Data Science & Machine Learning

Industry Data Science vs Academia Data Science Comparing Data Science in academia and Data Science in industry is like comparing tennis with table tennis: they sound similar but in the end, they are completely different! 5 big differences between Data Science in academia and in industry 👇: 1️⃣ Model vs Data: Academia focuses on models, industry focuses on data. In academia, it’s all about trying to find the best model architecture to optimise a defined metric. In industry, loading and processing the data accounts for around 80% of the job. 2️⃣ Novelty vs Efficiency: The end goal of academia is often to publish a paper and to do so, you will need to find and implement a novel approach. Industry is all about efficiency: reusing existing models as much as possible and applying them to your use case. 3️⃣ Complex vs Simple: More often than not, academia requires complex solutions. I know that this isn’t always the case but unfortunately, complex papers get a higher chance of being accepted at top conferences. In industry, it’s all about simplicity: trying to find the simplest solution that solves a specific problem. 4️⃣ Theory vs Engineering: To succeed in academia, you need to have strong theoretical and maths skills. To succeed in industry, you need to develop strong engineering skills. It is great to be able to train a model in a notebook but if you cannot deploy your model in production, it will be completely useless. 5️⃣ Knowledge impact vs $ impact: In academia, it’s all about creating new work and expanding human knowledge. In industry, it is all about using data to drive value and increase revenue.

Here is the list of few projects (found on kaggle). They cover Basics of Python, Advanced Statistics, Supervised Learning (Regression and Classification problems) & Data Science 👇👇 https://t.me/free4unow_backup/582

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1. What is the Impact of Outliers on Logistic Regression? The estimates of the Logistic Regression are sensitive to unusual observations such as outliers, high leverage, and influential observations. Therefore, to solve the problem of outliers, a sigmoid function is used in Logistic Regression. 2. What is the difference between vanilla RNNs and LSTMs? The main difference between vanilla RNNs and LSTMs is that LSTMs are able to better remember long-term dependencies, while vanilla RNNs tend to forget them. This is due to the fact that LSTMs have a special type of memory cell that can retain information for longer periods of time, while vanilla RNNs only have a single layer of memory cells. 3. What is Masked Language Model in NLP? Masked language models help learners to understand deep representations in downstream tasks by taking an output from the corrupt input. This model is often used to predict the words to be used in a sentence. 4. Why is the KNN Algorithm known as Lazy Learner? When the KNN algorithm gets the training data, it does not learn and make a model, it just stores the data. Instead of finding any discriminative function with the help of the training data, it follows instance-based learning and also uses the training data when it actually needs to do some prediction on the unseen datasets. As a result, KNN does not immediately learn a model rather delays the learning thereby being referred to as Lazy Learner.

On What Kinds of data does chatgpt trained on