MockQuestions

Keyrus Mock Interview

Question 2 of 28 for our Keyrus Mock Interview

Keyrus was updated by on February 28th, 2019. Learn more here.

Question 2 of 28

Explain what the difference is between Data Mining and Data Analytics, and tell me how you have used both in your recent projects?

"Data Mining and Data Analytics data is compiled by a number of different sources throughout a company. This data comes into the organization in many different forms from different formats, platforms, media, etc. It comes in different shapes, sizes, and venues like social media, social activity reports, customer surveys, emails, weblogs, sensors and bots related to the Internet of Things. I take this data and put it in the correct silos of a data warehouse, then break it down into data points that have relevant and usable data that the business can understand and use it to make business decisions. I was responsible for a data mining project that needed data to calculate operational expenses within the marketing group. They needed to know what it cost the Marketing department to run lead generation campaigns, and what the customer acquisition costs were."

Next Question

How to Answer: Explain what the difference is between Data Mining and Data Analytics, and tell me how you have used both in your recent projects?

Advice and answer examples written specifically for a Keyrus job interview.

  • 2. Explain what the difference is between Data Mining and Data Analytics, and tell me how you have used both in your recent projects?

      How to Answer

      Data Mining and Data Analytics go hand In hand in the world of big data. Data Mining is used to find patterns among large datasets, while Data Analytics is used to test hypothetical models on particular datasets. Over a period of time, you acquire skills that companies see as valuable to their organization. It's these skills that set you apart in the market. As you hone these skills over time, you become good at many different soft skills that accompany the technical skills. Here's where you can use a combination of these skills to articulate a compelling message that explains the differences between Data Mining and Data Analytics.

      A very important question that might come from an interviewer is how much data do you analyze, and how do you manage that data without risking a security breach. Large scale big data projects use tools like Hadoop as a repository for this data. The reason I mention it is the hiring manger might ask what repository you use to store it, and what your level of confidence using this tool is.

      Written by Tom Dushaj on February 27th, 2019

      1st Answer Example

      "Data Mining and Data Analytics data is compiled by a number of different sources throughout a company. This data comes into the organization in many different forms from different formats, platforms, media, etc. It comes in different shapes, sizes, and venues like social media, social activity reports, customer surveys, emails, weblogs, sensors and bots related to the Internet of Things. I take this data and put it in the correct silos of a data warehouse, then break it down into data points that have relevant and usable data that the business can understand and use it to make business decisions. I was responsible for a data mining project that needed data to calculate operational expenses within the marketing group. They needed to know what it cost the Marketing department to run lead generation campaigns, and what the customer acquisition costs were."

      Written by Tom Dushaj on February 27th, 2019

      2nd Answer Example

      "To me, comparing and contrasting two things like Data Mining and Data Analytics is an exercise that I enjoy a lot. I'm very passionate about data in general. Let me give you an example of these two, and how I used them in my current and past projects. Let's examine the difference between the two, and my experience using both. The Data Analytics process goes into the following steps: Project Definition, Data Collection, Data Analysis, Statistical Analysis, Data Modeling, and Deployment. The Data Mining process is a little different. The steps are Problem Definition, Data Collection, Data Analysis, Statistical Analysis, Data Modeling, Verification and Validation, and Insights. Now for my experience: In a recent project, I collected data from our websites to look at patterns in user behavior and website traffic for all our pages. This data was helpful to the Search Engine Optimization team so they could focus on redeveloping pages that were getting inquires and orders."

    • Next Question