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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

29 Inspiring Women Blazing a Trail in the Data Science World

Pranav Dar
Pranav Dar Last Updated : 06 May, 2019
9 min read

This article has been updated on Women’s Day, 2019.

Introduction

This women’s day, we at Analytics Vidhya are celebrating the power of women in data science.

Women around the world are blazing a trail in the data science world and what better day to honor and appreciate them than today? There are a lot of women data scientists (but still not enough in our opinion) and leaders out there who are inspiring both women AND men.

Women excel at communication, nurturing a positive atmosphere in the team, problem solving, asking the right questions (among a whole host of other things!). These are all primary leadership qualities and we hope to see a lot more women in data science leadership roles in 2019.

Let’s take a look at the women who are doing it all and inspiring us to be a better version of ourselves every single day. I encourage you to follow them on social media and read more about their accomplishments.

And to all the women, Happy Women’s Day!

 

Emily Glassberg Sands

In her role as the Head of Data Science at Coursera, her mission is to build a better learning platform through data-drive decisions and products. She is a Ph.D in Economics from Harvard University. She has an impressive list of awards and honors she won while at Harvard. You can hear her on our DataHack Radio podcast here.

 

Carla Gentry

Carla is currently working as the Digital Marketing Manager at Samtec Inc. She has over 20 years worth of experience working for companies like Johnson & Johnson, Hershey, Kraft, among others. She is a very popular and active social media user and her posts are always worth reading for their invaluable knowledge. Carla was one of the earliest guests on our DataHack Radio podcast.

 

Monica Rogati

Monica is an independent data science and AI advisor. She helps companies make the most of their data – both technically and strategically. She has previously worked as the VP of Data at Jawbone, a Senior Data Scientist at LinkedIn and is also currently a guest lecturer at Stanford University.

 

Yael Garten

Yael is the Director of Siri Analytics, Evaluation and Data Engineering at Apple where her mission is to improve Siri by using Data as the voice of their customers. Previously, she has worked as a Director of Data Science at LinkedIn. She also works as an advisor at various startups and is a leading voice at various conferences.

 

Sarah Nooravi

Sarah is currently working as the Senior Data Scientist at Operam. She is one of the most popular and helpful personalities on LinkedIn. Sarah regularly posts content aimed at helping aspiring data scientists break into the field and is always available for helpful and insightful advice.

 

Cassie Kozyrkov

Cassie Kozyrkov

Cassie is the Chief Decision Scientist at Google. She is a well-known speaker in the data science sphere, and often pens down her thoughts in articulate fashion in this field. In her DataHack Radio podcast episode, she takes us on a journey into her life at Google and how she went from being a Statistician at Google to her current role.

 

Kate Strachnyi

Kate Strachnyi ♕

Kate is a data visualization master and a leading data science voice on LinkedIn. She has inspired countless aspiring data science professionals to take up storytelling with data. I’m a huge fan of her Tableau skills and her willingness to give back to the community through her knowledge. Make sure you check out her course – Tableau Visual Best Practices: Go for Good to GREAT!

 

Dr. Jeannette Wing

Jeannette Wing

Dr. Jeannette Wing is the Avanessians Director of the Data Science Institute and Professor of Computer Science at Columbia University. 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. And that’s we talk to her about in this DataHack Radio episode!

 

Kristen Kehrer

Kristen Kehrer

Kristen is a data scientist with 9 years’ experience who delivers innovative and actionable machine learning solutions in business. Like Kate, she is a leading data science voice on LinkedIn and is always sharing her rich knowledge with the community. She has interviewed numerous data scientists over the years and has drawn on her experience to create the ‘Up-Level your Data Science Resume‘ course.

 

Vivian Zhang

Vivian is the CTO and Chief Data Scientist at the popular NYC Data Science Academy. She has almost a decade’s worth of experience in the field of research and data science. She was featured in the list of “9 Women Leading the Pack in Data Analytics” by Forbes in August 2016.

 

Rachel Thomas

The co-founder of fast.ai, Rachel was selected as one of the “20 Incredible Women in AI” by Forbes. Fast.ai has created courses taken by over 100,000 students from around the world. She’s also a very popular writer and keynote speaker and her articles have been translated into various other languages.

 

Dr. Elena Grewal

Dr. Elena is the head of Data Science at Airbnb. She is leading a team of 100+ data scientists. She did her graduation in economics and political science from Yale and spent a year in India working on a project to bring clean water to children who had acute diarrhoea.

 

Jana Eggers

Jana is the CEO of Nara Logics, an AI company focused on turning big data into smart actions. She has over 25 years experience in the business industry and is an active speaker, and contributor at various AI startups.

 

Caitlin Smallwood

Caitlin is the Vice President of Science and Analytics at Netflix. Her team develops new models and algorithms that directly improve the Netflix service. At Netflix, she was previously leading a team of mathematicians, statisticians and data scientists who worked on algorithms research, development, predictive modeling to bring to you a seamless experience while using Netflix. She is one of the leading figures in the data science community.

 

Daphne Koller

Daphne is co-founder of the ultra popular online learning platform Coursera. She has also part of the Stanford University faculty for almost 19 years. Her research areas include AI and it’s applications in biomedical sciences. She has been felicitated with various awards & accolades which include ACM Infosys Awards, MacArthur Foundation Fellowship and many more.

 

Professor Melanie Mitchell

Melanie Mitchell

Melanie Mitchell is Professor of Computer Science at Portland State University and the author of multiple books on artificial intelligence. She has over three decades of experience in academia, computer science and artificial intelligence. She spoke to us about artificial general intelligence (AGI) and the challenges we face to get there.

 

Fei-Fei Le

Image result for fei fei li

Fei-Fei Li is a Professor of Computer Science at Stanford University. She is truly one of the most influential people in technology and data science. She has previously worked as the Chief Scientist of Artificial Intelligence and Machine Learning at Google Cloud. Check out her brilliant TED Talk on Computer Vision.

 

Anima Anandkumar

Anima Anandkumar

Anima Anandkumar is the Director of ML Research at NVIDIA and a Bren Professor at Caltech. She has spearheaded research in tensor-algebraic methods, non-convex optimization, probabilistic models and deep learning.

 

Women in the AV Community

In this section, we celebrate some of the women who are regular contributors to our AV community.

Pavleen Kaur

Pavleen is a data visualization wizard. Her Tableau series, from beginners to advanced, has been incredibly well received in the community. She is a final year undergrad and a major data science enthusiast. The future of data science is very bright with her!

 

Shilpi Bhabhra

speaker-info

Shilpi leads Retail and Marketplace analytics practice at Flipkart. Her expertise is in driving data-based decisions for product and business. During her 14 years of work experience, she has led several data sciences projects for implementation in the field of Retail and Marketing.

 

Parul Pandey

Parul Pandey

Parul is one of the best writers you’ll come across in the machine learning and artificial intelligence space. She writes comprehensive articles and breaks complex concepts into easy-to-digest information. One of the most approachable and knowledgeable personalities to follow on LinkedIn for data science.

 

Divya Choudhary

speaker-info

Divya Choudhary is a data scientist, currently pursuing her MS in Data Science from the University of Souther California. With 4 years of work experience before starting her Master’s, Divya is a computer science engineer who has traversed her professional career from being an analyst to a decision scientist to a data scientist.

 

Srishti Gupta

speaker-info

Srishti works on training deep learning models and building end to end ML pipelines to deploy them at Hike for a variety of data science problems such as recommendations, image stylisation, growth and more. Srishti has a Masters in Machine Learning from Georgia Tech and used to work at Apple before joining Hike in her current role.

 

Mathangi Sri

speaker-info

Mathangi is the Head of Data Science at PhonePe. She has 15+ years of proven track record in building world-class data science solutions and products. She has extensively worked on building chatbots and productizing text mining insights. She has 5 Patent grants and 20+ patents pending in the area of intuitive customer service, indoor positioning and user profiles.

 

Prathrna Bhat

Prarthana Bhat is a Data Scientist at Flutura Desicion Science and Analytics. Prarthana is skilled in SQL, R and Business Intelligence. She is an active contributor on Analytics Vidhya and was the first female data scientist to secure a rank in top 3 on Analytics Vidhya. She has over 5 years of experience in the data industry.

 

Anchal Gupta

Anchal is a Data Scientist at Paisabazaar.com. She is a regular participant in the Analytics Vidhya hackathons and can often be found contributing to AV’s Slack channel.

 

Preeti Agarwal

Preeti is the Assistant Vice President at JP Morgan Chase & Co.. She is also a Analytics Vidhya Data Science Volunteer in Mumbai. She has 11+ years of experience in Telecom & Banking domain. She loves data science and wants to spread her knowledge to the world. Preeti is a graduate from Nagpur University.

 

Tanvi Purohit

Tanvi Purohit is working as a Consultant with Deloitte. She is skilled in RDBMS and passionate about the field of data science. She has a working experience of over 5 years, out of which 4 have been spent working with data. She has worked in data warehousing and data analytics, and is a trained strategist. Tanvi is also a AV Volunteer in Mumbai.

 

Aishwarya Singh

Aishwarya Singh

Aishwarya joined Analytics Vidhya as an intern and has bloomed into her data science role. She loves reading and learning, and that dovetails nicely into her daily role as a data science professional. Aishwarya is also an excellent writer and has penned down plenty of articles on various data science techniques, primarily focusing on time series analysis.

And finally, please note that this is by no means an exhaustive list. There are so many wonderful women leading the way in data science and if you know of them, please post their names and links in the comments below.

 

Learn, compete, hack and get hired.

 

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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Responses From Readers

Clear

Madhur Modi
Madhur Modi

Cheers to all the women out there leading the world with their skills and ability!

123

Jagannadham Boddapati
Jagannadham Boddapati

Great article about leading personalities in data world.

123

Junru Li
Junru Li

Thank you for the sharing of these brilliant women in data science field, really inspiring! One comment: Would you kindly change Ms. Li Feifei's name in title from "Fei-Fei Le" to "Fei-Fei Li"? Thank you!

123

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