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Articles by Federico Mannucci
Should you tell the interviewer that you've already seen the question
What to do during a technical interview if you have already solved the problem before? I recently came across a post on Reddit that caught my attention. Op was asking what to do when, during a coding interview, you are posed a question that you have solved already. Interestingly this dilemma has been posed many times before and I always find the debate that comes with it very compelling. There is a strong faction that suggests just taking the stroke of luck and running with it.
Five Interview Mistakes I Am Not Doing Anymore
In my brief career as a software engineer, I already had the opportunity to go through hundreds of interviews. I started looking for my first developer job just before the pandemic started, it took me six months and dozens of interviews to find my first role. Since then I have changed roles twice, joining Glovo in September 2021 and now Uber in November 2022. Originally I was supposed to join Meta in September, but my offer was rescinded shortly before the start date, despite a signed contract.
My Experience Interviewing with Google, Meta, Amazon
Opinion | Engineering interviews with big tech What I learned and how you can also land your offer 2021 and the first half of 2022 have been a magic periods for tech workers. The economic recovery from the pandemic, the “great resignation”, and the strong stock market contributed to creating the best job market for software engineers in decades, with crazy compensations and awesome perks like full remote work and unlimited paid time off.
Using Hugging Face Models on non-English Texts
Image by author A pre-trained model is a saved machine learning model that was previously trained on a large dataset (e.g all the articles in the Wikipedia) and can be later used as a “program” that carries out an specific task (e.g finding the sentiment of the text). Hugging Face is a great resource for pre-trained language processing models. That said, most of the available models are trained for popular languages (English, Spanish, French, etc.).
How is AI-centered product design different?
People who are interested in AI often ask me what an AI designer is,and I’ve attempted to answer that question in this article. I wanted to go a step further, by helping designers and product teams understand how designing AI-based product experiences is different from traditional product design. Here is what I’ve learned over the last two years of managing AI design & innovation teams, about how AI-first product thinking is evolving the traditional product design process.
The Highest Data Science Salaries
Introduction Employment Level State Salary Breakdown Summary References Data Science salaries vary from state to state, as well as industry to industry. The occupation, employment, and wage statistics discussed in this article were gathered from the U.S. Bureau of Labor Statistics [2].
You are underutilizing SHAP values: understanding populations and events
I have been working with SHAP values for some time, and have had the opportunity to test a lot of applications that I had never seen on the web, but which worked very nicely and so I wanted to share with more people. This post is kind of a continuation of this other one about feature groups and correlation.
Development of a Benchmark Dataset with an interface to the FastAI DataLoader using Label Studio
Sequential Illustration of the Creation of a Segmentation Mask (Image by Author) I was excited to use Label Studio because it had so much buzz in the MLOps community, and there weresome impressive visuals on their marketing webpage. The use case we’re working on is to generate detailed segmentation masks for a small number of images that can be used as a benchmark dataset as we train a model to identify the “Canvas” part of a picture frame.
When and how to use power transform in machine learning
Power transfom is a family of functions that transform data using power laws. The idea is to apply a transformation to each feature of our dataset. What’s the purpose of apower transform? The idea is to increase the symmetry of the distribution of the features. If a feature is asymmetric, applying a power transformation will make it more symmetric. Let’s see an example using the breast cancer dataset in scikit-learn.
How biological and artificial A.I neurons compare
Dive into the salty ocean of the brain and get closer to the entities that inspire our A.I systems and make your thoughts possible. Explore how understanding the difference between artificial and biological neurons may give us clues about how to move towards a more flexible kind of artificial intelligence. “Sounds of a million souls”, 2 minute artistic tribute to our cortical columns and the billions of neurons in the neocortex (8K quality).
The Surprising Challenges of Global AI Philanthropy
PODCAST APPLE | GOOGLE | SPOTIFY | OTHERS Editor’s note: This episode is part of our podcast series on emerging problems in data science and machine learning, hosted by Jeremie Harris. Apart from hosting the podcast, Jeremie helps run a data science mentorship startup called SharpestMinds. We’ve recorded quite a few podcasts recently about the problems AI does and may create, now and in the future. We’ve talked about AI safety, alignment, bias and fairness.
Geopandas Hands-on: Introduction to Geospatial Machine Learning
Part 1: Introduction to geospatial concepts (this post) Part 2: Geospatial visualization and geometry creation (coming soon) Part 3: Geospatial operations (coming soon) Part 4: Building geospatial machine learning pipeline (coming soon) Photo by Marjan Blan | @marjanblan on Unsplash In this post we are going to cover the preliminary ground of basic geospatial datatypes, attributes, and how to use geopandas to achieve these.
10 tricks for converting numbers and strings to datetime in Pandas
Pandas has 2 built-in methods astype() and to_datetime() that can be used to convert numbers to datetime.
Sell your Data Science Projects by Showing the Financial Impacts on your Organization
How many articles have you read explaining “How to teach Data Science to Non-Technical colleagues”, “How to explain Machine Learning to top management audience”? The concept behind these articles is the idea of a dichotomy between the “Tech World” and the “Non-Tech World”. Data Science is a black box, a magical world where the Data Scientist must guide the uninitiated by explaining in simple words complex concepts.
Exploring the Wealth of Nations
Explaining the wealth of nations has been a topic of discussion and research since Adam Smith published his “Wealth of Nations” in 1776. The US Congress introduced the metric of Gross Domestic Product to measure national wealth in 1937 and since then it has become the ultimate yardstick in this regard. Simply put the Gross Domestic Product (GDP) is the total value of all goods and services produced in a country in a given year.
How to use Six Sigma in Data Quality improvements.
We are hearing everywhere about the high importance of data. We call even call it the new oil. As we are living in a data-driven age we need to know how to keep up with data trends and maintain a certain quality that allows using it. Once you understand the purpose, you can start with taking actions in Data Quality Improvements Program. Photo by @benchaccounting on Unsplash Key to having high data quality in organizations are awareness and bulletproof processes. Here well` focus on the second one.
A Few Words on Representation Learning
Image by Ahmed Gad from Pixabay In the last two decades, the field of Artificial Intelligence (AI) has seen substantial development in research and industrial applications. Machine learning algorithms such as Logistic Regression and Naive Bayes can recognize patterns from a set of features and solve problems that seemed otherwise impossible to be solved by hard-coding knowledge into expert systems.
Best Library To Simplify Math For Machine Learning!
We have discussed that integral and differential calculus are considered as a pre-requisite for understanding multiple machine learning concepts like backpropagation. Luckily, python also offers a free, lightweight, python-based library called sympy. It is a python library for symbolic mathematics. Before you install the sympy library on your system, there are a few pre-requisite requirements.
Google Fit Data Analysis
Photo by William Iven on Unsplash A while ago I started to study data analysis, and after some learning exercises, I desired to make my own data exploratory project. I thought about the topic and decided that I don’t want to use common and generally available data resources such as Kaggle or other popular platforms with datasets. I wanted to use something personal and interesting to me.
A Practical Guide To Quantum Amplitude Amplification
Grover’s search algorithm is one of the first and most prominent examples to show how a quantum circuit can be magnitudes faster than a classical algorithm In a previous post, we built a conceptual understanding of how the algorithm works. It follows a simple procedure. A quantum oracle inverts the amplitude of the searched state. Then, the diffuser flips all states about the mean amplitude, therefore, magnifying the searched state.
The Inferiority of Complexity
Photo by Renith R on Unsplash Seventy years after Nobel Prize winner Harry Markowitz’s development of modern portfolio theory, mean-variance optimisation remains a hip cornerstone of portfolio selection methods and a key component of the fund manager’s statistical toolbox. For contemporary investors seeking to minimise volatility and increase their returns, the standard combination of multiple regression and mean-variance optimisation is the order of the day.
Advice for Aspiring Data Scientists
Photo by Christina @ wocintechchat.com on Unsplash. A few years ago I wrote a piece called “Advice for New Data Scientists,” based on my time on Airbnb’s Data Science Team. The piece was one of KD-nugget’s most viewed blogs in April 2019 and it has nearly 40k views on Medium since then.
Power BI - How to fit 200 million rows in less than 1GB!
https://unsplash.com/photos/uu-Jw5SunYI When you are dealing with technology, sometimes you need to accept the fact that there are certain limits — especially when it comes to resources available. And sometimes, maybe more often than not, business requests are such that it’s hard or almost impossible to implement within the current setup. I’ve faced a quite challenging task recently — to migrate an SSAS Multidimensional solution to Power BI.
Periodicity of Scripted Network Attacks
Scripted network attacks refer to attacks automatically generated using automated scripts e.g DOS attacks and Probe attacks. In network traffic analysis, it is often cumbersome to separate anomalous traffic i.e generated by scripted codes from legitimate traffic as these scripts are usually written to evade most intrusion detection systems. Scripted codes for attacks are written to send X amount of traffic to certain target(s) in X amount of spaced time unit.
Why Queues and Heaps in Python are Critical to Understand
Photo by Melanie Pongratz on Unsplash Queues are something intrinsic to humans as when we need to do something and it’s busy you usually have to wait in a queue or line of some sort. In Python and other programming languages, queues and heaps are a way to build sequential lists of objects and a tidy way to keep them in order. For programs that are time- or sequence-dependent, queues and heaps are ideal ways to capture this type of information to flow through the required processes of the program.
MLOps vs DevOps
Photo by HalGatewood.com on Unsplash As the field of machine learning has matured in recent years, the need for integrating automatic continuous integration (CI), continuous delivery (CD) and continuous training (CT) to machine learning systems has increased. The application of DevOps philosophy to a machine learning system has been termed MLOps. The aim of MLOps is to fuse together the machine learning system development (ML) and machine learning system operation (Ops) together. What is DevOps?
Autoencoder network optimization for dimensionality reduction
In order to understand how well the optimization worked, we will compare the dimensionality reduction of the autoencoder to that of a PCA (principal component analysis). We built our dataset with 4 features. We introduced the polynomial features to increase the dimensionality, then we apply standard scaling to all of them. Finally we will reduce the dimensionality of our dataset to 3. PCA explains around 77% of variance for this dataset. PCA and Encoder first 3 components.
Essential Math for Data Science: Visual Introduction to Singular Value Decomposition (SVD)
ESSENTIAL MATH FOR DATA SCIENCE (image by author) In this article, you’ll learn about Singular value decomposition (SVD), which is a major topic of linear algebra, data science, and machine learning. It is for instance used to calculate the Principal Component Analysis (PCA). You’ll need some understanding of linear algebra basics (feel free to check the previous article and the book Essential Math for Data Science).
3 Tools to Monitor and Optimize your Linux System
If you are a Linux user, you might want to know some important information about your computer such as: System’s CPU usage, memory usage, and disk usage System information Disk usage of each folder or file and when was the last time that you used them. Memory and CPU consumption of the running processes Startup applications Knowing those pieces of information will enable you to optimize your system.
5 Data Science Hackathons to Grow and Improve Your Skills
Photo by Sigmund on Unsplash When you get into a field like data science, a theoretically endless field that’s always developing, always advancing, and always moving, it will be challenging to surely pinpoint a good point where you can say, “for now, I have enough knowledge to start applying for jobs.” In most technical and science fields, there’s no clear pausing point where you feel like you know “enough.” In those fields, it’s impossible to try and master everything, so when do you know...
Don’t Let a Machine Learning Engineer Work Alone
Image under license to Mary Alfheim from Shutterstock Machine learning engineers are in enormous demand. You already are painfully aware of this if you’ve tried to hire anyone with a title even close to this in the past few years. These engineers are incredibly skilled at taking a data science output, like a model, and actually putting it into production to ship new applications.
6 Math Foundations to Start Learning Machine Learning
Photo by Antoine Dautry on Unsplash As a Data Scientist, machine learning is our arsenal to do our job. I am pretty sure in this modern times, everyone who is employed as a Data Scientist would use machine learning to analyze their data to produce valuable patterns. Although, why we need to learn math for machine learning? There is some argument I could give, this includes: Math helps you select the correct machine learning algorithm.
5 Things You Didn’t Know Anaconda Navigator Had
Introduction Python Training Video Tutorials Learning Webinars Community Events Community Forum Summary References Data scientists often use Anaconda Navigator [2], which houses popular and useful applications like JupyterLab, Jupyter Notebook, and RStudio. It is usually at these three applications where we tend to stop looking into this platform for other tools.
Visualisation and Costing of Truck Transport Network with Python
Build a model to get visibility of your transportation network focusing on deliveries per run, average truck size, and cost per truck Photo by ALE SAT on Unsplash After the previous series of articles covering Warehousing Operations Optimization with Python (Link), in this article, we’ll try to use the same methodology for improving Road Transportation efficiencyby Processing Data: extract unstructured transportation records and process them to build your optimization model Improving...
6 Useful Metrics to Evaluate Binary Classification Models
Photo by Craig Adderley from Pexels Photo by Taylor Nicole on Unsplash This is Newt. Living in a mythical world, Newt aspires to become the best dragon trainer. But a dragon only accepts someone as his forever owner if his owner is the first creature it sees right after hatching. A bit like love at the first sight, coincidental yet so precious! That’s why Newt has been searching high and low for hatchable dragon eggs. Unfortunately, a hatchable egg is really difficult to come by. What’s more?
Soft-launching an AI/ML Product as a Solo Founder
Pro tip: 3D printing startups can make their own swag. Image by Author. Technical deep dive into how I built Print Nanny, which uses computer vision to automatically detect 3D printing failures. I’ll cover each development phase: from minimum viable prototype to scaling up to meet customer demand. Launching an AI/ML-powered product as a solo founder is a risky bet.
3 Steps to Ensure Success as a new-hire Data Engineer at Any Company
TL;DR at the end of the article. Photo by Razvan Chisu on Unsplash I’ve been four years into a career leading to data engineering, and am very glad to see the wave has risen.
Alexey Grigorev on His Career, Data Science, and Writing
Photo by Brendan Church on Unsplash This week, we chat with Alexey Grigorev, a lead data scientist from OLX Group. We actually met in person last year at OLX Group’s Prod Tech Conference where he presented how to deduplicate images. However, we didn’t recognize each other online, and only found out when I asked him about it right before this chat! Alexey has an interesting career. He started as a software engineer focused on Java.
Dask Delayed - How to Parallelize Your Python Code With Ease
Photo by Jose Castillo on Unsplash We all know Python isn’t the fastest programming language. Its (GIL) mechanism allows only one thread to execute Python bytecode at once. You can avoid this limitation by changing the interpreter or implementing process-based parallelism techniques.
How Does a Company Find the Best Market to Target Their Ads?
Photo by MayoFi on Unsplash A fictitious e-learning company, currently based in the U.S., has asked me to run a survey to determine the best markets to promote their product. They will also like to know the kind of content that learners are willing to pay for. This company offers different courses on programming including web development, mobile development, data science, and game development.
Visualizing How Global Trade Shapes the Climate Crisis
Decision-makers are on a tight schedule if they want to live up to their obligations in the Paris agreement. Luckily, there is a lot of research about understanding the climate crisis from a financial perspective that can help them. As a data scientist, I thought it would be interesting to look at some of the data and see if I could make out some patterns. In my previous blog post, I used data science tools to mine the global economy.
Causal Inference - To Control or not to Control
https://github.com/cryptexis/causal_inference/blob/main/collider_fork/collider_fork.ipynb I will avoid repeating most of the stuff that can be found in other tutorials and books e.g. what is a collider or fork. I will provide useful links when a relevant topic is discussed. Otherwise, I assume that the reader is either familiar with the topic or sufficiently motivated to learn more.
Home Field Advantage: Does It Exist Without Fans?
Photo by Matthew Henry from Burst In most sports, it has widely believed that the home team has a significant advantage over the visiting team, commonly known as the home advantage. This can be due to a variety of factors but two of the most popular reasons cited are fan presence and the players’ familiarity and comfort with the environment. An iconic example of the latter is the “Green Monster” wall at Fenway Park for the Boston Red Sox.
Why SOLID Principles are not a solid solution for Software Design
The Software World Photo by Michael Dziedzic on Unsplash Robert J. Martin introduced the SOLID Principles in 2000 when Object-Oriented Programming is state of art for programmers. Everyone wants to design something that is lasting, can be reused as much as possible, with minimal change needed in the future. SOLID is the perfect name for it. In fact, Object-Oriented Programming works best when we can separate out what will stay from what will change. SOLID Principle helps advocate that.
How You Should Read a Machine Learning Paper
Throughout this post, we will review the most important principles you should take into account when reading a machine learning paper and if you actually need to read papers to advance in your path as Machine Learning Engineer/Practitioner. Picture from Unsplash A paper is a written article, but not a journalist one or one you could find in an internet blog but an academic article.
7 Paths To Data Science
There are many ways to pursue a career in data science. This article covers six possibilities. Of all the possibilities two things to keep in mind. One, there are more than seven paths. Two, there is no right or wrong way to go about it. From my frequent and ongoing discussions with other data professionals, I wanted to share a summary of common paths towards data science. For example, I spent most of my career working in education. In my career, I have worked in the classroom. I taught abroad.
Here’s the Honest Truth About Making a Career Switch into Data Science.
Photo by Markus Winkler on Unsplash At the beginning of 2018, I really wanted to be a data scientist. I thought that the title sounded prestigious, I thought that building “machine learning models” sounded really neat, and it was a hot career to get into. If any of the points above resonated with you, then I encourage you to keep reading. I frequently get asked about how one can make a career switch into data science from something completely unrelated, like accounting or chemical engineering.
Neural Networks for Survival Analysis in R
I have received many questions about survival neural networks (‘survival networks’) in R, ranging from “is this even possible?” to “how do I install Python in R?” and “how can I tune these models?”. If you are an R user with an interest in survival networks then this is the article for you! This is not a tutorial of how to use the relevant packages but a demonstration to answer these questions.
The exploration-exploitation dilemma
Credit: Joshua Woroniecki on Pixabay This post was co-written with Baptiste Rocca. “Should I go for the decision that seems to be optimal, assuming that my current knowledge is reliable enough? Or should I go for a decision that seems to be sub-optimal for now, making the assumption that my knowledge could be inaccurate and that gathering new information could help me to improve it?”. In very simple terms, this is how we could summarise the exploration-exploitation dilemma.
4 Reasons why Correlation does NOT imply Causation
Photo by Lea L on Unsplash The phrase “correlation does not imply causation” has become a cliche of sorts. This seems to be the phrase that impassioned readers type into the comments section when they read articles claiming incredulous links between two variables. What does “correlation does not imply causation” mean? When should we use this phrase? How can we tell the difference between correlation and causation? What are the reasons why correlation does not equal causation?
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