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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

DataHack Radio #10: The Role of Computer Science in the Data Science World with Dr. Jeannette M. Wing

Pranav Dar
Pranav Dar Last Updated : 13 Jun, 2019
4 min read

Introduction

Have you noticed that the recent surge of data scientists have a background in computer science? It’s not a coincidence. These two domains are important in their own right but when merged together, they produce powerful results.

We are thrilled to announce the release of episode 10 of our DataHack Radio podcast with none other than Professor Jeannette M. Wing! She has over 4 decades of experience in academia and the industry, and there is no one better to give a perspective on how computer science has evolved, and how it meshes with the data science world.

I have briefly summarized the key takeaways from this episode below. I recommend listening to the podcast to truly get a feel for how computer science and data science are a powerful combination when used together. Enjoy this episode!

Subscribe to DataHack Radio NOW and listen to this, as well as all previous episodes, on any of the below platforms:

 

Professor Jeannette Wing’s Background

Professor Wing has always been fascinated by mathematics and engineering since her childhood. She went to graduation school at MIT and started majoring in electrical engineering there. During her initial days at the university, she was introduced to the world of computer science and that prompted her to change majors. And there was no looking back from that point on.

Post her days at MIT (where she also successfully completed her Ph.D in computer science), she worked at the University of Southern California for a couple of years before joining Carnegie Mellon University. She was the computer science department head twice at Carnegie Mellon. In between those two stints, she worked at the National Science Foundation (NSF).

During her second time as the department head at Carnegie Mellon, Microsoft approached her and she took up a role there in 2013. Within a year of joining, she was put in charge of all the basic research labs, including in Silicon Valley, New York, Bangalore, and Beijing, among others.

And then last year came Columbia University and a chance to work in academia again. At Columbia, she is the Avanessians Director of the Data Science Institute and Professor of Computer Science. She reports directly to the President of the University.

Using Formal Methods Techniques to Improve Machine Learning Algorithms

Although there has been decades of research done in computer science to formally show how one can prove how a program is correct, this is all with respect to mathematical logic. What data science is now bringing is the complexity for proving how a property is correct with respect to inherently probabilistic and statistical methods.

Professor Wing firmly believes that a lot of the new data science methods should be revisited by the formal methods techniques. Its a challenge for the formal methods community to help data science grow using these concepts, something which hasn’t yet happened.

In case you are not aware, formal methods are mathematics based techniques especially used in computer science. You can read more about them here.

 

Research Projects in Academia and Microsoft

Professor Wing, in her current role at Columbia University, is working with the AI community to understand what methods and logic are required to specify the relevant properties that these machine learned models should have. She feels this will help build safe and trustworthy AI systems for the future, a topic Professor Wing is a strong advocate of.

At Microsoft, she was overlooking several research projects in multiple locations as I mentioned above. The Bangalore lab, in particular, had a couple of big strengths:

  • Theoretical computer science, and
  • Technology for emerging markets

 

Difference between Working in Academia v Industry

“I’m really just an academic at heart.” – Professor Wing

A very common question from folks new to data science is – “what’s the difference between working in academia versus getting industry experience”? And Professor Wing was kind enough to cover this topic.

She echoes the wide-held belief that being a scholar has it’s own distinct advantages. You have more freedom to explore questions like why something works, rather than just focusing on how it works (which is what happens in most industry roles). The science part of both computer and data science comes from research and academia far more than the industry.

 

Where are Computer Science and Data Science Heading in the Next 5 Years?

  • Professor Wing feels AI, Big Data and Data Science are going to be much more pervasive in almost all sectors in the coming years
  • Quantum and biological systems are areas where we should be focusing on more
  • Cyber-physical systems like self-driving cars are going to an integral part of our lives
  • And of course addressing problems like bias and correcting algorithms keeping the society in mind is something the research community should be focusing on

 

End Notes

It was a privilege hosting Professor Wing on our podcast. Her explanation of formal methods and the important part they are playing in the software industry was a true delight to listen to. Fans of mathematics will surely love this episode.

Happy listening!

 

Computer Sciencedata sciencedata science podcastDataHack Radio
Pranav Dar
Pranav Dar

Senior Editor at Analytics Vidhya.Data visualization practitioner who loves reading and delving deeper into the data science and machine learning arts. Always looking for new ways to improve processes using ML and AI.

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