Data Science

What is data science?

Data science combines math and statistics, specialized programming, advanced analytics, artificial intelligence (AI), and machine learning with specific subject matter expertise to uncover actionable insights hidden in an organization’s data. These insights can be used to guide decision making and strategic planning.

"Data science" realized a disciplinary approach to interpreting and analyzing data, both structured and unstructured. To arrive at these conclusions, it relies on techniques such as statistics, mathematics, analytics, programming, and scientific methods. Data science aids in the discovery of trends and patterns in data, allowing stakeholders to make well-informed decisions about their businesses. The data science approach relies on the use of analytical tools and techniques to extract and interpret information. Predictive analysis can be used to help solve complex industrial problems.

Weighing in on Data Science:

• For better marketing and customer acquisition, data science is a boon to industries.
• It aids businesses in resolving a variety of issues and in making quick decisions.
• Uncovering hidden patterns and trends from data is a major goal of data science.
• Additionally, businesses use data science for innovation and to improve the lives of their customers.
  

Why Data Science?

Information overload is making it increasingly difficult to find the accurate data needed for business decisions. Nowadays, most of the data is unstructured and one could find a lot of junk data when they extract information. There is a new field of study called data science.

These days, every company has a data scientist who examines their previously unused and inactive data to see if it can be useful. The importance of having a data scientist on staff is realized by every company, resulting in an increased demand for certified and qualified data scientists. Worldwide, data scientists are in demand and can earn more than other professions.

Uses for Data Science:

Finance

Companies in the financial sector rely on data science to automate a variety of financial tasks to make strategic business decisions. Personalization with customers has also been made possible by its financial institutions.

Manufacturing

To reduce costs, optimize production and increase profits, data science has been widely used in the manufacturing industry. Now, industries can easily optimize their production hours and monitor their energy costs, thanks to a new technology called Smart Energy.

Healthcare

Applied data science plays a major role in the healthcare industry. Oncologists can detect cancer & tumors at an early stage by utilizing the various tools and techniques available. Choosing a patient's treatment has become a lot simpler.

E-commerce

Various aspects of data science have changed the E-commerce industry. As a result, it can be used to identify a potential customer base, optimize pricing, and predict trends.

Learnbay's domain elective module is listed below:

• Telecommunication
• Hospitality and transportation
• HR
• Marketing and Sales
• Pharma and healthcare
• Banking
• Manufacturing
• Oil, gas, and energy
• E-commerce and retail.

Prerequisites for Data Science:

1.  Machine Learning
2.  Modeling
3.  Statistics
4.  Programming
5.  Databases

Who Oversees the Data Science Process?

1.  Business Managers
2. IT Managers
3. Data Science Managers

What Does a Data Scientist Do?

•  You know what is data science, and you must be wondering what exactly is this job role like - here's the answer. A data scientist analyzes business data to extract meaningful insights. In other words, a data scientist solves business problems through a series of steps, including:
• Before tackling the data collection and analysis, the data scientist determines the problem by asking the right questions and gaining understanding.
• The data scientist then determines the correct set of variables and data sets.
• The data scientist gathers structured and unstructured data from many disparate sources—enterprise data, public data, etc.
• Once the data is collected, the data scientist processes the raw data and converts it into a format suitable for analysis. This involves cleaning and validating the data to guarantee uniformity, completeness, and accuracy.
  When the data has been completely rendered, the data scientist interprets the data to find opportunities and solutions.
• The data scientists finish the task by preparing the results and insights to share with the appropriate stakeholders and communicating the results.
• Now we should be aware of some machine learning algorithms which are beneficial in understanding data science clearly.