Data Science is a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the raw data. As a discipline, Data science involves the collection and study of data – both structured and unstructured – to gain insights and information that can be used by organizations to create effective strategies. By collecting and analysing data and patterns over time data scientists can identify trends and make suggestions to stakeholders that will help them to find new market opportunities, enhance efficiency, reduce costs, and result in a competitive advantage in their industry.
The Pillars of Data Science ExpertiseWhile data scientists often come from many different educational and work experience backgrounds, most should be strong in, or in an ideal case be experts in four fundamental areas. In no particular order of priority or importance, these are:
Let’s take a different scenario to understand the role of Data Science in decision making. How about if your car had the intelligence to drive you home? The self-driving cars collect live data from sensors, including radars, cameras, and lasers to create a map of its surroundings. Based on this data, it takes decisions like when to speed up, when to speed down, when to overtake, where to take a turn – making use of advanced machine learning algorithms.
Let’s see how Data Science can be used in predictive analytics. Let’s take weather forecasting as an example. Data from ships, aircraft, radars, satellites can be collected and analysed to build models. These models will not only forecast the weather but also help in predicting the occurrence of any natural calamities. It will help you to take appropriate measures beforehand and save many precious lives.
Traditionally, the data that we had was mostly structured and small in size, which could be analysed by using simple BI tools. Unlike data in the traditional systems which was mostly structured, today most of the data is unstructured or semi-structured. The data trends show that by 2020, more than 80 % of the data will be unstructured..
This data is generated from different sources like financial logs, text files, multimedia forms, sensors, and instruments. Simple BI tools are not capable of processing this huge volume and variety of data. This is why we need more complex and advanced analytical tools and algorithms for processing, analysing and drawing meaningful insights out of it.
This is not the only reason why Data Science has become so popular. Let’s dig deeper and see how Data Science is being used in various domains.How about if you could understand the precise requirements of your customers from the existing data like the customer’s past browsing history, purchase history, age and income. No doubt you had all this data earlier too, but now with the vast amount and variety of data, you can train models more effectively and recommend the product to your customers with more precision. Wouldn’t it be amazing as it will bring more business to your organization?
Before deciding where to get started, people should make sure they know what data scientists do. In short, they identify problems in existing data analytics and plan to dig deeper into more data to fix them. They do this throughout the entire lifecycle - from choosing the correct datasets to work with, collecting both structured and unstructured data from multiple sources, cleaning and validating datasets, creating and applying algorithms to mine the data, then analysing and interpreting it, and finally presenting the findings to key stakeholders.
The job of Data scientist has become a new trend across the world, as the Data-driven decision making is increasing in popularity. Each and every company no matter how small or big they are, looking for employees who can understand and analyse the data.
It has already been declared as the hottest job, data scientist brings in skill sets and knowledge from various backgrounds such as mathematics, statistics, Analytics, modeling, and business acumen. These skills help them to identify patterns which can help the organization to recognize new market opportunities.
Anyone who has minimum degree qualifications can choose a Data Science course. Prior knowledge of Maths and basics of Statistics and Computer applications is required.
Python programming language is essential statistical tool in Data Science. Apart from this SQL is also important. Python is a general-purpose programming language, used to deploy Machine Learning and Data Engineering models, etc
You can become a successful Data Scientist if you have a strong will. The prerequisites for becoming a Data Scientist are that you should have a basic degree, knowledge of Maths, computers, and Statistics. If you have good communication skills, that would be an added advantage to excel in your career.
Data Science is a vast field that covers various aspects that include Maths, Statistics, Computer Science, and Information Technology. It deals in extracting, analysing, and optimizing massive amounts of data. The rise of Data Science will create 12M job openings by 2026. Now the business is data globally. Data Science is going to conquer the world by providing valuable insights from this data. The demand is going to sustain for a larger period. Data Science is gaining popularity day by day because of its enormous benefits. It helps brands to connect with customers in a personalized way and helps in the engagement of brands and building awareness of the brand. Data Science is not specific to a particular field. The applications of Data Science can be applied to any sector that includes Transportation, Manufacturing, Automation, Education, Entertainment, Healthcare, etc. Today, Data Scientists are working vigorously to innovate new technologies that help to improve and ease human work. The demand for Data Science is rapidly growing as numerous enterprises are adopting innovative technologies to enhance their productivity and efficiency.
Here is the list of a few top hirers of Data Science and Big Data professionals
