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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 #4 – Data Privacy, Women in Data Science and More with Carla Gentry

Pranav Dar
Pranav Dar Last Updated : 21 Jan, 2019
4 min read

Introduction

Carla Gentry is one of most popular social media influencers in the data science field. She has over 300,000 followers on LinkedIn and 48k followers on Twitter. Her experience in this field is unparalleled and we are grateful for leaders like her who consistently give back to the community.

She has been a regular reader of Analytics Vidhya’s articles for quite a while now. It was a pleasure to have her appear on the podcast and to hear her views on data privacy, how the domain has changed in the last few years, her advice for women in data science, and a whole host of other topics.

This article contains highlights of Carla’s conversation with Kunal Jain. You can listen to the podcast by clicking on the above SoundCloud link or on our iTunes channel. Happy listening!

You can subscribe to DataHack Radio and listen to this, and all previous episodes, on any of the below platforms:

 

Carla’s Background

Carla holds a number of degrees including one in advanced economics, one in advanced mathematics, among others. Right after college she got an internship at one of the few econometrics firms in the United States.

There she worked with terabytes of data (this was well before ‘Big Data’ was a buzzword). Her experience started with tools and platforms like SAS, Pico, etc. Her role was working with credit card data but the nature of the work was such that it became monotonous after a while.

From there, she moved to the Weinstein Organization as a Senior Analyst and Data Specialist. Here Carla’s role expanded to include direct marketing experience. This was followed by a year at the University of Chicago Booth School of Business as a Research Support Analyst where she taught Ph.D students how to work with data, how to do data mining, etc.

Carla then moved on to work as the Marketing Information Manager at Career Education Corporation for the next 4 years. Post that she spent the next 3.5 years in the marketing and data field at PromoWorks, Tandus and Area203. She is now the owner and data scientist at Analytical Solution, where she has worked with clients like Kellogg, Johnson & Johnson, among various other organizations.

 

The Importance of Data Mining and Dealing with Bias in the Data

Carla considers data mining a critical aspect in any data driven project. It helps you understand trends, see patterns, interpret reasons why a person was denied a loan or credit card, etc. It’s at the core of the business and should be respected as such.

But she stressed on the importance of privacy and the need to be clear with your clients and customers about where you are going to use their data, for what purposes, and how it might impact them (if at all). GDPR has of course changed the game in Europe with regards to being transparent with users but Carla feels everyone, regardless of laws, should have this as a best practice.

With the amount of data that’s being generated in the world, from websites to social media, it’s critical to have that level of sensitivity.

Data scientists have a responsibility to be unbiased, have integrity and use their experience to add a positive background to the dataset, rather than let their feelings cloud the model building exercise.

 

Changes in the Last Few Years in the Data Science Domain

Carla recalled that if a business had terabytes of data in the 90s, running a program on that was next to impossible because the mainframe would have crashed. Now a mainframe isn’t even required! If you have a good database architecture set up, you can access millions and billions of rows in a fraction of the time it used to take previously.

COBOL, PASCAL, C++, SAS, Mathematica, MATLAB – these were the only programs available back when Carla started her journey. Of course now we have much more robust tools like R, Visual Studio (SQL), IBM’s suite of tools, etc. One of the biggest reasons why the older programs have been phased out is because of their inability to handle gigantic datasets, which the new tools can.

Of course with the rise of this data wave, and the advent of the digital era, the number of hackers and cyber thieves has also risen. So as things have gotten better in many ways, they have also gotten worse when it comes to security of your data.

 

Changes to be Expected in the next Few Years

Carla expects more laws like GDPR to come to action in the next few years that will dictate how organizations collect and deal with your data. She has a warning for businesses that abuse data – people will leave and look for ways to go incognito, which will leave your business with no data at all.

 

Women in Data Science

“We have got to get rid of the thinking that it’s always been this way, so it should stay that way.”

Carla is a champion of women in data science. She strongly believes that in order to incorporate more females into this field, the change has to start from the top. The CEO should have an obligation to encourage diversity into the organization by going to the HR department and digging deeper to understand the percentage of women in the company, and how to further improve upon that.

Her advice to aspiring female data scientists was to the point – stand your ground, be confident in yourself, find mentors, keep going and keep learning. You will find the perfect fit for you as long as you continue to believe in yourself and your abilities.

 

Social Media

Carla is an avid social media user and a HUGE influencer, especially in the data science field. It’s very time consuming (2-3 hours per day at times) but if what she shares helps even one person, she definitely considers it worth that investment. She feels it’s her responsibility to give back to the community, given that she has gotten so much from it over the years.

There are some awesome rapid fire questions at the end of the podcast – ensure you listen to those as well!

 

*P.S. – All views expressed by the guests on DataHack Radio are their own, and not of Analytics Vidhya.

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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