--- title: "Role of data scientist in big data" subject: "Big Data Technologies" chapter: 1 key: "semester_8/big_data_technologies:role-of-data-scientist" source: https://www.bctengineer.com/q/semester_8/big_data_technologies/role-of-data-scientist site: BCT Engineering --- # Role of data scientist in big data Big Data Technologies · Chapter 1: Introduction to Big Data - Asked in: 2074 Bhadra, 4 marks; 2073 Bhadra, 5 marks (asked 2 times) - Video: [What is Data Science?](https://www.youtube.com/watch?v=RBSUwFGa6Fk) — IBM Technology - Answer checked by Claude Opus 5.5 (high effort) on 2026-10-14 - Page: https://www.bctengineer.com/q/semester_8/big_data_technologies/role-of-data-scientist - Whole chapter: https://www.bctengineer.com/md/semester_8/big_data_technologies/chapter-1 ## Question What is the role of a Data Scientist in Big Data? ## Short answer A data scientist extracts actionable insights from big data by defining problems, collecting and cleaning data, building predictive models, and communicating results. They combine statistics, programming, and domain knowledge, using tools like Hadoop, Spark, and NoSQL to scale analyses across clusters. ## Answer A **data scientist** is the person who turns raw, large and messy data into useful knowledge that a business can act on. The role combines statistics, programming and domain knowledge. #### Main roles and responsibilities 1. **Problem definition:** work with managers to convert a business problem (e.g. "why are customers leaving?") into a data problem. 2. **Data collection and acquisition:** gather data from logs, sensors, social media, databases and APIs, and decide which sources are useful. 3. **Data cleaning and preparation:** remove duplicates, fix missing values, convert formats and integrate sources. This often takes most of the project time. 4. **Exploration and analysis:** find patterns, correlations and outliers using statistics and visualisation. 5. **Model building:** apply machine learning (classification, clustering, regression, recommendation) to predict or classify. 6. **Using big data tools:** write jobs in Hadoop MapReduce, Spark, Hive or Pig, and query NoSQL stores, so the analysis scales over a cluster. 7. **Validation:** test the model for accuracy, precision, recall and bias before use. 8. **Communication:** present results through dashboards, charts and reports so non-technical people can decide. 9. **Deployment and monitoring:** put the model in production and update it as data changes. #### Skills required - Mathematics and statistics, probability - Programming (Python, R, Java, SQL) - Big data platforms (Hadoop, Spark, NoSQL) - Machine learning and data visualisation - Domain knowledge and communication #### Example In a telecom company, the data scientist collects call records and complaints, cleans them on Hadoop, builds a churn-prediction model, and gives the marketing team a list of customers likely to leave. ## Check yourself The answer's key points as multiple-choice questions, with the correct option and why. 1. What is the main job of a data scientist in big data? A. Maintaining the hardware of the Hadoop cluster B. Turning raw, large and messy data into knowledge a business can act on C. Only designing dashboards for managers D. Only storing data in HDFS Answer: B. The data scientist turns raw data into actionable knowledge by combining statistics, programming and domain knowledge. (Key point: Core purpose of the role) 2. Which activity often takes most of the time in a data science project? A. Presenting results in reports B. Writing the final deployment script C. Cleaning and preparing data (duplicates, missing values, formats, integration) D. Choosing the colour of charts Answer: C. Removing duplicates, fixing missing values, converting formats and integrating sources is usually the most time-consuming step. (Key point: Data cleaning and preparation) 3. Why does the data scientist validate a model before deploying it? A. To test accuracy, precision, recall and bias before it is used B. To make the data bigger C. To convert the data into HDFS blocks D. To remove the need for monitoring Answer: A. Validation checks model quality (accuracy, precision, recall, bias) so that wrong or biased models are not put into use. (Key point: Validation before use) ## Related in Big Data Technologies - [Why distributed computing for big data](https://www.bctengineer.com/question.md?key=semester_8%2Fbig_data_technologies%3Adistributed-computing-necessary) — Big Data Technologies, chapter 1 - [Distributed system in big data example](https://www.bctengineer.com/question.md?key=semester_8%2Fbig_data_technologies%3Adistributed-system-example) — Big Data Technologies, chapter 1 - [Big data trends, challenges, characteristics](https://www.bctengineer.com/question.md?key=semester_8%2Fbig_data_technologies%3Abig-data-trends-challenges) — Big Data Technologies, chapter 1 - [Hybrid data in big data warehouse](https://www.bctengineer.com/question.md?key=semester_8%2Fbig_data_technologies%3Ahybrid-data-warehouse) — Big Data Technologies, chapter 1 - [Attributes of Big Data](https://www.bctengineer.com/question.md?key=semester_8%2Fbig_data_technologies%3Abig-data-attributes) — Big Data Technologies, chapter 1 - [GFS master role in read/write](https://www.bctengineer.com/question.md?key=semester_8%2Fbig_data_technologies%3Agfs-master-read-write-flow) — Big Data Technologies, chapter 2 ## Related in other subjects - [Big data and its processing](https://www.bctengineer.com/question.md?key=semester_8%2Finformation_system%3Abig-data-processing) — Information System, chapter 8 - [Define Big O, Omega and Theta](https://www.bctengineer.com/question.md?key=semester_4%2Fdata_structure_and_algorithm%3Abig-o-omega-theta-define) — Data Structure and Algorithm, chapter 9, asked 4× - [Big-O and Big-Omega with curves](https://www.bctengineer.com/question.md?key=semester_4%2Fdata_structure_and_algorithm%3Abig-o-big-omega-curves) — Data Structure and Algorithm, chapter 9, asked 4× - [Big-Oh significance and limitation](https://www.bctengineer.com/question.md?key=semester_4%2Fdata_structure_and_algorithm%3Abig-oh-significance-limitation) — Data Structure and Algorithm, chapter 9