Data Science and Analytics

Data Science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from noisy, structured and unstructured data, and apply knowledge from data across a broad range of application domains. Data science is related to data mining, machine learning and big- data. The field encompasses preparing data for analysis, formulating data science problems, analyzing data, developing data- driven solutions, and presenting findings to inform high- level decisions in a broad range of application domains. It uses techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, information science and domain knowledge.

In the simple words, it is the field of applying advanced analytics techniques and scientific principles to extract valuable information from data for business decision- making, strategic planning and other uses. Data scientists examine which questions need answering and where to find the related data. They have business acumen and analytical skills as well as the ability to mine, clean and present data. Businesses use data scientists to source, manage, and analyze large amounts of unstructured data. Data science continues to evolve as one of the most promising and in- demand career paths for skilled professionals. 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.

• 1. Statistical analysis and computing.
• 2. Machine Learning
• 3. Deep Learning
• 4. Data Visualization
• 5. Data Wrangling
• 6. Mathematics
• 7. Programming

Effective data scientists are able to identify relevant questions, collect data from a multitude of different data sources, organize the information, translate results into solutions and communicate their findings in a way that positively affects business decisions. These skills are required in almost all industries, causing skilled data scientists to be increasingly valuable to companies. Data scientists needs to be curious and result- oriented, with exceptional industry- specific knowledge and communication skills that allow them to explain highly technical results to their non- technical results to their non- technical counterparts. They possess a strong quantitative background in statistics and linear algebra as well as programming knowledge with focuses in data warehousing, mining and modeling to build and analyze algorithms.

Data Analytics is the process of examining data sets in order to find trends and draw conclusions about the information they contain. Increasingly, data analytics is done with the aid of specialized systems and software. A data analyst collects, cleans and interprets data sets in order to answer a question or solve a problem. They work in many industries, including business, finance, criminal justice, science, medicine and government. Data analytics is the process of storing, organizing and analyzing data for business purposes. This process is used to inform key decision- makers and allows them to make important strategic decisions based on data, rather than hunches. To become a data analyst, candidates need to hold a bachelor’s degree preferably in Information Technology, Computer Science, Mathematics or Statistics with a minimum of 50% marks from any recognized University.

There are various and several skills that are in demand by the recruiters to become a data analyst like:

• 1. Data Visualization
• 2. Analytic Skill
• 3. Python
• 4. Statistics Skills
• 5. Data Management
• 6. Communication Skill

Data analytics is important because it helps businesses optimize their performances. Implementing it into the business model means companies can help reduce costs by identifying more efficient ways of doing business. A company can also use data analytics to make better business decisions and help analyze customer trends and satisfaction, which can lead to new and better products and services.

Data analytics help individuals and organizations make sense of data. The data analytics process has some components that can help a variety of initiatives. By combining these components, a successful data analytics initiative will provide a clear picture of where you are, where you have been and where you should go. Data analytics is a broad field. There are four primary types of data analytics- descriptive, diagnostic, predictive and prescriptive analytics. Each type has a different goal and a different place in the data analysis process.

Data analysts exist at the intersection of information technology, statistics and business. They combine these fields in order to help businesses and organization succeed. The primary goal of a data analyst is to increase efficiency and improve performance by discovering patterns in data.

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