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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

Women Leaders in Data Science: Top Influentials from the Industry

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Analytics Vidhya Last Updated : 02 Apr, 2022
7 min read

Introduction

The thriving industry of Data Science is continuously evolving with the technological advancements in Machine Learning and Artificial intelligence. This has opened up whole new avenues for Data Scientists worldwide. Professionals who can handle Big Data and have the necessary knowledge required for understanding, analysing and processing data are in high demand in the job market. However, there is one important thing that also needs to be addressed is the raging problem caused by the gender gap in this sector. 

Women Leaders in Data Science

As per the statistical report from the Boston Consulting Group, only 15 to 22 per cent of the Data Science-related professional roles are occupied by women. Women in the professional work sectors often get demotivated due to sometimes being subjected to gender bias that leads to lesser pay and little to no growth opportunities. However, despite all odds, quite a few notable women have excelled in this field and have become the role models that the female population needs to look up to. These incredible women have broken barriers and have worked their way to the top of the ladder in this field of Data Science. 

Their hard work, drive to survive in a male-dominated industry, as well as their journey to the pinnacle of their success in Data Science, has motivated young women and students all over the world to not shirk away from their passions out of fear, but to take up the challenge and emerge victoriously. 

Let us have a look at these amazing women who have paved an easier way for women to pursue this field. 

Women Leaders in Data Science

Daphne Koller 

Daphne Koller is a Machine Learning pioneer. She is the founder and CEO of Insitro and trying to bridge the gap between biomedicine and machine learning. At insitro, she aims to address both these challenges and help address an important societal problem: design novel, safe, and effective therapies that help more people, faster, and at a lower cost.

Women Leaders in Data Science

Anima Anandkumar 

Animashree (Anima) Anandkumar is a Bren professor at the Caltech CMS department and a director of machine learning research at NVIDIA. Her research spans both theoretical and practical aspects of large-scale machine learning. In particular, she has spearheaded research in unsupervised AI, deep learning, optimization and tensor methods. Previously, she was a principal scientist at Amazon Web Service, where she has enabled machine learning on the cloud infrastructure.

Women Leaders in Data Science

Prof. Maria Fasli

Prof. Maria Fasli is the first UNESCO Chair in Data Science and analytics and also the director of the Institute for Analytics and Data Science at the University of Essex. As a female professional in the Data Science field, she emphasises the importance of data for advocating equality and sustainable development. 

Women Leaders in Data Science

Emily Glassberg Sands

Emily is the Data Science Head at Coursera. She aims to build a better learning platform by incorporating data-driven products and decisions. She has a PhD from Harvard University in Economics and has received numerous impressive awards and honours from Harvard. She runs her own podcast called DataHack Radio.Women Leaders in Data Science

Cathy O’Neil

Cathy O’Neil is a professor, mathematician, Data Scientist as well as a hedge-fund analyst. She founded ORCAA, which is an algorithmic auditing company and she also wrote the book ‘Weapons of Math Destruction’ and articles in The Guardian, Bloomberg. She is also a TED talk speaker where she addresses the dangers and the challenges faced by big data.Monica Rogati

Monica Rogati

Monica Rogati was the previous VP of Data at Jawbone and a Senior Data Scientist at LinkedIn. She is an independent AI and Data Science advisor now and aims to help companies both strategically and technically benefit the most from their data. She is also working currently at Stanford University as a guest lecturer.

Danai Koutra

Danai Koutra received the 2020 Association for Computing Machinery’s SIGKDD Rising Star Award. She has been promoted to Associate professor at the University of Michigan and enjoys teaching Computer Science and Engineering. Her research on large-scale data mining about scalable and interpretable methods for multi-network analysis and network summarisation has garnered considerable recognition.  She is also an Associate Director for the Michigan Institute for Data Science and an Amazon Scholar. 

Kate Strachnyi | Women Leaders in Data Science

Kate Strachnyi

Kate is the founder of DATAcated and she has been focussing on everything and anything related to Data Visualisation. She has authored 4 books on different topics related to Data Visualisation. So, yes! She is your go-to person when you wish to excel and become a pro on Data Visualisation.

Women Leaders in Data Science

Medb Corcoran

Medb Corcoran is currently leading Accenture Labs in Ireland, where she is responsible for the centre’s artificial intelligence R&D and also for creating new concepts and prototypes with the help of applied R&D projects. She is also a member of the advisory board of the master’s in business analytics in UCD where she helps in shaping the new bachelor’s degree in Data Science at DCU.Mathangi Sri | Women Leaders in Data Science

Mathangi Sri 

Mathangi has 17+ years of proven track record in building world-class data science solutions and products. She has overall 20 patent grants in the area of intuitive customer experience and user profiles. She has recently published a book with Apress, Springer – “Practical Natural Language Processing with Python”. She is currently working as CDO at CredAvenue.

Rachel Thomas | Women Leader in Data Science

Rachel Thomas 

Rachel is a co-founder of fast.ai and a professor of practice at the Queensland University of Technology. She has contributed to the Data Science Industry immensely by developing world-class content and courses on Deep Learning.

Sarah Nooravi

Sarah Nooravi

Sarah Nooravi is a Senior Data Scientist at Operam. She has garnered quite a big name as a personality in LinkedIn as a person popular for helping people out when it comes to the field of Data Science. Sarah is a regular LinkedIn user where she very often posts content for helping budding Data Scientists and enthusiasts of Data Science on how to make it big in this field. She is a warm and welcoming person who is always available for providing any advice or insights and answer questions.

Sharvari Gujja

Sharvari Gujja

Sharvari Gujja is the current director of bioinformatics at the advanced artificial intelligence laboratory in Genuity Science. Having been a bioinformatician before, she has worked her way to the top by making significant contributions to numerous high-grade scientific publications, in the research of Cell Stem Cell, Nature Metabolism and many more. She and her team at Genuity Science focus on the application of domain-specific and proprietary  AI algorithms for revealing causal dependencies and novel patterns for understanding disease at an advanced biological level.

Naomi Hanlon

Naomi Hanlon is a Data Scientist and is currently a Senior Data Scientist at Liberty IT and has a degree in mathematics and bioinformatics. She uses her exceptional analytical thinking and problem-solving skills in working with R for data analysis and visualisation to handle large datasets.

 

Fei Fei Li

Fei Fei Li

Fei Fei Li is currently working as a professor at Stanford University and teaching computer science. She has co-founded ai4allorg which is a nonprofit educating the next generation of AI technologists, thinkers, and leaders. She has worked with Google formerly and is an inspiration to all Data Science enthusiasts. 

 

Cassie Kozyrkov

Cassie Kozyrkov is currently working as the Chief Decision Scientist at Google and is well-known as a Data Science-related speaker. She has written down and shared her articulate thoughts related to this field. She has a podcast episode on DataHack Radio where she has elaborated on her life in Google and how she achieved her current position from being a Statistician.

Endnotes

Needless to say, women have the power to achieve anything and the aforementioned females who have emerged victorious in the cutthroat field of Data Science are prime examples of this statement. In the professional world of STEM, it is harder for women to survive considering how much of a male-dominated and male-oriented field it is, but most certainly not impossible. These important women leaders in Data Science indeed should be looked up to. Their hard work, determination and success have indeed motivated young students to actually pursue or consider pursuing a career in this field. 

 

breakthebiasEmpowerHERFei Fei LiInternational women's dayRachel Thomaswomen in data sciencewomen leaders
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