Data Science Daily

Data Science Daily explores the journey of transitioning from a data scientist to entrepreneurship, focusing on practical data science applications, insights into working in small vs. big companies, and leveraging emerging technologies like GPT and LSTM. It includes tutorials, career advice, and comparisons of tools and methodologies.

Data Science Applications Career Development Emerging Technologies Machine Learning Entrepreneurship Programming Tutorials Company Culture Data Science Tools Model Evaluation Business Strategy

The hottest Substack posts of Data Science Daily

And their main takeaways

You probably work at a big company because you thought you should: Why I'll never work at a FAANG or big company again

2 HN points • 04 Mar 23
💼 Business Workplace dynamics Career Choices Corporate culture
  1. Consider where you live and work based on constraints like availability, budget, and awareness.
  2. Big companies might offer prestige, but small companies can provide faster decision-making and more impactful work.
  3. Working at a small company can be more fulfilling than at a large corporation, with less bureaucracy and more client focus.

🤓LSTM Networks: The Power of Long-Short-Term Memory and comparisons to ARIMA/XGBoost/Prophet

0 implied HN points • 23 Feb 23
🕹 Technology Data science Neural Networks Machine Learning
  1. LSTM Networks can remember information for long periods and are great for processing sequential data.
  2. LSTMs can handle a wide variety of input and output types, making them flexible for real-world data.
  3. LSTMs are powerful for time series forecasting but can be computationally expensive, especially with large datasets.

An Introduction to LSTM with Attention Model

0 implied HN points • 01 Mar 23
🕹 Technology Machine Learning Neural Networks Artificial Intelligence Data science Applications
  1. LSTM models are good for handling input sequences of varied length like in language modeling and translation.
  2. Attention models help LSTM models focus on important parts of a sequence, improving accuracy.
  3. Combining LSTM with attention models can lead to better predictions and performance in tasks like natural language processing and image captioning.
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[🔥Hot Takes] Deep learning outperforms linear regression for causal inference and tabular data??

0 implied HN points • 02 Mar 23
🕹 Technology Data science Deep Learning Causal Inference Machine Learning Research
  1. Deep learning can outperform linear regression for causal inference in tabular data.
  2. Different perspectives exist in the debate between deep learning and traditional models like XGBoost.
  3. The study suggests that deep learning models like CNN, DNN, and CNN-LSTM may offer better performance in certain scenarios.

🤓[DS:Culture] Startup Math and Opportunity Costs

0 implied HN points • 17 Feb 23
💼 Business Startups Big Tech Economics Decision-making
  1. Being recruited by big tech as a data scientist is common due to the high demand for DS professionals.
  2. The opportunity cost of working at a startup versus a big tech company heavily depends on the potential stock gains.
  3. Economic-wise, bigger tech companies offer more stability and higher potential earnings, but working at a startup can offer a more dynamic and problem-solving oriented environment.

Workflow orchestrators: Metaflow, Kedro, Luigi, Airflow, Flyte, Prefect and others

0 implied HN points • 05 Apr 23
🕹 Technology Comparison Integration Design Debugging
  1. There are numerous workflow orchestrators available for data scientists to choose from.
  2. Consider the tool's design, local/remote running capabilities, and ease of interacting with output when selecting a workflow orchestrator.
  3. Choose a workflow orchestrator based on your specific use case, whether it's for data engineers, ML engineers, or data scientists.

😎 What is min_child_weight in XGBoost?

0 implied HN points • 25 Feb 23
🕹 Technology Data science
  1. For regression tasks, min_child_weight in XGBoost is the number of samples in a leaf.
  2. For non-regression tasks, min_child_weight in XGBoost is a measure of node purity.
  3. min_child_weight in XGBoost determines the minimum sum of instance weight needed in a child node.

How to interview: Probe, probe, probe

0 implied HN points • 28 Feb 23
💼 Business Interviewing
  1. In interviews, it's important to dig deep by asking detailed questions about projects and methodologies.
  2. Evaluate models not just based on accuracy, but also consider factors like false positive rates.
  3. Being aware of potential biases or traps in evaluation can help in making more informed decisions.

[Hack] Open Obsidian Templater from the Command Line, an iOS Shortcut, or a browser bookmark

0 implied HN points • 05 Mar 23
🕹 Technology Productivity Plugins Troubleshooting
  1. Learn how to open and run Obsidian commands from external applications using plugins like Advanced URI, Dataviewjs, and Templater.
  2. Utilize the constructed URI to open Obsidian from Chrome bookmarks, create shortcuts on iPhone, or create a terminal alias for easy command line access.
  3. Be aware of potential troubleshooting issues like needing Obsidian to be open and loaded for commands to work on iOS, and managing annoying popups in Chrome bookmarks.