HRDF Fully Claimable HRDF Claimable Data Science Training Course is 100% Claimable for You – Claimable under HRDF SBL Khas for HRDF Registered Company

Why study data science?
With the amount of data being generated and the development of the field of analytics, data science has become a necessity for businesses. In order to take full advantage of data, companies from different fields, be it finance, marketing, retail, IT or banking. Everyone is looking for data scientists. This has created a great demand for data scientists around the world. With the salary the company has to provide, IBM has announced that it will be a hot job in the 21st century. For a lot of people, this is a lot of work. In this field, people from all walks of life can pursue a career as a data scientist.

Components of data science
Data science consists of three parts:

Machine Learning – Machine learning comprises mathematical models and algorithms and is mainly used for machine learning and to prepare for daily work. For example, time-series forecasting is widely used in business and financial systems today. This allows the machine to predict the results of the coming months or years based on historical data models. This is a machine learning application.

Big Data – People generate large amounts of data on a daily basis in the form of clicks, orders, videos, images, comments, articles, RSS feeds, etc. This data is generally unstructured and is often referred to as big data. Big data tools and technologies primarily help convert this unstructured data into a structured form. Suppose someone wants to keep track of the prices of various products on an ecommerce website. You can use the web API and RSS feeds to access the same product data from different websites. Then convert them to a structured form.

Business Intelligence – Every company has and generates too much data every day. This data is carefully analyzed and then presented in a visual report in graphical form so those good decisions can be made. After carefully examining the patterns and details contained in the report, this can help management make the best decisions.

Introduction to Data Science Training Course

• Math and statistics skills
• Machine learning
• Coding
• Algorithms used in machine learning
• The statistical foundations of data science
• Data Structures and Algorithms
• Scientific Computing
• Optimization technology
• Data visualization
• Matrix calculation
• School model
• Experimentation, evaluation and project deployment tools
• Use clustering for predictive analysis and segmentation
• Applied Mathematics and Computer Science
• Exploratory data analysis
• Business acumen and artificial intelligence