A complete overview of data science

Table of Contents

What is data science?

An overview of data science that helps in extracting useful insights and knowledge from huge sets of unstructured and structured data using mathematical algorithms and scientific inference is known as data science. Data science includes a combination of computer science, statistical mathematics, data analysis, machine learning, domain knowledge, and data visualization. These mathematical algorithms require lots of processing and are implemented using computer programs and run on hardware.

Application of data science

Following are the application of data science that depends on data science and ML technologies to solve certain problem sets. Are you looking to become a Data Scientist? Go through 360DigiTMG’s PG Diploma in Data Science and Artificial Intelligence!

Internet search results

When a person searches on the internet or Google, critical ML algorithms decide the most relevant search results. Using these ML algorithms, the pages can be ranked accordingly so that the person gets the most relevant page just by clicking a button.

Recommendation engine

The recommendation engine is a great example of a data science application. It is used by the music streaming service Spotify to recommend its user’s music lists as per their taste and preference. Data science algorithms generate data of each user over some time and recommend the music of their taste. This helps companies to attract more customers. Earn yourself a promising career in data science by enrolling in the Data Science Certification in Chennai offered by 360DigiTMG.

Intelligent digital assistant

One application of data science is using advanced machine learning algorithms in text or voice-based digital assistants, which are also called chatbots. For instance, Google assistant uses ML algorithms to translate the user’s speech to text, know about the context and provide relevant information.

Addition to the field of data science

Data science has been evolving since the early 200s and includes more technologies in this multidisciplinary field. Some recent additions include-

Artificial intelligence

One core component of data science is known to be machine learning. With the rapid parallel computing capabilities, the latest addition to the field of data science is known to be deep learning. Also, check the Best Data Science Institute in Bangalore with Placement to start a career in Data Science.

Intelligent systems

Data science has included data-driven smart apps and intelligent applications accessible in portable form. A significant portion of data science is centered around machine learning which is the basis of these intelligent systems and smart apps.

Edge computing

This computing concept is related to the Internet of Things and has been recently incorporated into this field. It includes putting data science pipeline of data processing, collection, and delivery. This recently developed concept is availed through IoT. Become a Data Scientist with 360DigiTMG Data Science Coaching in Hyderabad. Get trained by the alumni from IIT, IIM, and ISB.

Security

One main challenge in the field of digital space is security. Digital systems are more vulnerable to hacking and malware injection concepts. Data science has recently added technologies and advancements to prevent the vulnerability of the digital system. Machine learning algorithms can now detect malware and computer viruses more effectively than traditional ML algorithms.

The life cycle of data science

Project analysis – the first step of data science is to determine the needs of the project regarding availability, source, the budget of data

Data preparation: Here, raw data is converted to structured data and includes data visualization, data cleaning, transformation, etc.

Exploratory data analysis- data scientist uses different tools and programming languages to explore data and extract insights. It includes feature selection, visualization, model selection, and rapid prototyping.

Model building – this step model is developed using ML technologies and an ideal hyperparameter.

Deployment – after building the model, the next step is to deploy it in the real world in mobile or web apps.

Real-world testing and results-Model output is monitored to detect where the output is failing. The key findings are noted and informed to stakeholders.

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