Best AI tools for< Python Data Scientist >

WiseData is an AI Assistant for Python Data Analytics designed to help Data Analysts and Data Scientists be 2X more productive. It offers features like data transformation with natural language, data visualization with natural language, and data transformation with SQL. WiseData ensures privacy by not sending analyzed data to its server and protects transmitted prompts and suggestions through encryption. It is a valuable tool for simplifying complex data analytics tasks and enhancing productivity.

Mito is a low-code data app infrastructure that allows users to edit spreadsheets and automatically generate Python code. It is designed to help analysts automate their repetitive Excel work and take automation into their own hands. Mito is a Jupyter extension and Streamlit component, so users don't need to set up any new infrastructure. It is easy to get started with Mito, simply install it using pip and start using it in Jupyter or Streamlit.

Deepnote is an AI-powered analytics and data science notebook platform designed for teams. It allows users to turn notebooks into powerful data apps and dashboards, combining Python, SQL, R, or even working without writing code at all. With Deepnote, users can query various data sources, generate code, explain code, and create interactive visualizations effortlessly. The platform offers features like collaborative workspaces, scheduling notebooks, deploying APIs, and integrating with popular data warehouses and databases. Deepnote prioritizes security and compliance, providing users with control over data access and encryption. It is loved by a community of data professionals and widely used in universities and by data analysts and scientists.

The website offers a Synthetic Data Generation platform with the highest accuracy for free. It provides detailed information on synthetic data, data anonymization, and features a Python Client for data generation. The platform ensures privacy and security, allowing users to create fully anonymous synthetic data from original data. It supports various AI/ML use cases, self-service analytics, testing & QA, and data sharing. The platform is designed for Enterprise organizations, offering scalability, privacy by design, and the world's most accurate synthetic data.

Giskard is a testing platform for AI models that helps protect companies against biases, performance, and security issues in AI models. It offers automated detection of performance, bias, and security issues, unifies AI testing practices, and ensures compliance with the EU AI Act. Giskard provides an open-source Python library for data scientists and an enterprise collaborative hub to control all AI risks in one place. It aims to address the shortcomings of current MLOps tools in handling AI risks and compliance.

Data Science Dojo is a globally recognized e-learning platform that offers programs in data science, data analytics, machine learning, and more. They provide comprehensive and hands-on training in various formats such as in-person, virtual instructor-led, and self-paced training. The focus is on helping students develop a think-business-first mindset to apply their data science skills effectively in real-world scenarios. With over 2500 enterprises trained, Data Science Dojo aims to make data science accessible to everyone.

Amazon SageMaker Python SDK is an open source library for training and deploying machine-learned models on Amazon SageMaker. With the SDK, you can train and deploy models using popular deep learning frameworks, algorithms provided by Amazon, or your own algorithms built into SageMaker-compatible Docker images.

Hex is a collaborative data workspace that provides a variety of tools for working with data, including queries, notebooks, reports, data apps, and AI. It is designed to be easy to use for people of all technical skill levels, and it integrates with a variety of other tools and services. Hex is a powerful tool for data exploration, analysis, and visualization.

LangChain is a framework for developing applications powered by large language models (LLMs). It simplifies every stage of the LLM application lifecycle, including development, productionization, and deployment. LangChain consists of open-source libraries such as langchain-core, langchain-community, and partner packages. It also includes LangGraph for building stateful agents and LangSmith for debugging and monitoring LLM applications.

Quadratic is an infinite spreadsheet with Python, SQL, and AI. It combines the familiarity of a spreadsheet with the power of code, allowing users to analyze data, write code, and create visualizations in a single environment. With built-in Python library support, users can bring open source tools directly to their spreadsheets. Quadratic also features real-time collaboration, allowing multiple users to work on the same spreadsheet simultaneously. Additionally, Quadratic is built for speed and performance, utilizing Web Assembly and WebGL to deliver a smooth and responsive experience.

Hal9 is an AI coworker creation platform that allows organizations, data teams, and developers to effortlessly build custom AI coworkers with any level of complexity. It provides a secure and customizable model-agnostic AI coworker solution that accelerates the development of AI applications by saving significant engineering time. Hal9 enables users to leverage the best generative AI models, connect their data securely, and start building enterprise-ready AI applications with the necessary engineering components. The platform aims to empower users to leverage AI technology effectively and efficiently in their projects.

DataCamp is an online learning platform that offers courses in data science, AI, and machine learning. The platform provides interactive exercises, short videos, and hands-on projects to help learners develop the skills they need to succeed in the field. DataCamp also offers a variety of resources for businesses, including team training, custom content development, and data science consulting.

PandasAI is an open-source AI tool designed for conversational data analysis. It allows users to ask questions in natural language to their enterprise data and receive real-time data insights. The tool is integrated with various data sources and offers enhanced analytics, actionable insights, detailed reports, and visual data representation. PandasAI aims to democratize data analysis for better decision-making, offering enterprise solutions for stable and scalable internal data analysis. Users can also fine-tune models, ingest universal data, structure data automatically, augment datasets, extract data from websites, and forecast trends using AI.

Scikit-learn is a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.

Google Colab is a free Jupyter notebook environment that runs in the cloud. It allows you to write and execute Python code without having to install any software or set up a local environment. Colab notebooks are shareable, so you can easily collaborate with others on projects.

NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and high-level mathematical functions to perform operations on these arrays. It is the fundamental package for scientific computing with Python and is used in a wide range of applications, including data science, machine learning, and image processing. NumPy is open source and distributed under a liberal BSD license, and is developed and maintained publicly on GitHub by a vibrant, responsive, and diverse community.

Walter Shields Data Academy is an AI-powered platform offering premium training in SQL, Python, and Excel. With over 200,000 learners, it provides curated courses from bestselling books and LinkedIn Learning. The academy aims to revolutionize data expertise and empower individuals to excel in data analysis and AI technologies.

Streamlit is a web application framework that allows users to create interactive web applications with Python. It enables data scientists and developers to easily build and share data-driven applications. With Streamlit, users can create interactive visualizations, dashboards, and machine learning models without the need for extensive web development knowledge. The platform simplifies the process of turning data scripts into shareable web apps, making it a valuable tool for data science projects, prototyping, and showcasing insights.

NLTK (Natural Language Toolkit) is a leading platform for building Python programs to work with human language data. It provides easy-to-use interfaces to over 50 corpora and lexical resources such as WordNet, along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning, wrappers for industrial-strength NLP libraries, and an active discussion forum. Thanks to a hands-on guide introducing programming fundamentals alongside topics in computational linguistics, plus comprehensive API documentation, NLTK is suitable for linguists, engineers, students, educators, researchers, and industry users alike.

Pickl.AI is a platform offering professional certification courses in Data Science, empowering individuals to enhance their career prospects. The platform provides a range of courses tailored for beginners, students, and professionals, covering topics such as Machine Learning, Python programming, and Data Analytics. Pickl.AI aims to equip learners with industry-relevant skills and expertise through expert-led lectures, real projects, and doubt-clearing sessions. The platform also offers job guarantee programs and short-term courses to cater to diverse learning needs.

The LLM course is divided into three parts: 1. 🧩 **LLM Fundamentals** covers essential knowledge about mathematics, Python, and neural networks. 2. 🧑‍🔬 **The LLM Scientist** focuses on building the best possible LLMs using the latest techniques. 3. 👷 **The LLM Engineer** focuses on creating LLM-based applications and deploying them. For an interactive version of this course, I created two **LLM assistants** that will answer questions and test your knowledge in a personalized way: * 🤗 **HuggingChat Assistant**: Free version using Mixtral-8x7B. * 🤖 **ChatGPT Assistant**: Requires a premium account. ## 📝 Notebooks A list of notebooks and articles related to large language models. ### Tools | Notebook | Description | Notebook | |----------|-------------|----------| | 🧐 LLM AutoEval | Automatically evaluate your LLMs using RunPod |  | | 🥱 LazyMergekit | Easily merge models using MergeKit in one click. |  | | 🦎 LazyAxolotl | Fine-tune models in the cloud using Axolotl in one click. |  | | ⚡ AutoQuant | Quantize LLMs in GGUF, GPTQ, EXL2, AWQ, and HQQ formats in one click. |  | | 🌳 Model Family Tree | Visualize the family tree of merged models. |  | | 🚀 ZeroSpace | Automatically create a Gradio chat interface using a free ZeroGPU. |  |

The AI Scientist is a comprehensive system for fully automatic scientific discovery, enabling Foundation Models to perform research independently. It aims to tackle the grand challenge of developing agents capable of conducting scientific research and discovering new knowledge. The tool generates papers on various topics using Large Language Models (LLMs) and provides a platform for exploring new research ideas. Users can create their own templates for specific areas of study and run experiments to generate papers. However, caution is advised as the codebase executes LLM-written code, which may pose risks such as the use of potentially dangerous packages and web access.

DataChain is an open-source Python library for processing and curating unstructured data at scale. It supports AI-driven data curation using local ML models and LLM APIs, handles large datasets, and is Python-friendly with Pydantic objects. It excels at optimizing batch operations and is designed for offline data processing, curation, and ETL. Typical use cases include Computer Vision data curation, LLM analytics, and validation.

PythonDataScienceFullThrottle is a comprehensive repository containing various Python scripts, libraries, and tools for data science enthusiasts. It includes a wide range of functionalities such as data preprocessing, visualization, machine learning algorithms, and statistical analysis. The repository aims to provide a one-stop solution for individuals looking to dive deep into the world of data science using Python.

Taipy is an open-source Python library for easy, end-to-end application development, featuring what-if analyses, smart pipeline execution, built-in scheduling, and deployment tools.

Pathway is a Python data processing framework for analytics and AI pipelines over data streams. It's the ideal solution for real-time processing use cases like streaming ETL or RAG pipelines for unstructured data. Pathway comes with an **easy-to-use Python API** , allowing you to seamlessly integrate your favorite Python ML libraries. Pathway code is versatile and robust: **you can use it in both development and production environments, handling both batch and streaming data effectively**. The same code can be used for local development, CI/CD tests, running batch jobs, handling stream replays, and processing data streams. Pathway is powered by a **scalable Rust engine** based on Differential Dataflow and performs incremental computation. Your Pathway code, despite being written in Python, is run by the Rust engine, enabling multithreading, multiprocessing, and distributed computations. All the pipeline is kept in memory and can be easily deployed with **Docker and Kubernetes**. You can install Pathway with pip: `pip install -U pathway` For any questions, you will find the community and team behind the project on Discord.

Awesome MLOps is a curated list of tools related to Machine Learning Operations, covering areas such as AutoML, CI/CD for Machine Learning, Data Cataloging, Data Enrichment, Data Exploration, Data Management, Data Processing, Data Validation, Data Visualization, Drift Detection, Feature Engineering, Feature Store, Hyperparameter Tuning, Knowledge Sharing, Machine Learning Platforms, Model Fairness and Privacy, Model Interpretability, Model Lifecycle, Model Serving, Model Testing & Validation, Optimization Tools, Simplification Tools, Visual Analysis and Debugging, and Workflow Tools. The repository provides a comprehensive collection of tools and resources for individuals and teams working in the field of MLOps.

This repository contains examples, applications, starter code, & tutorials to help you kickstart your GenAI projects. * These are built using LanceDB, a free, open-source, serverless vectorDB that **requires no setup**. * It **integrates into python data ecosystem** so you can simply start using these in your existing data pipelines in pandas, arrow, pydantic etc. * LanceDB has **native Typescript SDK** using which you can **run vector search** in serverless functions! This repository is divided into 3 sections: - Examples - Get right into the code with minimal introduction, aimed at getting you from an idea to PoC within minutes! - Applications - Ready to use Python and web apps using applied LLMs, VectorDB and GenAI tools - Tutorials - A curated list of tutorials, blogs, Colabs and courses to get you started with GenAI in greater depth.

LLMOps Duke AIPI is a course focused on operationalizing Large Language Models, teaching methodologies for developing applications using software development best practices with large language models. The course covers various topics such as generative AI concepts, setting up development environments, interacting with large language models, using local large language models, applied solutions with LLMs, extensibility using plugins and functions, retrieval augmented generation, introduction to Python web frameworks for APIs, DevOps principles, deploying machine learning APIs, LLM platforms, and final presentations. Students will learn to build, share, and present portfolios using Github, YouTube, and Linkedin, as well as develop non-linear life-long learning skills. Prerequisites include basic Linux and programming skills, with coursework available in Python or Rust. Additional resources and references are provided for further learning and exploration.

The 'machine-learning-research' repository is a comprehensive collection of resources related to mathematics, machine learning, deep learning, artificial intelligence, data science, and various scientific fields. It includes materials such as courses, tutorials, books, podcasts, communities, online courses, papers, and dissertations. The repository covers topics ranging from fundamental math skills to advanced machine learning concepts, with a focus on applications in healthcare, genetics, computational biology, precision health, and AI in science. It serves as a valuable resource for individuals interested in learning and researching in the fields of machine learning and related disciplines.

LLM101n is a course focused on building a Storyteller AI Large Language Model (LLM) from scratch in Python, C, and CUDA. The course covers various topics such as language modeling, machine learning, attention mechanisms, tokenization, optimization, device usage, precision training, distributed optimization, datasets, inference, finetuning, deployment, and multimodal applications. Participants will gain a deep understanding of AI, LLMs, and deep learning through hands-on projects and practical examples.

Sycamore is a conversational search and analytics platform for complex unstructured data, such as documents, presentations, transcripts, embedded tables, and internal knowledge repositories. It retrieves and synthesizes high-quality answers through bringing AI to data preparation, indexing, and retrieval. Sycamore makes it easy to prepare unstructured data for search and analytics, providing a toolkit for data cleaning, information extraction, enrichment, summarization, and generation of vector embeddings that encapsulate the semantics of data. Sycamore uses your choice of generative AI models to make these operations simple and effective, and it enables quick experimentation and iteration. Additionally, Sycamore uses OpenSearch for indexing, enabling hybrid (vector + keyword) search, retrieval-augmented generation (RAG) pipelining, filtering, analytical functions, conversational memory, and other features to improve information retrieval.

The 'meet-libai' project aims to promote and popularize the cultural heritage of the Chinese poet Li Bai by constructing a knowledge graph of Li Bai and training a professional AI intelligent body using large models. The project includes features such as data preprocessing, knowledge graph construction, question-answering system development, and visualization exploration of the graph structure. It also provides code implementations for large models and RAG retrieval enhancement.

Steel-LLM is a project to pre-train a large Chinese language model from scratch using over 1T of data to achieve a parameter size of around 1B, similar to TinyLlama. The project aims to share the entire process including data collection, data processing, pre-training framework selection, model design, and open-source all the code. The goal is to enable reproducibility of the work even with limited resources. The name 'Steel' is inspired by a band '万能青年旅店' and signifies the desire to create a strong model despite limited conditions. The project involves continuous data collection of various cultural elements, trivia, lyrics, niche literature, and personal secrets to train the LLM. The ultimate aim is to fill the model with diverse data and leave room for individual input, fostering collaboration among users.

LakeSoul is a cloud-native Lakehouse framework that supports scalable metadata management, ACID transactions, efficient and flexible upsert operation, schema evolution, and unified streaming & batch processing. It supports multiple computing engines like Spark, Flink, Presto, and PyTorch, and computing modes such as batch, stream, MPP, and AI. LakeSoul scales metadata management and achieves ACID control by using PostgreSQL. It provides features like automatic compaction, table lifecycle maintenance, redundant data cleaning, and permission isolation for metadata.

Phidata is a framework for building AI Assistants with memory, knowledge, and tools. It enables LLMs to have long-term conversations by storing chat history in a database, provides them with business context by storing information in a vector database, and enables them to take actions like pulling data from an API, sending emails, or querying a database. Memory and knowledge make LLMs smarter, while tools make them autonomous.

LLM Foundry is a codebase for training, finetuning, evaluating, and deploying LLMs for inference with Composer and the MosaicML platform. It is designed to be easy-to-use, efficient _and_ flexible, enabling rapid experimentation with the latest techniques. You'll find in this repo: * `llmfoundry/` - source code for models, datasets, callbacks, utilities, etc. * `scripts/` - scripts to run LLM workloads * `data_prep/` - convert text data from original sources to StreamingDataset format * `train/` - train or finetune HuggingFace and MPT models from 125M - 70B parameters * `train/benchmarking` - profile training throughput and MFU * `inference/` - convert models to HuggingFace or ONNX format, and generate responses * `inference/benchmarking` - profile inference latency and throughput * `eval/` - evaluate LLMs on academic (or custom) in-context-learning tasks * `mcli/` - launch any of these workloads using MCLI and the MosaicML platform * `TUTORIAL.md` - a deeper dive into the repo, example workflows, and FAQs

This repository contains the code that Defog uses for the evaluation of generated SQL. It's based off the schema from the Spider, but with a new set of hand-selected questions and queries grouped by query category. The testing procedure involves generating a SQL query, running both the 'gold' query and the generated query on their respective database to obtain dataframes with the results, comparing the dataframes using an 'exact' and a 'subset' match, logging these alongside other metrics of interest, and aggregating the results for reporting. The repository provides comprehensive instructions for installing dependencies, starting a Postgres instance, importing data into Postgres, importing data into Snowflake, using private data, implementing a query generator, and running the test with different runners.

PIXIU is a project designed to support the development, fine-tuning, and evaluation of Large Language Models (LLMs) in the financial domain. It includes components like FinBen, a Financial Language Understanding and Prediction Evaluation Benchmark, FIT, a Financial Instruction Dataset, and FinMA, a Financial Large Language Model. The project provides open resources, multi-task and multi-modal financial data, and diverse financial tasks for training and evaluation. It aims to encourage open research and transparency in the financial NLP field.

LLM-PowerHouse is a comprehensive and curated guide designed to empower developers, researchers, and enthusiasts to harness the true capabilities of Large Language Models (LLMs) and build intelligent applications that push the boundaries of natural language understanding. This GitHub repository provides in-depth articles, codebase mastery, LLM PlayLab, and resources for cost analysis and network visualization. It covers various aspects of LLMs, including NLP, models, training, evaluation metrics, open LLMs, and more. The repository also includes a collection of code examples and tutorials to help users build and deploy LLM-based applications.

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