What is Data Science and its types?
What is Data Science?
Data scientists are the burning top among professionals and companies that focus mainly on data and draw insight out of data and business growth. Data is the asset of an organization if used efficiently. The need for storage of data grew in 2010 when the age of big data came in. As Hadoop, big data taking care of the storage, the concern moved to insight towards the data and business.
Why do businesses require data science?
We have been working with small sets of structured data to large mine of semi-structured or unstructured data coming from various sources. The traditional BI tools lack processing a huge amount of unstructured data. Data science comes with enhanced tools and works on a large amount of data such as finances, marketing forms, telecom, e-commerce, etc.
Different types of Data scientist
There are numerous data scientist designations in different industries for a data scientist. Marketing research requires a statistician who will crunch data and would bring insight and strategy for business growth. While an advertising company will require a data scientist to look into the TRP report of the channel and create insight for their client.
List of different types of data scientists are
- Machine Learning Scientists
- Data Engineers
- Software Programmer analyst
- Digital Analytics consultant
- Quality Analyst
- Business Analytic Practitioner
- Spatial Data Scientist
- Actuarial Scientist
Machine Learning Scientists – Machine Learning scientists aim to explore new innovative approaches which deal with fundamental and applied research. Uses algorithms that are accustomed to strategies, patterns, large input data, and demand forecasting. They apply algorithms, build a model which suits best for the data set. Machine learning algorithms include linear regression, clustering, logistic regression, forecasting models like ARIMA, SARIMA. Machine learning has proved to be used in numerous fields like pattern recognition, web- searches, email-spamming, fraud detection, credit/score, and many more.
The one who practices science and works on data to make the decision. They decide what data they require and how they will collect that data, analyze the data and then interpret it through reports. The statistician is used in healthcare, public safety, environmental study, and sports. A person with a bachelor’s or master’s degree in statistics is qualified for the job role. With a degree, the person should also have the right level of training and knows how much data needs to be collected.
Mathematicians have been earning a lot of acceptance in the corporate world due to their profound knowledge in the area of applied mathematics and research capabilities. Their deific presence in the business world is a boon to the company in executing analytics in multiple fields like supply-chain and pricing algorithm etc.
Data engineers are It professional who prepare data for analytical and operational use. They are software engineers who are responsible for building data pipelines to bring data from different sources. They cleanse, consolidate and structure the data to use it in analytics applications. They aim to make it accessible for their organization. The amount of data varies with the organization. Data engineers work with a data scientist in terms of improving the data and enable the business to make better decisions.
Software Programmer analyst
Software programmer analysts can do complex calculations using programming ability. They adopt programming languages such as python, R programming, supporting visualization and analytics.
Digital Analytics consultant
Digital analytics consultants are professionals who require talent and business marketing skills to be successful. They configure web pages, collect data and direct it to analytical tools and visualize it through processing, designing on the dashboard.
The quality analyst has been connected with the statistical process used mostly in the manufacturing sector. The job has been modernized with analytical tools used by a data scientist to prepare, interact and visualize core inputs in making a decision.
Business Analytic Practitioner
Business Analytic Practitioner is a business professional who acquires business as well as data knowledge. Business Analytic Practitioner makes important decisions related to the business and builds dashboard ROI. Analysis of high-level database design, optimization, etc.
Spatial Data Scientist
Spatial Data scientists are a subset of data science that focuses on spatial data, moving beyond looking at the thing that happens at that place and why. Google maps, bing maps, car navigations, and numerous applications make use of spatial navigation, localization, etc. Python and R programming are the most commonly used languages in the community. Python and R include special spatial libraries.
The actuarial scientist is all about evaluating risks and maintains the economical stability of insurance or finances. Actuarial graduates know the use of statistics, mathematics, and probability principles to predict future events and would take preventive measures.
If you are planning to make a career in data science you have huge options mentioned above for yourself. But you need to figure out which suits best for you and how you can make a career in that domain.
Data Science Applications
Data Science has multiple applications in different sectors like mentioned below-
- Fraud and Risk detection
- Credit scoring
- Recommendation system
- Advanced Image search
- Airline Routing
- Social media recommendations
- Augmented Reality
- Character Recognition
Top Qualities of Data Scientist
Below mentioned are some of the top qualities that are required to be a data scientist.
- Statistical thinking
- Technical expertise
- Curious mind
- Mathematical expertise
- Strong business knowledge
There are skills you need if you want to be a data scientist
1. Mathematical Expertise
There is a misconception that data science is all about data analysis and statistics. No doubt Bayesian statistics are important but quantitative techniques are important too, especially linear algebra and machine learning algorithms.
2. Strong business knowledge
The data scientist must have the critical business knowledge and is also required to share applied business knowledge with the team. They are in a position to contribute to business strategy as they have sufficient exposure to data like no one else. Hence, they require business knowledge.
3. Technical skills
They require strong technical skills in Python, R programming, and they are expected to code and have problem-solving capability. Along with Python and R, they also require knowledge of SAS, SQL, Scala, Julia, etc.
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