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By Abid Ali Awan , KDnuggets Assistant Editor on July 1, 2025 in DataScience Image by Author | Canva Awesome lists are some of the most popular repositories on GitHub, often attracting thousands of stars from the community. In this article, we will review some of the most popular and impressive lists for datascience.
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Every data scientist has been there: downsampling a dataset because it won’t fit into memory or hacking together a way to let a business user interact with a machinelearning model. MachineLearning in your Spreadsheets BQML training and prediction from a Google Sheet Many data conversations start and end in a spreadsheet.
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By Abid Ali Awan , KDnuggets Assistant Editor on July 14, 2025 in Python Image by Author | Canva Despite the rapid advancements in datascience, many universities and institutions still rely heavily on tools like Excel and SPSS for statistical analysis and reporting. Learn more: [link] 6. import statistics as stats 2.
By Bala Priya C , KDnuggets Contributing Editor & Technical Content Specialist on July 28, 2025 in MachineLearning Image by Author | Ideogram # Introduction From your email spam filter to music recommendations, machinelearning algorithms power everything. Let’s begin! # She enjoys reading, writing, coding, and coffee!
Learn how to build your own agentic application and start using AI the right way. Abid Ali Awan ( @1abidaliawan ) is a certified data scientist professional who loves building machinelearning models. Abid holds a Masters degree in technology management and a bachelors degree in telecommunication engineering.
She likes working at the intersection of math, programming, datascience, and content creation. Her areas of interest and expertise include DevOps, datascience, and natural language processing. More On This Topic How To Overcome The Fear of Math and Learn Math For DataScience How Much Math Do You Need in DataScience?
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By Vinod Chugani on June 27, 2025 in DataScience Image by Author | ChatGPT Introduction Creating interactive web-based data dashboards in Python is easier than ever when you combine the strengths of Streamlit , Pandas , and Plotly.
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By, Avi Chawla - highly passionate about approaching and explaining datascience problems with intuition. Avi has been working in the field of datascience and machinelearning for over 6 years, both across academia and industry.
By Iván Palomares Carrascosa , KDnuggets Technical Content Specialist on July 4, 2025 in Python Image by Author | Ideogram Principal component analysis (PCA) is one of the most popular techniques for reducing the dimensionality of high-dimensional data. He trains and guides others in harnessing AI in the real world.
While most people associate workflow automation with business processes like email marketing or customer support, n8n can also assist with automating datascience tasks that traditionally require custom scripting. Most importantly, this approach bridges the gap between datascience expertise and organizational accessibility.
As managing editor of KDnuggets & Statology , and contributing editor at MachineLearning Mastery , Matthew aims to make complex datascience concepts accessible. His professional interests include natural language processing, language models, machinelearning algorithms, and exploring emerging AI.
He graduated in physics engineering and is currently working in the datascience field applied to human mobility. He is a part-time content creator focused on datascience and technology. You can go check the full code on the following GitHub repository. Josep Ferrer is an analytics engineer from Barcelona.
Abid Ali Awan ( @1abidaliawan ) is a certified data scientist professional who loves building machinelearning models. Currently, he is focusing on content creation and writing technical blogs on machinelearning and datascience technologies.
By Cornellius Yudha Wijaya , KDnuggets Technical Content Specialist on June 18, 2025 in DataScience Image by Author As a data scientist, Jupyter Notebook has become one of the first platforms we learn to use, as it allows for easier data manipulation compared to standard programming IDEs.
Blog Top Posts About Topics AI Career Advice Computer Vision Data Engineering DataScience Language Models MachineLearning MLOps NLP Programming Python SQL Datasets Events Resources Cheat Sheets Recommendations Tech Briefs Advertise Join Newsletter AI Agents in Analytics Workflows: Too Early or Already Behind?
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Step 1: Choose a Topic To we will start by selecting a topic within the fields of AI, machinelearning, or datascience. Jayita Gulati is a machinelearning enthusiast and technical writer driven by her passion for building machinelearning models.
Kanwal Mehreen Kanwal is a machinelearning engineer and a technical writer with a profound passion for datascience and the intersection of AI with medicine. Remember, the goal isn’t to eliminate all loops from your code. It’s to use the right tool for the job.
By Kanwal Mehreen , KDnuggets Technical Editor & Content Specialist on July 28, 2025 in DataScience Image by Author | Canva # Introduction I understand that with the pace at which datascience is growing, it’s getting harder for data scientists to keep up with all the new technologies, demands, and trends.
Here is the link to the data project we’ll be using in this article. It’s a data project from Uber called Partner’s Business Modeling. Uber used this data project in the recruitment process for the datascience positions, and you will be asked to analyze the data for two different scenarios.
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Using these, youll likely be able to quickly process API responses, transform data between different formats, and extract useful info from complex JSON structures. She likes working at the intersection of math, programming, datascience, and content creation. Bala Priya C is a developer and technical writer from India.
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Cornellius Yudha Wijaya is a datascience assistant manager and data writer. While working full-time at Allianz Indonesia, he loves to share Python and data tips via social media and writing media. Cornellius writes on a variety of AI and machinelearning topics. I hope this has helped!
Conclusion Modal is an interesting platform, and I am learning more about it every day. It is a general-purpose platform, meaning you can use it for simple Python applications as well as for machinelearning training and deployments. In short, it is not limited to just serving endpoints. The rest is handled by the Modal cloud.
Our customers are telling us that they are seeing their analytics and AI workloads increasingly converge around a lot of the same data, and this is changing how they are using analytics tools with their data. They aren’t using analytics and AI tools in isolation. The tools to transform your business are here.
By Cornellius Yudha Wijaya , KDnuggets Technical Content Specialist on June 10, 2025 in Python Image by Author | Ideogram Python has become a primary tool for many data professionals for data manipulation and machinelearning purposes because of how easy it is for people to use. I hope this has helped!
She likes working at the intersection of math, programming, datascience, and content creation. Her areas of interest and expertise include DevOps, datascience, and natural language processing. So yeah, happy automating! Bala Priya C is a developer and technical writer from India.
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