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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Journeying Through Google’s Analytics and Data Science Domain

Sakshi Raheja
Sakshi Raheja Last Updated : 28 Jul, 2023
7 min read

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.

Analytics and Data Science

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.

Tableau

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

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

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.

data science skillsPower BIpythonSQLsuccess storytableau
Sakshi Raheja
Sakshi Raheja

I am a passionate writer and avid reader who finds joy in weaving stories through the lens of data analytics and visualization. With a knack for blending creativity with numbers, I transform complex datasets into compelling narratives. Whether it's writing insightful blogs or crafting visual stories from data, I navigate both worlds with ease and enthusiasm. 

A lover of both chai and coffee, I believe the right brew sparks creativity and sharpens focus—fueling my journey in the ever-evolving field of analytics. For me, every dataset holds a story, and I am always on a quest to uncover it.

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