Applications of Big Data and Data Science

Data science is an interdisciplinary field that uses developments in computer science, statistics, and other fields to extract information from Big Data or data in general.

Data Processing Methods

Data analysts and scientists in big data analysis use different data management approaches. They consist of capture, curation, storage, search, and transfer.

• Capture: Describes the method by which data is gathered and put into a form that the analytical process may use.
• Curation: Data curation ensures the quality and accuracy of the data by undertaking a data cleaning activity. This procedure finds data inaccuracies, and any missing data is compensated for.
• Storage: Process of recording, archiving, and accessing data, as well as the fundamental structure of the underlying database:
• Search: Involves querying data to locate specific information. With big data, sophisticated techniques are necessary to efficiently retrieve the requested data content.
• Transfer: Describes the process of transferring data from the underlying data source or storage place to the underlying analytical instrument.

Data Visualization

Visualization encompasses data formatting, display, and summarization through graphical representations. Tables, charts, and trends are commonly used for traditional structured data, while non-traditional unstructured data demands innovative techniques like interactive three-dimensional (3D) graphics, tag clouds, and mind maps.

Fintech is applied in investment management, including text analytics, natural language processing, risk assessment, and algorithmic trading.

Text Analytics and Natural Language

Text analytics employs computer programs to analyze and extract insights, primarily from unstructured text- or voice-based datasets like company filings, written reports, quarterly earnings calls, and social media content. Text analytics can be utilized in predictive analysis to identify potential indicators of future performance, such as consumer sentiment.

Natural language processing (NLP) is an area of study that involves creating computer programs to decipher and analyze human language. Essentially, NLP combines computer science, AI, and linguistics.

Translation, speech recognition, text mining, sentiment analysis, and topic analysis are examples of automated tasks that use NLP. Annual reports, call transcripts, news articles, social media posts, and other text- and audio-based data may all be analyzed using natural language processing (NLP), allowing NLP to discover trends more quickly and accurately than is humanly possible.

Using natural language processing data, earnings projections for a company’s near-term prospects can be created. X (formerly Twitter) sentiments have also been used to gauge an initial public offering (IPO) success.

Python, R, and Excel VBA are frequently used programming languages, whereas SQL, SQLite, and NoSQL are prominent database systems.

Question

Which of the five data processing methods refers to the process of ensuring data quality and accuracy through a data cleaning exercise?

1. Data search.
2. Data storage.
3. Data curation.

The correct answer is C.

Data curation refers to the process of ensuring data quality and accuracy through a data cleaning exercise. It involves uncovering data errors and adjusting for missing data.

A is incorrect. Data search refers to how to query data. Big data requires advanced techniques to locate requested data content.

B is incorrect. Data storage refers to how the data will be recorded, archived, and accessed and the underlying database design.