His skill set includes Python, R, SPSS, SAS EG, Clustering, Market-Basket, SPADE, Regression, Decision Tree, Random Forest, SVM, Time Series, NLP, Text Mining, Neural Network, Deep Learning, TensorFlow, Keras, SQL, MS Excel, MS PowerPoint, Tableau, and COGNOS. His areas of expertise include Data Science, Machine Learning, Artificial Intelligence, Deep Learning, Customer & Marketing Analytics, Customer Life Cycle Management, Sentiment Analysis, Pricing, Text/Image Analysis, Business Consulting and Strategy, Project Management, and Team Mentoring.
He is a leader with global client exposure across the UK, Ireland, UAE, KSA, India, and South Africa. He is well-known for his work in retail, telecom, manufacturing, insurance, and energy utility domain.
What was the first data set you remember working with? What did you do with it?
Table of Contents
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- What was the first data set you remember working with? What did you do with it?
- Was there a specific “aha” moment when you realized the power of data?
- How do you stay updated on the latest trends in Data Analytics? Which are the Data Analytics resources (i.e. blogs/websites/apps) you visit regularly?
- Share the names of 3 people/publications/research that you follow in the field of Data Science or Big Data Analytics.
- Team, Skills, and Tools
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- Which are your favorite Data Analytics Tools that you use to perform in your job, and what are the other tools used widely in your team?
- What are the different roles and skills within your data team?
- Help describe some examples of the kind of problems your team is solving this year?
- How do you measure the performance of your team?
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- Industry Readiness for Data Science
- Advice to Aspiring Data Scientists
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- According to you, what are the top skills, both technical and soft-skills that are needed for Data Analysts and Data Scientists?
- How much focus should aspiring data practitioners do in working with messy, noisy data? What are the other areas that they must build their expertise in?
- What is your advice for newbies, Data Science students or practitioners who are looking at building a career in Data Analytics industry?
- What are the changing trends that you foresee in the field of Data Science and what do you recommend the current crop of data analysts do to keep pace?
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Vaibhav Verdhan: I remember that I was analyzing a pre-paid subscriber base for a telecom operator. The analysis was to identify patterns in the average call duration of customers wrt to their revenue. I identified the relationships so that we can improve realized revenue from the subscribers.
Was there a specific “aha” moment when you realized the power of data?
Vaibhav Verdhan: Yes, multiple times. Most recently, Artificial Intelligence is allowing us to improve product quality and make it better.
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How do you stay updated on the latest trends in Data Analytics? Which are the Data Analytics resources (i.e. blogs/websites/apps) you visit regularly?
Vaibhav Verdhan: Get knowledge from wherever you can. Videos, podcasts, blogs, articles help in it. And refresh your knowledge by taking online courses/certificates. I generally follow Kdnuggest, datasciencecentral, analyticsvidhya, Kaggle.
Vaibhav Verdhan: Prof Andrew Ng, Prof Fei-Fei Li
Team, Skills, and Tools
Which are your favorite Data Analytics Tools that you use to perform in your job, and what are the other tools used widely in your team?
Vaibhav Verdhan: Excel, SQL, R, Python, Tableau are the most widely used tools
What are the different roles and skills within your data team?
Vaibhav Verdhan: Data Analysts, Data Engineers, Visualization Expert, and Data Scientists.
Help describe some examples of the kind of problems your team is solving this year?
Vaibhav Verdhan: Using AI to solve Image Classification problems. Using ML to help increase productivity.
How do you measure the performance of your team?
Vaibhav Verdhan: The insights we are sharing with the business, how much of it is creating an impact on the life of the end-customer.
Industry Readiness for Data Science
Are the industries looking to understand what they can do with data? Do they have the required data in place?
Vaibhav Verdhan: Yes, multiple industries are cognizant of the power of data now. Telecom, retail, BFSI, aviation, manufacturing, etc. are taking a real interest in data. The data exists but currently, a lot of it (particularly unstructured) is yet to be analyzed. And a push is required for the IT infrastructure too.
Which are the top 3 problems that are on top of the Data Science, either based on industries or based on technology area?
Vaibhav Verdhan: We face data availability as the first challenge. It is followed by data quality and finally the availability of resources like server space, computation power, etc.
Advice to Aspiring Data Scientists
According to you, what are the top skills, both technical and soft-skills that are needed for Data Analysts and Data Scientists?
Vaibhav Verdhan: Coding (Python/R and SQL). Statistical concepts and ML basics. Soft skills will be communication skills, collaborative and storytelling
How much focus should aspiring data practitioners do in working with messy, noisy data? What are the other areas that they must build their expertise in?
Vaibhav Verdhan: Messy data is a truth and we have to face it every day. We spend 60%-70% on it. Aspiring data practitioners should know how to deal with missing values, outliers, NAs, imbalanced datasets, etc.
What is your advice for newbies, Data Science students or practitioners who are looking at building a career in Data Analytics industry?
Vaibhav Verdhan:
1)Programming and software skills – R/Python, SQL, and Excel
2)Visualization Tools: Tableau/ Qlik/ Power BI/ Cognos
3)Statistical foundation and applied knowledge: on-going but at least basics at the start
4)Machine Learning: at least basics of Supervised and Unsupervised learning
What are the changing trends that you foresee in the field of Data Science and what do you recommend the current crop of data analysts do to keep pace?
Vaibhav Verdhan: Open-source AI libraries like TensorFlow, Keras is giving us Deep Learning power. With increase computation power and using Google Colab, we can train our own Neural Networks too. The current generation should study and keep abreast of the latest happenings but always stick to the basics.
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