Journeying Through Google’s Analytics and Data Science Domain
Introduction
Meet Rishabh Dhingra, an accomplished professional excelling in Analytics and Data Science at Google. Rishabh possesses extensive expertise and a passion for utilizing data effectively. He drives innovation through advanced technologies, extracting valuable insights and revolutionizing data-driven decision-making. Rishabh’s journey at Google has been remarkable, transforming the Analytics and Data Science domain. Let’s explore his achievements and contributions that have propelled Google’s success to new heights.
Let’s Learn from Rishabh!
AV: Can you share your journey to becoming a data scientist at Google? What steps did you take to get where you are today?
Mr. Rishabh: I started my career as a BI Consultant with Thorogood Associates in 2011 and have worked in Data Space since then. So learning languages like SQL, Python, data modeling, presentation skills, and tools like Tableau are the initial required steps in the journey. And then, some people start by going deep into math and theory and doing some projects. But I feel doing it and then understanding the concepts as I apply work the best. Some key steps that helped me:
- Taking incredible courses on platforms like Analytics Vidhya
- Identifying opportunities in your role where you can apply Data Science skills
- Doing Projects on something you are passionate about
- Working closely with the business and learning about the business
- Sharing my knowledge with others as it helps me understand the concepts better
- Networking and learning from others
- Gaining skills in Google Cloud technologies
Skills for Aspiring Data Scientists
AV: As a successful data scientist, what skills are most important for aspiring data scientists? How did you develop these skills?
Mr. Rishabh: As a successful data scientist, I believe that the most important skills for aspiring data scientists to have are:
- Technical Skills: This includes a strong mathematics, statistics, and programming foundation. Data scientists need to be able to collect, clean, analyze, and visualize data. They also need to be familiar with machine learning and deep learning techniques.
- Problem-solving Skills: Data scientists need to be able to identify and solve problems using data. They need to think critically and creatively and come up with new and innovative solutions.
- Communication Skills: Data scientists need to be able to communicate their findings to both technical and non-technical audiences. They need to be able to explain complex concepts clearly and concisely.
- Teamwork Skills: Data scientists often work on projects with other data scientists, engineers, and business professionals. They need to collaborate effectively and work towards a common goal.
I developed these skills by taking courses, working on personal projects, networking with other data scientists, and learning from their experiences.
Aspiring Data Scientists Should Avoid Mistakes
AV: What should aspiring data scientists should focus on developing? What mistakes should they avoid?
Mr. Rishabh: I think these are mistakes the data scientists should avoid:
- Not understanding the business problem. Data scientists need to understand the business problem they are trying to solve before they can start working on the data. This includes understanding the business’s goals, the available data, and the data’s limitations.
- Not cleaning the data. Dirty data can lead to inaccurate and misleading results. Data scientists need to take the time to clean the data before they start working with it. This includes removing errors, outliers, and missing values.
- Using the wrong tools. There are many different tools available for data science. Data scientists need to choose the right tools for the job. This includes considering the data’s size and complexity, the project’s goals, and the budget.
- Not communicating the results. Data scientists must be able to communicate the results of their work to both technical and non-technical audiences. This includes explaining the methods used, the results obtained, and the limitations of the analysis.
AV: Which projects should students pursue to strengthen their understanding of concepts?
Mr. Rishabh: My suggestion is to take two types of projects – one that aligns with your business that you work closely with – this could be taking on stretch projects within your job and trying to add value to the business and would also help you learn on the job and make an impact. And the second type of project would be your passion project. For example – if you are into sports, pick a dataset related to it, build your hypothesis, and do a project on it.
Rishabh’s Journey
AV: What unique challenges did you face as a Manager of Data Science & Analytics at Home Depot, and how did you overcome them?
Mr. Rishabh: I really enjoyed my time at Home Depot Canada and was fortunate to be exposed to various data science challenges. One of the learning experiences that is very underrated, in my opinion, is defining the business problem and success metrics of data science projects, and getting alignment with all the stakeholders is very critical for the project’s success. This would guide everyone before jumping into solutions to the problem and building things, analyzing the business problem, and defining the success.
AV: If you could choose any Google product to have an unlimited supply for the rest of your life, what would it be and why?
Mr. Rishabh: Youtube – I go to Youtube to learn anything and find answers to all my “How To” questions. It has so much content and info available for us to learn new skills – ML/AI or how to cook ‘Biryani’ – it’s all available on Youtube.
AV: What are some of your favorite hobbies or interests outside of work? How do you balance your professional life with your pursuits?
Mr. Rishabh: I engage myself in a lot of things outside work – listening to podcasts and running my podcast ‘Inspired’, playing sports, especially cricket, being an instructor on data analytics and data science, mentoring new immigrants in Canada, reading books, running my side hustle business of home decor. Balancing all this with professional life sometimes becomes difficult, but that makes life interesting and keeps me going.
Short-term and Long-term Analytics Initiatives
AV: How did you balance the need for short-term and long-term analytics initiatives as Manager of Data Analytics & Insights at TD Insurance?
Mr. Rishabh: As a leader, you need to have both a long-term vision and short-term wins that would help the business. You need to be very clear and communicate the long-term vision of the analytics journey to the stakeholders and your team so everyone is clear on how the future will look and what steps we need to accomplish to reach it. But you need to also seize the moments in the short run where you can impact the business using analytics. However, your short-term decisions must align with your long-term vision. I suggest identifying and going for quick wins to make an impact that aligns with the long-term vision.
AV: How important are continuous learning and upskilling in data science? How do you keep yourself updated with the latest developments and technologies in the industry?
Mr. Rishabh: The field of data science is constantly changing, with new technologies and techniques emerging all the time. Data scientists must constantly learn and upskill to stay ahead of the curve. Some ways I keep myself updated on the latest developments in the industry are:
- Listening to various podcasts
- Take new courses
- Personal Projects
- Networking
Future Forecast
AV: Where do you see the future of data science heading in the next 5-10 years? What goals do you hope to achieve in this field during that time?
Mr. Rishabh: I think the future will be AI; you will see AI embedded in every aspect of our life. So, there will be a lot of demand for AI developers/engineers. New machine learning and AI techniques will be developed to solve real-world problems and make us more productive. Like we see how Generative AI is making us more productive these days. You must have seen the announcements that Google made at I/O 2023 event on the great AI features coming to Google products and how they will make us more productive. I also think open-source data science tools and libraries will continuously grow. My goals in this field would be to find real-world problems where we can apply the new ML/AI techniques and educate others about my learnings, and I would ideally want to get into Product Management in ML/AI.
AV: What advice do you have for companies looking to implement a business intelligence and analytics solution like Tableau, and what are some common mistakes to avoid during the implementation process?
Mr. Rishabh: Below are the things I would suggest for companies looking to implement a BI and Analytics solution like Tableau:
- Define your goals and objectives: What do you wish to achieve with BI & Analytics solution? How will this help you and the business? What are your success criteria?
- Assess your current landscape: What data do you have available? How is it stored? How is it structured? How does the BI & Analytics solution fit into your current technology landscape? Does this align with your long-term vision of the overall technology landscape?
- Run PoCs to evaluate different solutions and choose the right solution: It’s important to choose a solution that is right for your needs – Run PoC and evaluate different tools on various use cases critical to your business. Consider factors such as your budget, goals, and technical expertise.
- Get buy-in from stakeholders. BI and analytics solutions are not just for IT. They need to be used by people across the organization. Make sure you get buy-in from stakeholders across the organization before you start to implement a solution.
- Monitor and evaluate your results. Once you use a BI and analytics solution, you must monitor and evaluate your results. This will help you see if the solution meets your goals and objectives.
Resources Recommendation
People who are looking for an entry/ transition in Data Science
Books
- Data Science from Scratch
- Python for Data Analysis
- 100 Page Machine Learning Book
- Practical Statistics for Data Scientists
- Machine Learning for Absolute Beginners
Courses
Applied Machine Learning – Beginner to Professional by Analytics Vidhya
Podcasts
- SuperDataScience
- Inspired
- DataSkeptic
Resources for professionals to stay relevant on industry updates
Newsletters
- TechCrunch
- TLDR
Podcasts
- Bloomberg Technology
- TechCrunch
- ALL-IN
- Lex Fridman
- WIRED Business
- The Week in Startups
Specific Resources for Tableau/ Power BI/ languages – python/SQL
Books
- Storytelling with Data
- Automate the Boring Stuff with Python
- Python for Data Analysis
- Big Book of Dashboards
- Data Modelling with Tableau
Website
Resources, in general, to stay motivated/ develop thought leadership qualities, etc.
Books
Podcast
- On Purpose with Jay Shetty
Conclusion
In conclusion, Rishabh Dhingra is a true exemplar in the Analytics and Data Science domain, leaving an indelible mark on Google’s groundbreaking work. His exceptional skills, unwavering dedication, and remarkable ability to provide insightful guidance make him a valuable resource for those entering or transitioning into the data science industry. Rishabh’s commitment to sharing knowledge and empowering freshers with invaluable insights in analytics and data science ensures that the next generation of data scientists will have the tools and inspiration to succeed. As Rishabh Dhingra continues to revolutionize the field, his impact on both Google and the broader data science community is a testament to the boundless possibilities ahead in this dynamic and ever-evolving industry.
