Data plays a most important role in an organization, whichever industry you work in, or whatever your interests, you will almost certainly have come across a story about how data is changing the face of our world. In general we can say data is a form of information, there are different type of data according to the organization, a company or a institute.
There are different types of data we go through daily are data in news, personal data, transnational data, web data, sensor data, technically there are structured and unstructured data. Data Science is one of the top trending technology of the era and data science has stretched its roots deep down in the corporate industry. Due to this vast inference, new age learners and working professionals are keen to learn this technology. To curb this, various online data science certification are available through which one can master data science and begin career as a data scientist.
Data science is a broad career path and is undergoing developments and thus promises abundant opportunities in the future. Data Science job roles are likely to get more specific, which will lead to specializations in the field. People inclined towards this stream can exploit their opportunities and pursue what suits them best through there specializations and specifications.
Usually there will be a large data will be present in an organization but to manage these data, DATA SCIENCE come to the role.
Data science is a field that uses scientific methods, processes, algorithm and systems to extract data or knowledge inside from the data. Data science is mainly related to data mining and big data.
Data science is study of data using different concepts, this study come under analyzing, mining, modifying and understanding the data.
Data science uses the techniques and theories drawn from many fields within the context of mathematics, statistics, computer science and information science.
Data science continues to evolve as one of the most promising and in-demand career paths for skilled professionals. Today, successful data professionals understand that they must advance past the traditional skills of analyzing large amounts of data, data mining, and programming skills. In order to uncover useful intelligence for their organizations, data scientists must master the full spectrum of the data science life cycle and possess a level of flexibility and understanding to maximize returns at each phase of the process.
There are five stages in the data science life cycle:
- Capture, (data acquisition, data entry, signal reception, data extraction).
- Maintain (data warehousing, data cleansing, data staging, data processing).
- Process (data mining, clustering/classification, data modeling).
- Analyze (exploratory/confirmatory, predictive analysis, regression, text mining).
- Communicate (data reporting, data visualization, business intelligence).
Tools used in data science:
- Apache Spark
- Big ML
Advantages of Data science:
- Data Science is greatly in demand. Prospective job seekers have numerous opportunities.
- There are very few people who have the required skill-set to become a complete Data Scientist. This makes Data Science less saturated as compared with other IT sectors.
- Data Science is versatile there are numerous applications of Data Science. It is widely used in health-care, banking, consultancy services, and e-commerce industries. Data Science is a very versatile field.
- Data Science makes data better companies require skilled Data Scientists to process and analyze their data. They not only analyze the data but also improve its quality. Therefore, Data Science deals with enriching data and making it better for their company.
- Data science makes products smarter data Science involves the usage of Machine Learning which has enabled industries to create better products tailored specifically for customer experiences.
Applications where data science is used:
- Fraud and Risk Detection
The earliest applications of data science were in Finance. Companies were fed up of bad debts and losses every year. However, they had a lot of data which use to get collected during the initial paperwork while sanctioning loans. They decided to bring in data scientists in order to rescue them out of losses.
- Medical Image Analysis
Procedures such as detecting tumors, artery stenosis, organ delineation employ various different methods and frameworks like MapReduce to find optimal parameters for tasks like lung texture classification.
- Genetics & Genomics
Data Science applications also enable an advanced level of treatment personalization through research in genetics and genomics. The goal is to understand the impact of the DNA on our health and find individual biological connections between genetics, diseases, and drug response.
- Virtual assistance for patients and customer support
Optimization of the clinical process builds upon the concept that for many cases it is not actually necessary for patients to visit doctors in person. A mobile application can give a more effective solution by bringing the doctor to the patient instead.
- Internet Search
Now, this is probably the first thing that strikes your mind when you think Data Science Applications. When we speak of search, we think ‘Google’. Right? But there are many other search engines like Yahoo, Bing, Ask, AOL, and so on.
Data Science Is About Answers and Decision-Making:
Data Science Provides More Accurate Answers The tech industry is pulling in massive amounts of data from users on mobile apps and websites, tracking where they go, when they go, what they buy, what they share, what they click, and who their friends are. They are in the perfect position to use this data to “predict the winners”, discovering accurate answers where an educated guess, in the past, would have been as good as they could get.
Data Science Finds the Trends Data science is what tells you what’s hot before the experts even see it on the radar. This is competitive advantage to the nth degree. Forget copycat trends, corporate espionage, or stealing the competitor’s best workers. Data science taps into the information that’s already out there, the information that’s pointing the way a trend is headed.
The Tech Industry Is Attractive to Data Scientists Faster Returns On Data Experiments The faster returns aren’t the only reason the tech industry is pulling in data scientists. During the recent recession, opportunities in academia or on Wall Street dried up as research funding was reduced. Data scientists turned towards tech, filling a need, revealing their value, and driving home the promise and importance of data science.
The Data Floodgates Are Already Open Data scientists in the tech industry are positioned at the leading edge of this data deluge, one that’s already pouring in from mobile apps, internet, social media, eCommerce, and wearable technology.
Big Business, it turns out, really needs Big Data. And because of that, the tech industry is needed to harness the power of data science into something usable. Someone must create the apps and systems and algorithms that power these data-driven customer targeting engines.