebook, we get suggestions to tag friends. These automatic suggestions uses face recognition algorithms. Apple uses the same kind of software to segregate photo in the photo gallery. Online payment app uses QR code to make the payments successful.
- Speech Recognition:- Best example of speech recognition products are Siri, Alexa, Google voice, Cortana etc. Now days, it is an added feature in almost every electronic product which uses graphic user interface to take commands from its users. Speech recognition is being used to type messages on almost every message sharing applications.
- Gaming:- Electronic games are designed using machine learning algorithms which improves and upgrade themselves as the player moves up to the next level. In motion gaming to the opponent (computer) analyses, previous moves and accordingly shapes the games.
With each passing day, the volume of data is increasing and it demands analytical tools for storing, processing and analyzing data.
So, with the provided scope and tips, you can choose your career in Data Sciences.
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