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Start here with a simple Python pipeline that covers the essentials. Start here with a simple Python pipeline that covers the essentials. Lets build an ETL pipeline that takes messy data and turns it into something actually useful. She likes working at the intersection of math, programming, datascience, and content creation.
By Bala Priya C , KDnuggets Contributing Editor & Technical Content Specialist on July 16, 2025 in Python Image by Author | Ideogram Pythons expressive syntax along with its built-in modules and external libraries make it possible to perform complex mathematical and statistical operations with remarkably concise code.
With Connected Sheets, a business user could open a Sheet, enter data for a new property (square footage, number of bedrooms, location), and a formula can call a BQML model to return a price estimate. No Python or API wrangling needed - just a Sheets formula calling a model. Colab notebooks also have a built-in DataScience Agent.
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By Bala Priya C , KDnuggets Contributing Editor & Technical Content Specialist on June 24, 2025 in Python Image by Author | Ideogram Data is messy. Instead of writing the same cleaning code repeatedly, a well-designed pipeline saves time and ensures consistency across your datascience projects. Happy data cleaning!
Blog Top Posts About Topics AI Career Advice Computer Vision DataEngineeringDataScience Language Models Machine Learning MLOps NLP Programming Python SQL Datasets Events Resources Cheat Sheets Recommendations Tech Briefs Advertise Join Newsletter Go vs. Python for Modern Data Workflows: Need Help Deciding?
By Josep Ferrer , KDnuggets AI Content Specialist on June 10, 2025 in Python Image by Author DuckDB is a fast, in-process analytical database designed for modern data analysis. Its tight integration with Python and R makes it ideal for interactive data analysis. Let’s dive in! What Is DuckDB?
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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These clients can include Python frameworks, desktop chatbots, VSCode extensions, agentic code editors, and CLI tools like Claude Code. Currently, he is focusing on content creation and writing technical blogs on machine learning and datascience technologies.
Blog Top Posts About Topics AI Career Advice Computer Vision DataEngineeringDataScience Language Models Machine Learning MLOps NLP Programming Python SQL Datasets Events Resources Cheat Sheets Recommendations Tech Briefs Advertise Join Newsletter 5 Fun Python Projects for Absolute Beginners Bored of theory?
By Shamima Sultana on June 19, 2025 in DataScience Image by Editor | Midjourney While Python-based tools like Streamlit are popular for creating data dashboards, Excel remains one of the most accessible and powerful platforms for building interactive data visualizations.
random_state=42)[:2]) os.makedirs("model", exist_ok=True) joblib.dump(clf, "model/iris_model.pkl") print("✅ Model saved to model/iris_model.pkl") This script loads the data, splits it, trains the model, and saves it using joblib. Run it once to generate the model file: python model/train_model.py Create a file called train_model.py
What if you could paste any CSV URL and get a professional data quality report in under 30 seconds? No Python environment setup, no manual coding, no switching between tools. Unlike writing standalone Python scripts, n8n workflows are visual, reusable, and easy to modify. Which columns are problematic?
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By Bala Priya C , KDnuggets Contributing Editor & Technical Content Specialist on July 22, 2025 in Python Image by Author | Ideogram # Introduction Most applications heavily rely on JSON for data exchange, configuration management, and API communication. This double-loop structure efficiently handles variable-length nested arrays.
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. import statistics as stats 2. import statistics as stats 2.
By Matthew Mayo , KDnuggets Managing Editor on July 17, 2025 in Python Image by Editor | ChatGPT Introduction Pythons standard library is extensive, offering a wide range of modules to perform common tasks efficiently. Remembering Insertion Order with OrderedDict Before Python 3.7, This is especially useful for grouping items.
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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 machine learning for over 6 years, both across academia and industry.
The world’s leading publication for datascience, AI, and ML professionals. In this article, I’ll walk you through a simple but powerful Python automation that selects the best machine learning models for your dataset automatically. Just plug in your data and let Python do the rest. Why Automate ML Model Selection?
Abid Ali Awan ( @1abidaliawan ) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and datascience technologies.
Essential Prerequisites Building generative AI applications requires comfort with Python programming and basic machine learning concepts, but you dont need deep expertise in neural network architecture or advanced mathematics. Born in India and raised in Japan, Vinod brings a global perspective to datascience and machine learning education.
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Data Project - Uber Business Modeling We will use it with Jupyter Notebook, combining it with Python for data analysis. To make things more exciting, we will work on a real-life data project. Here is the link to the data project we’ll be using in this article. Let’s get started!
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Blog Top Posts About Topics AI Career Advice Computer Vision DataEngineeringDataScience Language Models Machine Learning MLOps NLP Programming Python SQL Datasets Events Resources Cheat Sheets Recommendations Tech Briefs Advertise Join Newsletter 10 FREE AI Tools That’ll Save You 10+ Hours a Week No tech skills needed.
By Jayita Gulati on July 16, 2025 in Machine Learning Image by Editor In datascience and machine learning, raw data is rarely suitable for direct consumption by algorithms. She holds a Masters degree in Computer Science from the University of Liverpool.
py # (Optional) to mark directory as Python package You can leave the __init.py__ file empty, as its main purpose is simply to indicate that this directory should be treated as a Python package. Tools Required(requirements.txt) The necessary libraries required are: PyPDF : A pure Python library to read and write PDF files.
Step 1: Choose a Topic To we will start by selecting a topic within the fields of AI, machine learning, or datascience. She holds a Masters degree in Computer Science from the University of Liverpool. Overview of the Workflow To make the most of modern AI tools, we will combine deep research with interactive note-taking.
For example, here is the Python code to use Google’s Gemini model with LiteLLM. With modest resources required for Python library installation, we can run LiteLLM on our local laptop or host it in a containerized deployment with Docker without a need for complex additional configuration.
Blog Top Posts About Topics AI Career Advice Computer Vision DataEngineeringDataScience Language Models Machine Learning MLOps NLP Programming Python SQL Datasets Events Resources Cheat Sheets Recommendations Tech Briefs Advertise Join Newsletter 5 Fun Generative AI Projects for Absolute Beginners New to generative AI?
With Modal, you can configure your Python app, including system requirements like GPUs, Docker images, and Python dependencies, and then deploy it to the cloud with a single command. It is an ideal platform for beginners, data scientists, and non-software engineering professionals who want to avoid dealing with cloud infrastructure.
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