Data Science Technology & Its Usage


Data Science has emerged out as one of the popular fields of 21st Century. Companies hire Data Scientists and Data Analyst to help them gain insights about the market and to improve their products and services in better way.

Data Scientists work as decision-makers and are responsible for analysing and for handling a large amount of organized or predefined data as well as raw data which have to be first organized to analyse.

Data science is the field of study that consists of domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data. Data scientists apply machine learning with python algorithms to numbers, text, images, video, audio, and more to produce artificial intelligence (AI) and deep learning to perform tasks that work on the human intelligence to make every field more precise. In turn, these systems generate insights which analysts and business users can translate into tangible business value.

The field includes preparing data for analysis, formulating data science problems, analysing data, developing data-driven solutions, and presenting findings to inform high-level decisions in a broad range of application domains. As such, it incorporates skills from computer science, statistics, information science, mathematics, information visualization, data sonification, data integration, graphic design, complex systems, communication and business.

 

Brillica Services Provides best project based training course in Data Science using python Machine Learning in Dehradun, Uttarakhand & Delhi.

 

Why Data Science training course is Important?

In 80s, "data analysis", acknowledged the emergence of a new discipline focused on data of various origins and forms, combining established concepts and principles of statistics and data analysis with computing.

With every increasing day, most companies are coming realizing the importance of data science, Artificial Intelligence, and machine learning. Regardless of industry or the size, organizations those wish to remain competitive in the age of need of data to efficiently develop and implement data science capabilities or risk being left behind.

 

Uses of data science:

·       Internet Search

·       Digital Advertisements (Targeted Advertising and re-targeting)

·       Recommender Systems

·       Advanced Image Recognition

·       Speech Recognition

·       Gaming

·       Price Comparison Websites

·       Airline Route Planning

·       Fraud and Risk Detection

·       Delivery logistics

·       Healthcare

·       Augmented Reality

 

A best data scientist is the one who creates programming code, and combines it with statistical knowledge to create insights from data. He is the one responsible for extracting, manipulating, pre-processing and generating predictions from the information provided.

 

Tools Used In Data Science:

Sas, Apache Spark, MATLAB, Excel, Tableau, Jupyter etc.

 

Follow for more: https://www.brillicaservices.com/

 

 

Comments

Post a Comment

Popular posts from this blog

What-if Analysis Tool in Excel

Image
In Excel , What-if analysis is a process of changing the value of the cells to see how the changes will affect the outcome. You can use different sets of values to explore all the various leads to one or more formulas. Nowadays, What-if Analysis in Excel is used by every data analyst and especially management professionals to make better, faster and more accurate decisions based on the data. What-if analysis tool is used in many situations, like: • To propose different budgets based on revenue. • To predict the future values based on the given historical values. • If you expect a certain value due to a formula, you can find different sets of input values that produce the desired result. To enable the what-if analysis tool you have to go to the Data menu tab and click on the What-If Analysis option. Goal Seek is a What-If Analysis tool which helps you to find the input value which results in a target value that you desire. Goal Seek requires a formula which uses the input...
Read more

Opportunities in Cyber Security

Image
Cyber security is the practice to defend computers, servers, mobile devices, electronic systems, networks, and data from malicious attacks. It is also referred as information technology security or electronic information security.   Application security focuses on keeping software and devices threat free. Cybersecurity course is also Empowering and educating current and future cybersecurity Professionals globally with skills & Knowledge, with industry-specific training, certifications.   Skills needed for cyber security: • Problem-Solving Skills. • Technical Aptitude. • Knowledge of Security Across Various Platforms. • Attention to Detail. • Communication Skills. • Fundamental Computer Forensics Skills. • A Desire to Learn. • An Understanding of Hacking.   Importance of Cyber Security: Cybersecurity is vital because it protects all categories of knowledge from theft and damage. This includes sensitive data, personally identifiable inform...
Read more

Algorithms of Machine Learning

Image
  We are, in all likelihood, living in the most defining period of human history. The period when computing moved from large mainframes to PCs and then to the cloud. But what makes it more defining is what is coming in our way in the upcoming years. What makes this period exciting and captivating for someone is the normalization of the various tools and techniques, which followed the boost in computing.   Widely, Machine Learning Algorithms are classified as: 1. Supervised Learning : Consists of a target / output variable (or dependent variable) which is to be predicted from a given set of predictors (independent variables). Using these set of variables, we generate a function which map inputs to desired outputs. The training process continues until the model achieves a desired level of accuracy on the training data. Some of the examples of Supervised Learning are Regression, Decision Tree, Random Forest, KNN, Logistic Regression etc.   2. Unsupervised Learni...
Read more