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Articles by Travis Tang
Class Imbalance Strategies — A Visual Guide with Code - NewsBreak
NC Teacher On Viral Video Which Showed Her Fighting A Student: 'As Much As I Love The Kids, I Cannot Put My Life In Danger'
Class Imbalance Strategies - A Visual Guide with Code
Understand Random Undersampling, Oversampling, SMOTE, ADASYN, and Tomek Links Class imbalance occurs when one class in a classification problem significantly outweighs the other class. It’s common in many machine learning problems. Examples include fraud detection, anomaly detection, and medical diagnosis. A model trained on an imbalanced dataset perform poorly on the minority class. At best, this can cause loss to the business in the case of a churn analysis.
60 ChatGPT Prompts for Data Science (Tried, Tested, and Rated)
Automate data science tasks with ChatGPT I rated 60 ChatGPT functions for Data Science. Use these prompts and ask ChatGPT to write, and explain code, optimize data science code. It can also explain data science concepts, suggest ideas, and troubleshoot problems. The table of content contains all 60 prompts. Click on the links in the table of content to navigate to the prompt, ChatGPT’s response and the rating.
ChatGPT Cheat Sheet for Data Science
Discover how NVIDIA's new cuDF pandas Accelerator Mode can turbocharge your data manipulation tasks in Python. Learn how to get started, the benefits it offers, and how it simplifies high-performance pandas coding. Discover the latest update from OpenAI, GPT-4 Turbo with vision, and its key features, including improved knowledge cutoff, an expanded context window, budget-friendly pricing, and more. Discover the future of AI customization as OpenAI unveils GPTs and the GPT Store.
Pandas DataFrame but Much Faster
Perform multithreaded, optimized pandas operations Let’s face it. Pandas is slow. When you have millions of rows in your dataframe, it becomes incredibly frustrating to wait for a minute for a single line of code to execute. You will end up spending more time waiting than doing actual analytics. Multiple libraries exist to solve this problem. PySpark, Vaex, Modin, and Dask are some examples. Today, let’s look at Polars. I am not affiliated with Polars, PySpark, Vaex, Modin, and Dask in anyway.
Lazypredict: Run all sklearn algorithms with a line of code
How to (and why you shouldn’t) use it Here are two pain points of data scientists: Pain Point 1: Limited time in the data science lifecycle Data scientists have to prioritize. This may mean spending more time on understanding the business problem and identifying the most appropriate approach rather than focusing solely on developing machine learning algorithms.
Run all sklearn algorithms with a line of code
Here are two pain points of data scientists: Pain Point 1: Limited time in the data science lifecycle Data scientists have to prioritize. This may mean spending more time on understanding the business problem and identifying the most appropriate approach rather than focusing solely on developing machine learning algorithms.
Convert Jupyter Notebooks into Functions
Parameterize notebooks so you can programmatically run them You’ve trained your machine learning model in a Jupyter notebook. Now, you want to run that model on data that comes in daily. Day in, day out, you create a new copy of the same notebook and run it. You store the copy of the notebook and pass that results to your stakeholders. In another scenario, you have a new set of data every day that needs to be visualized with the same code on Jupyter notebook.
4x Faster Pandas Operations with Minimal Code Change
Stop waiting on pandas operations. Parallelize them. One of the major limitations of Pandas is that it can be slow when working with large datasets, particularly when running complex operations. This can frustrate data scientists and analysts who need to process and analyze large datasets in their work. There are a few ways to address this issue. One way is using parallel processing.
4x Faster Pandas Operations with Minimal Code Change
One of the major limitations of Pandas is that it can be slow when working with large datasets, particularly when running complex operations. This can frustrate data scientists and analysts who need to process and analyze large datasets in their work. There are a few ways to address this issue. One way is using parallel processing. Running multiple pandas operations together! Image by Stable Diffusion.
How to Speed Up Data Processing in Pandas
Improving your big-data analysis workflows with an open-source library If you’re a data scientist working with large datasets, you must have run out of memory (OOM) when performing analytics or training machine learning models. That’s not surprising. The memory available on a desktop or laptop computer can easily exceed large datasets, making loading the entire dataset nearly impossible.
How to Speed Up Data Processing in Pandas
If you're a data scientist working with large datasets, you must have run out of memory (OOM) when performing analytics or training machine learning models. That's not surprising. The memory available on a desktop or laptop computer can easily exceed large datasets, making loading the entire dataset nearly impossible. We are forced to work with only a small subset of data at a time, which can lead to slow and inefficient data analysis.
Unit Testing for Data Science with Python
Catch expensive mistakes early with nose2 and parameterized tests You’ve deployed a new machine learning model at work. You can finally enjoy the weekend, you thought to yourself. Little did you know that an imminent storm of errors is about to tear down your model and ruin your weekend. Why does that happen? Insufficient error checking. Data scientists are taught to perform data exploration and modeling, but we’re not taught to perform unit tests, particularly on edge cases.
Unit Testing for Data Science with Python
You've deployed a new machine learning model at work. You can finally enjoy the weekend, you thought to yourself. Little did you know that an imminent storm of errors is about to tear down your model and ruin your weekend. Why does that happen? Insufficient error checking. Data scientists are taught to perform data exploration and modeling, but we're not taught to perform unit tests, particularly on edge cases.
Data Science Roles Beyond the Title “Data Scientist”
The Top Data Science Jobs of the Future This article will help you understand the evolving landscape of data science so you can embrace continuous learning and position yourself for success in this dynamic and in-demand field.
How NLP is Changing the Future of Data Science
I must have shadows on the way If I am to walk I must have Each step taken slowly and alone To have it ready made And I must think in lines of grey To have dim thoughts to be my guide Must look on blue and green And never let my eye forget That color is my friend And purple must surround me too The yellow of the sun is no more Intrusive than the bluish snow That falls on all of us. I must have Grey thoughts and blue thoughts walk with me If I am to go away at all.
Seven Tricks for Better Data Storytelling: Part II
blog With proper design skills, data teams have the power to frame arguments and persuade with data. That was the key takeaway from a recent episode of DataFramed featuring Andy Cotgrave, the technical evangelist at Tableau. blog Effective data storytelling enables data practitioners to cross the last mile of analytics, enabling them to drive action with their insights. In a recent webinar, Brent Dykes discussed how data, narrative, and visuals can drive effective data stories.
Seven Tricks for Better Data Storytelling: Part I
blog In a recent episode of DataFramed, Andy Cotgrave, technical evangelist at Tableau, shared the importance of data storytelling in driving change with analytics. In this two-part blog post, we deep dive into seven concrete tips that Andy provided on data storytelling blog Effective data storytelling enables data practitioners to cross the last mile of analytics, enabling them to drive action with their insights.
2022 data trends and predictions
Top 2022 Resources to Sharpen your Data Skills Get access to our top-performing resources from 2022, including webinars, blog posts, white papers, cheat sheets, tutorials, and articles, all designed to help you sharpen your data skills and scale your organization's data culture. Making the Most of Your Year In Data 2024 Dive into your Year In Data 2024 journey and discover how to make the most of your data achievements.
Four Ways Your Team Can Start Leveraging Data Science
How to Build a Winning Data Team Your organization is generating countless data points, and this data hides many actionable insights that can improve your company’s performance. Learn how to build a winning data-driven team that leverages the right data science skills. Data Science Cheat Sheet for Business Leaders This cheat sheet guides you through the basics of how data science can help your business, including building your data science team and the common steps in the data science workflow.
How To Manage AI Projects Effectively
blog We analyze how AI is rapidly reshaping the discipline of project management, helping business leaders automate tasks, manage their resources effectively, and make better, data-driven decisions. Javier Canales Luna 8 min blog Discover a comprehensive roadmap to scaling AI in your organization. Learn how to align AI with your business goals and create a robust data infrastructure to ensure your AI initiatives deliver lasting value and transformative growth.
Saturn Cloud vs Google Colab for Data Science and Machine Learning
An illustration of the end-to-end data science pipeline without GPU, parallel programming and a DevOps team. Photo by amirali mirhashemian on Unsplash Ask any data scientist for the most common tool they use at work. Chances are, you will hear a lot about Jupyter notebook or Google Colab.
Achieving Organizational Data Fluency with IPTOP
blog Scaling a data strategy successfully is no small feat. Follow our methodology to lay a strong data foundation and scale an enterprise data program. blog DataCamp is hosting a monthly webinar in series in 2021 focused on scaling data science and building organization-wide data fluency with our IPTOP Framework. blog Scaling organization-wide data fluency training is not an easy task. Learn how to scale your data program and ensure the success of your digital transformation initiatives.
Google Data Analytics Professional Certificate: A Review
(I am not paid to promote this course. This is my personal opinion.) In Mar 2021, Google launched a Data Analytics Professional Certificate. This came at a perfect time as the supply lag behind the demand for analytics role, creating a shortfall of data analysts in the market. The demand for data analytics role has skyrocketed in recent years, causing an increase in the number of openings in analytical roles with a lucrative paycheck.
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