Keyrus SA Mock Interview

Practice 28 Keyrus SA interview questions covering data analytics, digital transformation, and consulting expertise.

Question 28 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?

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Tom Dushaj is a business and technology executive and the author of 'Resumes That Work.' Tom has vast experience providing solutions to Fortune 500 companies in the areas of Information Technology Consulting, ERP Software, Personnel Management, and Intern

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.

Tom Dushaj

"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."

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28Keyrus SA Interview Questions & Answers

1.What questions do you have for me about Keyrus?

2.The way we approach clients in our business is we identify client needs and recommend solutions to their needs. Tell me about a time when your knowledge and expertise allowed you to make a recommendation to resolve a problem or address a pain point?

3.When dealing with data, in your opinion, is it better to have too many false positives, or too many false negatives? Please explain.

4.Describe to me your ideal employer.

5.What part of this career brings you the most stress?

6.Tell me about a time that you were assigned to manage a data analytics project. Walk me through the step-by-step process that you used to kick-off this project?

7.A client wants to implement a new software system that was recommended by a different division of the same company. How do you evaluate it to ensure it's the right choice?

8.Our clients have high expectations of our work, tell me about a time you worked with challenging time constraints, but were still able to exceed client expectations.

9.When designing a machine learning model, what in your opinion is more important, model performance or model accuracy, and why?

10.What are your salary expectations?

11.Have you ever broken a confidentiality agreement?

12.When you suffer a setback, how does that emotionally affect you and your work?

13.Your customer wants you to explain the benefits of the Big Data model you developed, how do you communicate the insights they can use for their business?

14.We're a company of innovative thinkers; we rely upon our innovative thinking to solve client problems. Tell me about a time when you came up with a breakthrough idea that was not obvious to others. Describe your idea and how you developed it?

15.Could you name a few data cleansing best practices that you follow, and how this has helped you become a better Data Consultant?

16.Describe a situation where a project you were managing failed. What did you learn about this failure, and were you able to salvage or turn it around?

17.Would you be willing to work over 40 hours a week?

18.How would your most recent manager describe you?

19.What is your knowledge of imputation? Would you kindly list different types of imputation techniques, and which method you find to be most favorable for your environment?

20.When have you had to change a major component of your project due to new information being presented?

21.What characteristics or events have contributed towards your success as a leader?

22.Big Data can be an efficient tool to monitor and grow a business, but can have challenges if not properly implemented. What challenges have you encountered while working with big data?

23.Tell me about a time where you made a great recommendation that you think would have greatly benefited your client, but they just didn't like it. What approach did you take to convince them?

24.Describe a time you helped implement a new technology for your client. Did you encounter any challenges, and how did you address them?

25.Help me understand your standards for success in your last job. Please describe what you did to attain those standards, and if you fell short of expectations, what did you do to remedy the problem?