This is my personal project, a part of the Data Science course at Duke University, Fuqua School of Business, MQM Business Analytics Program. Photo by Kym Ellis on Unsplash The red wine industry shows a recent exponential growth as social drinking is on the rise. Nowadays, industry players are using product quality certifications to promote … Read more Red Wine Quality Prediction Using Regression Modeling and Machine Learning

Photo by Lukas on Pexels After running several statistical tests to assess my models, I decided to dig deeper into the theory and ask myself questions such as why the number of samples is relevant for the statistical test, why the standard deviation has a square root in the denominator, or why statisticians differentiate between … Read more From the Central Limit Theorem to the Z- and t-distributions

Photo by Aleks Dorohovich on Unsplash This section is closely related to the points made in section 1. I’m not talking about reading news articles on the banning of facial recognition software or reading casual tech blogs. To keep up to date with the ‘real’ developments and progress occurring within the AI field, you have … Read more 5 Ways To Become A Better Machine Learning Practitioner

In this article, I will discuss what I think are the three most important architectures to be aware of for NLP. Recurrent Neural Network (RNN). Image from Wikipedia under CC BY-SA 4.0 License. Recurrent neural networks are special architectures that take into account temporal information. The hidden state of an RNN at time t takes … Read more 3 neural network architectures you need to know for NLP!

Identify defendants who are more likely to hire a private attorney using machine learning. Image by Author I recently consulted for a law firm to identify defendants who are more likely to use their services. Previously, they tried to send emails to all defendants, but the response rate was close to zero. They also tried … Read more HireAttorney — bringing a more focused market to defense attorneys

Counterfactual vs Contrastive Explanations: As defined in (Counterfactual explanations without opening the black box: Automated decisions and the GDPR [17]) counterfactual explanations have little difference from contrastive explanations as defined in [4]. Both look for minimal changes, although the latter looks for a more constrained change (additions), to the input for the decision of the … Read more Counterfactual vs Contrastive Explanations in Artificial Intelligence

How to bridge the gap between business executives and data scientists to overcome challenges and move towards data- and business-literate professionals Photo by Vladimir Proskurovskiy on Unsplash When you think about data scientists*, most likely you are imagining someone working in a tech company that has data solutions as their core, like software providers. Perhaps, … Read more Why data scientists and business executives struggle to work together

Number of counties won by each candidate Let’s check how many counties won by each candidate. I will show you two different ways to accomplish this task. The first way is to use the groupby function and count the number of “True” values for each candidate. winner = elections[[‘candidate’,’won’,’state’]]\.groupby([‘candidate’,’won’], as_index=False).count()winner = winner[winner.won == True]winner.rename(columns={‘state’:’won_county’}, inplace=True)winner … Read more Analysis of US Elections 2020 with Pandas

Want to get hired? Work on your technical skills! Want a promotion? Learn institutional knowledge! Domain Knowledge & Institutional Knowledge: When my past company’s CEO encouraged me to work on Declined Transactions as my first project after joining the team, I was disappointed! Right out of academia, I was hoping for a complex project like … Read more Paving the Way to Google!

A part of the Bootstrap Thompson Sampling algorithm (image by author). An uncommon combination allows efficient sequential learning on the edge The multi-armed bandit (MAB) problem is a relatively simple (to describe that is, not to solve) formalization of a sequential learning problem that has many applications. In its canonical form, a gambler faces a … Read more Bandits, WebAssembly, and IoT

A virtual environment is an isolated region where a particular version of Python and its packages are installed enabling the installation of different versions of Python. Each environment has its own files, directories, and paths. Thus a single system can cater to different projects that demand different Python versions. For Python installation and its basics, … Read more Installing Jupyter Notebook for Different Environments in Windows 10

AI for Assistance in Fact-Checking. Now that we have a basic understanding of the problem of fake news, how can we tackle it? Of course, there is always the path of manual fact-checking, such as when news groups fact-check presidential candidates during a debate. And completely opposite of this is the path of automatic fact-checking, … Read more Truth-Seeking in the Post-Truth Era: Tutorial at EMNLP 2020

There appears to be a wide variety of answers to whether you need an advanced degree to get a data science job. Here, I review and explore the educational level, as discussed by Robert Half, Burtch Works, and Indeed. As you begin your data science career, Robert Half and Indeed agree that you should have … Read more Do You Need a Master’s Degree in Data Science?

How to use scatterplots, correlation coefficients, and linear regression effectively Photo by Magda Ehlers from Pexels One of the most common analyses conducted by data scientists is the evaluation of linear relationships between numeric variables. These relationships can be visualized using scatterplots, and this step should be taken regardless of any further analyses that are … Read more Evaluating linear relationships

Let’s take a look at the function in Power BI, you know I love Power Query, so let’s start there. I have a simple table here — a list of units by company. Image by author Basically, I want to rejig this simple data frame into a table that has 2 rows of each company. … Read more Power BI Functions — (List.MaxN, R-slice_max, TOPN)

How to Write Basic GSQL Commands (Note: This is Part 3 of a series. Check out the past blog to load the data into your database!) Now that you have your data loaded, the next big step will be catching them all and extracting relevant information from your graph using queries. This blog will be … Read more PokéGSQL: Create your First GSQL Algorithms with a Pokémon Dataset!

Adding to the Schema and Adding More Data (Note: This is a Part II of a series. Please check out Part I here: https://towardsdatascience.com/using-api-data-with-tigergraph-ef98cc9293d3) In the last blog, we learned how to upsert API data into a graph for a basic schema. Now, we’re going to go deeper, making a more complex schema and adding … Read more Pokémon Lab Part II: Adding More Data

How to add click actions and live updates to the Dash DataTable Photo by Markus Spiske on Unsplash Plotly Dash is an incredibly powerful framework that allows you to create fully-functional data visualization dashboards. Using Dash, you can create a full front-end experience using only Python. The library does a great job of abstracting away … Read more Creating Interactive Data Tables in Plotly Dash

Introduction Why did you choose Data Science? What is your everyday work like? Does Data Science get easier? Where do you see Data Science in 5 years? Will you ever leave Data Science? Summary References Data Science has become an incredibly popular field over the past few years. When I started applying to Masters’s programs … Read more The Top 5 Data Science Questions I Get Asked

It doesn’t replace your job, it only makes it a little easier. Photo by Possessed Photography on Unsplash In the past five years, one trend that has made AI more accessible and acted as the driving force behind several companies is automated machine learning (AutoML). Many companies such as H2O.ai, DataRobot, Google, and SparkCognition have … Read more How to approach AutoML as a data scientist

A practical guide to creating differentially private, synthetic data with Python and TensorFlow In this post, we’ll train a synthetic data model on the popular Netflix Prize dataset, using a mathematical technique called differential privacy to protect the identities of anonymized users in the dataset from being discovered via known privacy attacks such as re-identification … Read more How To Create Differentially Private Synthetic Data

Photo by Amy-Leigh Barnard on Unsplash The era of the monolithic data warehouse/data lake is coming to an end — long live the decentralized data mesh! Oh, do not despair! All those person-years spent cleaning, transferring, and loading data into your centralized systems hasn’t been in vain. With data mesh, you don’t have to start … Read more Data Mesh 2.0

Pruning Neural Networks To obtain fast and accurate inference on edge devices, a model has to be optimized for real-time inference. Fine-tuned state-of-the-art models like VGG16/19, ResNet50 have 138+ million and 23+ million parameters respectively and inference is often expensive on resource-constrained devices. Previously I’ve talked about one model compression technique called “Knowledge Distillation” using … Read more Model Compression via Pruning