Learning Objective

This lesson only covers Big Data. At the end of the lesson, students should be able to

1. Describe briefly about Big Data 

2. Explain how big data is implemented in organizations.

3. Describe the potential careers in Big Data

Data Analysis vs Data Science

Read Clarifying Differences between Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data article by Pooja Bisht. The article gives you an understanding on the difference between Data Analysis, Data Mining and Data Science. 

Bisht (2019) stated that Data Analysis as 

The process of sourcing, cleaning, transforming and analyzing data to find out the meaningful pieces of information or insights out of big datasets which are useful to answer the big business questions

Data mining is 

The process of finding or extracting useful information out of the large datasets. 

So, how do we compare and contrast data analysis and data mining? For one thing both is about looking into big datasets and come up something useful out of that. The difference is that the term data mining is much narrower as compared to data analysis. Data mining is about extracting information from the data sets by looking at underlying patterns in those data sets. Knowledge emerges for the data mining because previously we do not know anything from these huge data sets. Another difference is that in data mining, the data is already cleaned. Data analysis is much bigger. The data is not clean so you got to clean it first. You analyze patterns from the data sets (like data mining) and from that, you find solutions to business problems. Data mining is about discovering new knowledge from the data sets. Data analysis goes beyond that.

Data Science is 

Data Science is a broader field using various algorithms and processes to extract meaningful insights out of the unstructured and structured data. 

Data science involves the use of algorithms and processes to extract meaning insights. It goes beyond data mining and data analysis in a way. Predictions based on current and historical data are made possible in data science through the adoption of machine learning. 

Below are the skill set required for data scientists, big data professional and data analysts. As an Information Systems graduate, data analyst is definitely a career option. Don't worry about tools, if you have the interest, you can learn them. You can even learn them on the job 

In the article, Data Scientist vs Business Analyst – 5 Core Aspects to Choose Your Career, the term Business Analyst is more or less similar to data analyst in the first article. Business analysts look into the analysis of data which provides insights to business operations. Business analysts would use statistical analysis technique to investigate the performance of a business (analyst, right?). Thus it is crucial that business analysts to understand business and be able to talk business language with the business team. Business analysts is suited for information system graduate. 

The image below shows the difference between data scientist and business analyst. 

Activity

1. Discuss with a colleague on you understanding on the difference between data analysts and data scientist.

2. Find a data analyst and a data scientist on Linkedin. What are the similarities and differences that you can see. 

Next: Tools in Big Data