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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

Thanking all successful women in the world of data analytics

A
Analytics Vidhya Last Updated : 13 Apr, 2015
5 min read

 Foreword

Women like Lady Ada Lovelace, Marie Curie, Mother Teresa, Indra Nooyi, Oprah Winfrey, Marissa Mayer, Sheryl Sandberg and many others have passionately served their communities across the world. They have enlightened other people to challenge the status quo and prove their worth.

On this International Women’s day, we thank some of the most successful women in the world of Data Analytics. We hope that they continue to contribute to the world of data science and inspire millions through their work.

Here are some of successful women who have proved their mettle in data analytics:

women_in_analytics

Carla Gentry(@data_nerd)

Carla Gentry

Carla Gentry is a Data Scientist & Founder of Analytical Solution. She holds a graduate degree in Maths & Economics from University of Tennessee. She carries an invaluable experience of over 15 years which includes working for Fortune 500 companies like Hershey, Kraft, Johnson & Johnson, Kellogg’s and Firestone. She is one of the most popular personality in Big Data community to follow on Twitter. In 2013, the Information Week named her one of “10 IT Leaders to Follow on Twitter.”

 

 

 

Hilary Mason(@hmason)

hilary_electronicsHilary Mason loves two things: Data & Cheeseburgers! She is the Founder of Fast Forward Labs. Also, she is the co-founder of hackNY.org & DataGotham.  She completed her graduation in Computer Science from Grinnell College, US. She carries an invaluable experience of over 15 years which involves working at companies such as Accel Partners, Bitly, Path101 etc.  In 2012, she was the recipient of TechFellow Engineering Leadership Award. She has also co-authored a book named ‘Data Driven: Creating a Data Culture’.

 

 

Corina Cortes(@CorinnaCortes)

CorinnaCortes_200Corinna Cortes is the Head of Google Research, NY. Corinna received her MS degree in Physics from University of Copenhagen and joined AT&T Bell Labs as a researcher. She has also worked at AT&T Labs, formerly AT&T Bell Labs for more than 10 years as a researcher. She received her PhD in Computer Science from the University of Rochester. Her research areas include Artificial Intelligence and Machine Learning, General Science and Algorithms & Theory. She is a competitive runner and mother of two.

 

 

Claudia Perlich(@claudia_perlich)

Claudia Perlich

Claudia is currently working as a Chief Data Scientist at Dstillery, where she is working closely over a multitude of machine learning algorithms and data processing tasks. She received her MS in Computer Science from University of Colorado. She holds a PhD from Leonard N. Stern School of Business, New York University. Claudia got featured in the list of 100 most creative people of 2014 by FastCompany. Her hobbies include horse riding, weight lifting, snowboarding, gardening etc.

 

 

Monica Rogati(@mrogati)

Monica Rogati

Monica has strong background in applied machine learning (CMU CS PhD), data science, wearables and health, social network analysis, recommender systems, statistical text mining and multilingual information retrieval (search). Her passion lies in turning data into products, actionable insights and meaningful stories. Currently, She is working as a Vice President of Data at Jawbone. Prior to Jawbone, She was one of the early members of the LinkedIn data science team and has worked there for more than 5 years as Senior Data Scientist. She is also one of the Fast Company’s 100 most creative people in business.

 

 Daphne Koller(@DaphneKoller)

The Future of Higher Education: Daphne Koller

Daphne is the President and Co-Founder of Coursera. She holds a PhD in Computer Science from Stanford University. Later, she taught at Stanford for 18 years as a Professor of Computer Science. She has been felicitated with various awards & accolades which includes ACM Infosys Awards, MacArthur Foundation Fellowship and many more. Her research focus lies in using probabilistic models and machine learning to understand complex domains that involve large amounts of uncertainty. During idle time, she likes to listen to music & spend time with her daughters.

 

 Lillian Pierson(@BigDataGal)

Lillian Pearson

Lillian is a self-proclaimed nomadic datapreneur. She is the Founder & Chief Data Scientist at ‘DataMania’, an information services start-up. Prior to DataMania, she founded BigDataGal.com. She strongly believes ‘Data science and big data technologies are quickly changing the world. These technologies have the power to solve business, social, lifestyle, and environmental problems to a degree that is unparalleled throughout human history’. She is the author of famous book ‘Data Science for Dummies’. She’s an avid traveller and has travelled over 28 countries across the world.

 

 

 Radhika Kulkarni(LinkedIn)

Radhika Kulkarni

Currently, She is the VP of Advanced Analytics R&D at SAS Institute. Radhika oversees software development in many analytical areas including Statistics, Operations Research, Econometrics, Forecasting and Data Mining. She received MSc Degree in Mathematics from IIT Delhi, India. She holds a PhD in Operations Research from Cornell University. Radhika is a Member of the Board of Directors for IDeaS, a SAS Company.

 

 

 

 Alice Zheng(LinkedIn)

alicez-headshot

Alice is the Director of Data Science at Dato. She holds Ph.D. and B.A. degrees in Computer Science, and a B.A. in Mathematics, all from University of California, Berkley. She is known to be an expert in machine learning algorithms. She loves juggling with data and empowers others in doing so. Prior to joining Dato, she worked for 6 years at Microsoft as a machine learning researcher. Her focus is on ‘easing the dependence on expertise by making learning algorithms more automated, their outputs more interpretable, and the labeling tasks simpler’.

 

 Sunita Sarawagi(LinkedIn)

Prof.-Sunita-Sarawagi-150x150Sunita Sarawangi is a graduate in Computer Science from IIT Kharagpur, India. Later, she received her PhD in databases from University of California at Berkley. Sunita’s work is well known in databases & data mining and has several publications & patents registered at her name. Her current research interests are web information extraction, data integration, graphical models and structured learning. She was awarded the Best paper award at 1998 ACM-SIGMOD International Conference on Management of Data. She serves on the board of directors of ACM SIGKDD and VLDB foundation.

 

 

Kristin P. Bennett

Kristan P Bennet

Kristin is a founding associate editor of ACM Transactions on Knowledge Discovery and Data Mining. She serves on the advisory board of the Journal of Machine Learning Research. Her research areas includes Mathematical Programming, Operations Research, Machine Learning, Bioinformatics and Data Mining communities. She has gained experience developing data mining approaches for chemistry, biology, and public health related applications. Currently, She is a Professor at Mathematical Science and Computer Science at Rensselaer.

 

Once again, we would like to thank these successful women in the field of data analytics. They have unfailingly contributed to the development of data analytics globally.

This is not an exhaustive list, so if you know some one who should be on the list, please feel free to add them through the comments below.

 

If you like what you just read & want to continue your analytics learning, subscribe to our emails, follow us on twitter or like our facebook page.

data analyticsSuccessful WomenWomen in Analytics
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Analytics Vidhya

Analytics Vidhya Content team

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Responses From Readers

Clear

Sudhi
Sudhi

Great to see that female fraternity is contributing in a great manner to this budding industry. All the best to all the ladies out their and happy women's day to you all.

123

Tom Dietterich
Tom Dietterich

Kristin Bennett is a Full Professor (according to her web page). Perhaps one of the most important names missing from this list is Grace Wahba (University of Wisconsin). She is the co-inventor of the kernel trick and co-author of the Representer Theorem that underlies support vector machines. There is also a long line of important women in the history of statistics.

123

Analytics Vidhya Content Team
Analytics Vidhya Content Team

Thanks for pointing out the correction. I too reckon, there are lot of women in statistics. I would soon write a separate blog post featuring their contributions.

123

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