Data science is a multidisciplinary blend of various tools, algorithms and machine learning principles with the focused on extracting knowledge and insights from the raw data. The term Data Science has emerged recently with the evolution of mathematical statistics and data analysis and it is also known as data driven science. Mining large amounts of structured and unstructured data, which makes use of scientific methods, processes and systems to extract and identify patterns that help an organization to increase efficiencies, recognize new market opportunities.
Why Data Science?
It’s been said that Data Scientist is the “Sexiest Job of the 21st Century”, the reason behind it is over the past years, companies have been storing their data from the various sources and every company has data from which they want to get meaningful information which will help them in growing.
How data Science will help, let’s understand by using an example:
Say, you have a company which makes LED screens. You have released your first product and it became a massive hit. Every technology has a limited life, so now it’s time to come up with something new. But you don’t know what should be innovated, so as to meet the user’s expectations, who are waiting for your next released. You can take user’s feedback and pick things which users are expecting in the next release. The feedback which you get from user’s end, you can apply various data mining techniques like sentimental analysis etc to get desired results which will help to make better decision.
Who is Data Scientist?
Data Scientist is responsible for deriving insights from large amount of data either structured or unstructured to help organization for their growth. The role of data scientist is becoming increasingly necessary as businesses rely more on data analytics to drive decision making.
If you want to know difference between Data Scientist and Data Analyst, check this
Technical Skills for Data Scientist:
1. R or SAS: Good knowledge of at least one analytical tools is generally preferred.
2. Python: Python is most common coding language typically required for data science roles.
3. Database Management: Either SQL or NOSQL, depending on the requirement.
4. Unstructured data: Data scientist should be able to deal with unstructured data. The unstructured data generated from social media platform or any other platform.
5. Visualization Skills: A data visualization tools like Tableau, Qlikview is generally preferred to present insights from data.
6. Statistical Knowledge: Good Understanding of statistics is vital for a data scientist. He should be proficient with statistical tests, distributions, maximum likelihood estimators etc.
Applications of Data Science:
1. Internet search: There are many search engines including Google which make use of data science algorithms to deliver the best result for our searched query.
2. Recommender Systems: A lot of companies used this engine to promote their products in accordance with user’s interest like Amazon, Google Play, Netflix and many more uses this system to improve user’s experience.
3. Price Comparison Websites: These websites are being driven by lots of data which is fetched using APIs and RSS feeds. This websites gives you comparison of the price of the product from multiple vendor at one place.
4. Delivery Logistics: companies like FedEx, DHL are using data science to improve operational efficiency. They used data science to find best routes to ship, best suited time to deliver and best mode of transport.