Data Science | Components of Data Science | Application of Data science

Data Science

• The fundamental concepts of data science are drawn from many fields that study data analytics.
• Fundamental concepts: Extracting useful knowledge from data to solve business problems can be treated systematically.
• Data scientists pay active roles in the design and implementation work of four related areas: data architecture. Data acquisition, data analysis, and data archiving.
• Key skills highlighted by the brief case study include communication skills, data analysis skills, and ethical reasoning skills.

 

Components of Data Science:

1.  Statistics:

• Statistics is one of the most important components of data science.
• Statistics is a way to collect and analyze the numerical data in a large amount and finding meaningful insights form it.

 

2. Visualization:

• Data visualization is meant by representing data in a visual context so that people can easily understand the significance of data.
• Data visualization makes it easy to access the huge amount of data visuals.

 

3. Data engineering:

• Data engineering is a part science, which involves acquiring, storing, retrieving, and transforming the data.
• Data engineering also includes metadata data about data to the data.

 

4. Advanced computing:

• Heavy lifting of data science is advanced computing.
• Advanced computing involves designing, writing, debugging, and maintaining the source code of computer programs.
• 5. Machine learning:
• Machine learning is all about to provide training to a machine so that it can act as human brain. In data science, we use various machine learning algorithms to solve the problems.

 

Advantage of Data Science:

• Data science helps organizations knowing how and when their products sell best and that’s why the products are delivered always to the right place and right time.
• Faster and better decisions are takes by the organization to improve efficiency and earn higher profits.
• It helps the marketing and sales term of organization in understanding by refining and identifying the target audience.
• It has made it comparatively easier to sort data and look for best of candidates for an organization. Big data and data mining have made processing and selection of CVs, aptitude tests and games easier for the recruitment teams.

 

Disadvantages of Data Science:

• Extracted information form the structured as well as unstructured data for further use can also misused against a group of people of a county or some committee.
• Tools used for the data science and analytics are more expensive to use to obtain information. The tools are also more complex, so people have to learn how to use them.

 

Application of Data science:

• Fraud and Risk detection
• Healthcare.
• Virtual assistance for patients and customer support.
• Internet Search
• Targeted Advertising.
• Website Recommendations
• Advanced Image Recognition
• Speech Recognition.
• Airline Route Planning
• Gaming
• Augmented Reality.