PyData Global 2023
2023-12-06..2023-12-08
-- online --
[PyData Global 2020, PyData Global 2022, PyData Global 2023, PyData Global 2024]
who it's for
data scientists, machine learning engineers, data engineers, analytics engineers, platform engineers, open source maintainers, data analysts
topics
Keras 3 with JAX backend; Pandas 2 with PyArrow backend; Polars dataframe performance; DuckDB in-process SQL analytics; sktime time series forecasting; Apache Arrow data interchange; retrieval augmented generation (RAG); vector search with LanceDB; LangChain and Elasticsearch workflows; LoRA fine tuning for LLMs
| title | author | length |
|---|---|---|
| Keras (3) for the Curious and Creative | Ngesa Marvin | 01h 14m |
Shows concrete demos and code: Keras 3 with Jax backend for a quick object‑detection build, swapping backends between JAX TensorFlow and PyTorch, and importing… | ||
| Predictive survival analysis with scikit-learn, scikit-survival and lifelines | Olivier Grisel | 01h 23m |
Predictive survival methods get specific: code runs in a JupyterLite notebook, the demo uses scikit-learn preprocessing, the lifelines Kaplan‑Meier estimator… | ||
| sktime - python toolbox for time series | Heidrich, Kiraly, Ray | 01h 30m |
sktime is scikit-learn compatible and claims to be easily extensible and interoperable with the PyData stack. the tutorial shows runnable notebooks (including… | ||
| Using LLMs to improve your Search Engine | Pattaniyil, Ravi, Zengin | 01h 30m |
Covers concrete claims and demos: using LLMs to extract query intent, judge query–item relevance, generate user queries from product titles, and fine‑tune… | ||
| When Design Thinking Meets Opensource | Ramona Sartipi | 46m |
Claims that adding basic design methods increases open-source usability and shows how: a one-hour workshop runs empathy map and As‑is/To‑Be scenario mapping… | ||
| Who needs ChatGPT? Rock solid AI pipelines with Hugging Face and Kedro | Juan Luis Cano Rodríguez | 01h 22m |
Shows a concrete pipeline that extracts ~200 Mastodon posts into Minio/Delta, cleans text with Polars, loads Hugging Face models (a ~1GB summarizer and a… | ||
| All Them Data Engines: Data Munging with Python circa 2023 | Shaurya Agarwal | 01h 55m |
Compares concrete tools (Pandas, NumPy, Spark, Dask, Polars, Arrow) on the MovieLens data and shows runnable notebooks and Colab links in a GitHub repo… | ||
| From raw data to interactive data app in an hour | Vino Duraisamy | 01h 18m |
Claims an end-to-end data pipeline and interactive app from raw CSV to deployed model inside Snowflake using Snowpark ML modeling API and model registry… | ||
| An Introduction to Pandas 2, Polars, and DuckDB | Matt Harrison | 01h 22m |
Shows concrete tradeoffs and outcomes: Pandas 2 optionally uses the PyArrow backend, yields ~50–70% string memory savings and copy‑on‑write behavior, and… | ||
| Btune: Making Compression Better | Francesc Alted | 01h 09m |
Btune is a dynamic plugin for Blosc2 that finds codec and parameter combinations by running a Btune genetic mode search and optional neural‑network inference… | ||
| Building an Interactive Network Graph to Understand Communities | Lucas Durand | 01h 33m |
Claims that an HR org chart can be improved by Clustering Attributed Graphs and a working toolkit that generates synthetic people, computes graph layouts, and… | ||
| Building Contextual ChatBot using LLMs, Vector Databases & Python | Nabanita Roy | 01h 27m |
Demo shows a runnable Python pipeline that turns PDFs into chunks, creates embeddings with text-embedding-ada-002, stores them in ChromaDB, and queries via a… | ||
| Full-stack Machine Learning and Generative AI for Data Scientists | Hugo Bowne-Anderson | 01h 29m |
Shows concrete pipeline pieces: uses metaflow to write versioned flows, decorates steps to run on Kubernetes and deploys with Argo workflows, indexes docs into… | ||
| HPC in the cloud | Eddie | 01h 21m |
Claim: modern cloud stacks can run true HPC-style distributed training using existing MPI tooling and container schedulers, not just vague “examples.” The… | ||
| Improving Open Data Quality using Python | Cesar Garcia | 01h 29m |
Uses Great Expectations with pandas and sweetviz to validate Toronto open data bicycle thefts, not just talk about cleaning. Finds ~34k rows with ~28k missing… | ||
| Compute anything with Metaflow | Ville Tuulos | 28m |
Metaflow claims to let Python workflows pick the actual hardware and run reliably: cloud or on‑prem, packaged dependencies, Kubernetes scheduling and GPU… | ||
| Intro to ML: How to Prevent Data Leakage and Build Efficient Workflows | Cainã Max Couto da Silva | 01h 49m |
Shows exactly how to stop leakage by learning preprocessing only on training folds and reusing those parameters for test and production, comparing a slow… | ||
| Accelerating fuzzy document deduplication to improve LLM training w/ RAPIDS & Dask | Jacob Tomlinson | 25m |
NVIDIA NeMo Data Curator uses RAPIDS and Dask to scale fuzzy deduplication of terabytes of text by bucketing with MinHash, expanding pairs into a Dask… | ||
| API development for data analysts/scientists with FastApi | Sara Iris Garcia | 27m |
Claims that a working web API is easy for non-developers and shows a minimal FastApi app that returns JSON, a parameterized GET endpoint, and a POST to create… | ||
| Arrow revolution in pandas and Dask | Patrick Hoefler, Matthew Rocklin | 28m |
Claims are concrete: Arrow-backed strings cut memory (example: three string columns drop ~220 GB to ~100 GB), copy and right turns defensive copies into a fast… | ||
| Blazing fast I/O of data in the cloud with Daft Dataframes | Jay Chia | 30m |
Daft (www.getdaft.io) gives a concrete end-to-end claim: a Rust I/O layer with a Python API that actually loads massive numbers of cloud files fast. Benchmarks… | ||
| Blosc2: Fast And Flexible Handling Of N-Dimensional and Sparse Datasets | Francesc Alted | 22m |
Blosc2 is a C and Python library plus a simple container format that claims up to 5–8× faster slicing via a double data partition and extreme compression (an 8… | ||
| Bridging Classic ML Pipelines with the World of LLMs | Elijah ben Izzy, Stefan Krawczyk | 30m |
Claims that standard MLOps patterns map directly onto LLM workflows and that a DAG-as-code approach is sufficient to unify them. Demonstrates the open source… | ||
| Build a Data Visualization App For Your Phone | Russell Keith-Magee | 27m |
Claims that a phone app can be written and run with familiar Python data tools using the BeeWare suite; it actually uses Briefcase to package and Toga +… | ||
| Build AI-powered data pipeline without vector databases | Bobur Umurzokov | 29m |
Claims a working alternative to vector DBs: a Python open-source llm application library plus pathway to build a real-time data pipeline that indexes in… | ||
| Build & deploy a Snowflake Native Application using Python | Gilberto Hernandez | 01h 12m |
Claims a full Snowflake native app that runs next to the data and is packaged, listed, and monetized; it uses 90–95% Python, a streamlit frontend, Snowpark… | ||
| Creating Interactive, Animated Reports in Streamlit with ipyvizzu | Blackwood, Vidos | 28m |
Demo shows Streamlit + ipyvizzu animate method driving interactive, animated charts where controls update filters, swap axes, and produce a downloadable… | ||
| Building Learning to Rank models for search using LLMs | Sujit Pal | 29m |
LLMs generate binary and listwise relevance judgments (Anthropic Cloud prompts) that train supervised LTR models (pointwise regression, ranknet, LambdaRank… | ||
| But what is a Gaussian process? Regression while knowing how certain you are | Nguyen Hong Quan | 31m |
Defines a Gaussian process as a predictive distribution that returns a mean and variance not a single point estimate and derives posteriors by conditioning a… | ||
| Collaborate with your team using data science notebooks | Megan Lieu | 28m |
Claim: collaborative notebooks (deepnote) let teams go from siloed code to shared reproducible work. Demo shows deepnote combining SQL python R and no code… | ||
| Get the best from your scikit-learn classifier | Guillaume Lemaitre | 28m |
Scikit-learn adds a meta-estimator that fits classifiers for calibrated probabilities then tunes the decision threshold to directly maximize the chosen metric… | ||
| cudf.pandas: The Zero Code Change GPU Accelerator for Pandas | Ashwin Trikuta Srinath | 29m |
Claim: run existing pandas code on GPU with zero code changes by swapping in a proxy that routes supported ops to cuDF/CUDA and falls back to pandas when… | ||
| Customizing and Evaluating LLMs, an Ops Perspective | Dean Pleban | 29m |
Clear claim: customizing and evaluating LLMs is messy but solvable with concrete steps. Practical wins include a fraud‑detection gullible agent deployed in… | ||
| Data Harvest: Unlocking Insights with Python Web Scraping | Yuliia Barabash | 28m |
Builds a working apartment scraper with Scrapy that crawls paginated listings, extracts name price size and rooms, computes price per square meter, and writes… | ||
| Data persistence with consistency and performance in a truly serverless system | William Dealtry | 28m |
ArcticDB is a pure Python library that uses commodity object storage (S3/minio) and a few concrete techniques to get ACID‑like behavior without servers… | ||
| Data Tales from an Open Source Research Team | Casari, Maria Cruz, Vargas | 30m |
Team analyzes the GitHub Archive and runs queries over >1M events from 2022 (20M distinct actors) and finds bots are <1% of accounts but create ~24% of events… | ||
| Data-Driven F&B Delivery: Jahez as a Leading Example | Basel Alebdi, Nouf Alroqi | 22m |
Search queries generate 55% of orders so a pipeline batches one week of queries, sessionizes fast keystrokes, filters item searches, applies Levenshtein… | ||
| Enhancing your JupyterLab Developer Experience with Local LLMs and Code Snippets | Lamba | 30m |
Demo shows a JupyterLab extension that captures code, generates runnable snippets, and answers errors using local models. It uses Pieces extension inside… | ||
| Ensuring Runtime Reproducibility in Python | Pavithra Eswaramoorthy, Jaime Rodríguez-Guerra | 24m |
Claims a specific solution: conda-store (built on conda) produces pinned YAMLs, lockfiles, Docker images and archives, offers a UI, version control, role‑based… | ||
| Reconceptualizing Machine Learning for the Real-time World | Patrick Deziel, Prema Roman | 30m |
Claim: 87% of data science projects never reach production, so the talk demonstrates moving from batch to continuous learning with concrete tools. Uses River… | ||
| Extremes, outliers, and GOATS: on life in a lognormal world | Allen Downey | 30m |
Claim: lifetime achievement often follows a lognormal rather than a Gaussian, so the top performer can be far ahead (Wayne Gretzky ~2900 vs ~1900 points). Two… | ||
| Fighting Money Laundering with Python and Open Source Software | Gajendra Deshpande | 28m |
A reproducible Python pipeline ingests heterogeneous bank statements, normalizes columns with pandas, validates integrity with sha256 hashing, flags anomalies… | ||
| From RAGs to riches: Build an AI document interrogation app in 30 mins | Philip Meier | 27m |
Claims a working RAG app in 30 minutes and actually shows a runnable stack: Ragna (Python + REST), a Panel web UI, Chroma vector store (lens DB mentioned), and… | ||
| Keynote: Building Machine Learning Apps in Python with Gradio | Abubakar Abid | 57m |
Shows how to build full ML demos in a few Python lines using Gradio, with production features like a built‑in queue, programmatic API clients (Python/JS), and… | ||
| sktime: Trials and tribulations of a charitable, openly governed open source project | Franz Kiraly | 28m |
sktime presents itself as a comprehensive Python time‑series ML framework started five or six years ago and claims to be openly and transparently governed with… | ||
| Keynote: Federated Learning with Flower: AI's Next Frontier | Daniel Beutel | 57m |
Claims that federated learning is usable from Python by shipping models instead of data and that Flower lets developers implement it in under 20 lines of code… | ||
| Keynote: Building and Productionizing RAG | Jerry Liu | 01h 00m |
Concrete recipe: a Python library called llama index ingests documents by chunking and embedding into a vector DB, then runs retrieval plus LM synthesis —… | ||
| Kùzu: A Graph Database Management System for Python Graph Data Science | Guodong Jin | 31m |
Kùzu is an open‑source, disk‑backed graph DBMS built as an embedded library for Python graph data science that installs into notebooks and scripts. It ingests… | ||
| LanceDB: lightweight billion-scale vector search for multimodal AI | Chang She | 28m |
Claim: LanceDB is an embedded vector database that runs in-process like SQLite yet hits billion vectors on a laptop with millisecond searches. Implementation… | ||
| Let chatGPT decide and run the function! | Sonam Pankaj | 14m |
Claim: chatGPT can pick the right tool and return call-ready arguments via openai function calling. Demo uses GPT-3.5 (GPT‑4 noted as supported), registers… | ||
| Getting better at Pokémon using data, Python, and ChatGPT | Juan De Dios Santos | 22m |
Uses hypergeometric math and Python to quantify card odds and uses ChatGPT to summarize notes from gameplay; calculates 33.63% chance to draw a Battle Baby… | ||
| Hands-On Network Science | Farrelly, Gaba, Kalala Mutombo | 24m |
Network methods convert data into graphs and produce concrete results: using Forman-Ricci curvature and centralities on windowed correlation networks of NASDAQ… | ||
| High speed data from the Lakehouse to DataFrames with Apache Arrow | Jim Dowling | 28m |
Claims use Apache Arrow end-to-end with Pandas2, Polars and DuckDB for zero-copy transfers and replace JDBC/ODBC with Arrow Flight (and ADBC) protocols. A… | ||
| How can a learnt ML model unlearn something: Framework for "Machine Unlearning" | Saradindu Sengupta | 23m |
Survey of methods and limits for removing training influence without full retrain claims a working machine unlearning framework and demos verification by… | ||
| How I used Polars to build built functime, a next gen ML forecasting library | Luca Baggi | 28m |
Claims a concrete speedup: feature engineering runs 1-2 orders of magnitude faster using Polars; batch inference and scaling happen without PySpark. Presents… | ||
| How to build a data pipeline without data | Ruan Pretorius | 29m |
Synthetic data replaces absent production rows by generating realistic records with Python and writing SQL migrations; the demo builds a Postgres schema via… | ||
| IID Got You Down? Resample Time Series Like A Pro | Sankalp Gilda | 28m |
tsbootstrap is an open source Python library that implements moving/circular/stationary/non‑overlapping block bootstrap plus residual and Markov‑style… | ||
| Intake 2 | Martin Durant | 28m |
Intake 2 is a complete rewrite that replaces tangled legacy APIs with a simpler reader interface and removes unused features, yielding tiny reader definitions… | ||
| Introduction to Using Julia for Decentralization by a Quant | Martin Y. Xie | 29m |
Claims that Julia solves the two‑language problem and matches C++/Rust performance while staying high‑level lead to concrete details: Julia 1.9 with JIT… | ||
| Maximize GPU Utilization for Model Training | Lu Qiu | 28m |
Uses PyTorch + Alluxio + S3 to build a distributed caching layer colocated with Kubernetes training clusters, implements consistent-hash affinity caching and… | ||
| Modeling Extreme Events with PyMC | Jorn Mossel | 29m |
Models New York extreme rainfall with PyMC using annual maxima (about 50 annual maxima) and fits an extreme value distribution to estimate return levels; the… | ||
| More like this: monitoring recommender systems in production | Emeli Dral | 28m |
Practical checklist: measure a revenue KPI first, add real‑time proxy metrics (clicks, views, conversion) and batch compute offline metrics like Precision@K… | ||
| how compiler smarts can help improve the performance of numerical methods | Chris Rackauckas | 32m |
A general solver called NonlinearSolve.jl integrates symbolic passes and compiler analyses to auto-generate Jacobians, adjoints, and sparsity so users need not… | ||
| Optimize first, parallelize second: a better path to faster data processing | Itamar Turner Trauring | 31m |
If code is slow, the decisive details are what actually changes performance: the measured speedup numbers, the algorithmic complexity fixed, the architecture… | ||
| Build on-demand logistics apps with Python, OR-Tools, and DecisionOps | Ryan O'Neil | 30m |
Three concrete models get built, tested, and deployed: LAD regression for forecasting (robust L1 fit implemented as a linear program), mixed‑integer scheduling… | ||
| Pandas 2, Dask or Polars? Tackling larger data on a single machine | Ian Ozsvald, Giles Weaver | 30m |
Pandas 2 adds Arrow types and copy‑on‑write (to be default later) and on one dataset shows 82 million rows → 39 GB (numpy) vs 11 GB (Arrow) but mixed perf… | ||
| Polars and time zones: everything you need to know | Marco Edward Gorelli | 29m |
Claim: never handle time zones manually; use Polars and let it do the hard work. Shows concrete tools: dt.offset_by(4H) to add four hours and get 00:00 → 05:00… | ||
| Paradoxes in model training and evaluation under constraints | Malte Tichy | 25m |
Concrete claims: training on sales equals underforecasting, truncating to non‑stockout days induces hindsight bias, and censoring shifts a true mean demand of… | ||
| Creating Interactive, Animated Reports in Streamlit with ipyvizzu | Zachary Blackwood, Peter Vidos | 01h 08m |
Shows an actual integration: Streamlit + ipyvizzu component that embeds Vizzu animations inside Streamlit apps, with widget-driven interactivity (sliders… | ||
| Production Data to the Model: “Are You Getting My Drift?” | Gatha Varma | 26m |
Defines concrete drift types and detection recipes then shows a small Python notebook that injects synthetic drift into Dow Jones price data and compares… | ||
| Productionizing Open Source LLMs | Sean Sheng | 27m |
Open source LLMs now approach GPT‑4 on some tasks (Llama 70B ~81.7% vs GPT‑4 85.5% on a summarization study) and fine‑tuned smaller models (7B/13B) can beat… | ||
| Python as a Hackable Language for Interactive Data Science | Stephen Macke | 25m |
Demonstrates concrete hacks: an optional chaining AST rewrite that evaluates a?.b()?.c style chains, a non‑blocking assignment transform that runs expensive… | ||
| Python-Driven Portfolios: Bridging Theory and Practice for Efficient Investments | Kalyan Prasad | 29m |
Claims a working Python implementation of Modern Portfolio Theory using five ETFs pulled from yahu finance (2018–2022), computes daily log returns, covariance… | ||
| Dashing through the snow (or Sharing your data), in a Quarto Dashboard | Mine Cetinkaya-Rundel | 27m |
Shows how Quarto Dashboards actually build from qmd plain-text notebooks (not just a concept), including the render pipeline that runs Jupyter kernels and… | ||
| Real Time Machine Learning | Eitan Netzer, Oren Netzer | 26m |
Claim: retraining frequently preserves and even improves models — weekly retraining raises accuracy ~11% and F1 ~20% on a flight delays example. A core set… | ||
| Real-Time Revolution: Kickstarting Your Journey in Streaming Data | Zander Matheson | 28m |
Bytewax claims a Python-native, stateful stream processor backed by a Rust timely-dataflow engine and demonstrates it by ingesting Wikimedia’s recent-changes… | ||
| simplifies building complex dashboards | Maarten | 28m |
Claims pure-Python dashboards scale to larger apps by combining a React-like API with ipywidgets and a Solara server (Starlette/FastAPI/Flask/Django… | ||
| The Hell, According to a Data Scientist | Giuditta Parolini | 25m |
Lists exact sins and punishments: creators of invalid CSVs must replace every unescaped comma by hand for eternity, placeholder-fillers must undo hidden… | ||
| The Internet's Best Experiment Yet | Avrahami | 28m |
Dataset of r/place includes 16.1M pixel changes by 1.2M unique users over 72 hours (2017) plus a larger 2022 release; raw records give timestamp color and… | ||
| Finding undeclared & unused dependencies in your notebooks | Johan Herland | 25m |
Conference analysis shows about 42% of executed notebooks fail from missing deps; only 3.9% reproduce results. FawltyDeps finds undeclared imports and declared… | ||
| The State of Production Machine Learning in 2023 | Alejandro Saucedo | 30m |
Claims that model lifecycle begins when deployed not when trained and that production ML shifts from model-centric to system- and data-centric; names practical… | ||
| Training large scale models using PyTorch | Shagun Sodhani | 24m |
Covers concrete methods and tradeoffs for scaling: using NCCL with distributed data parallel to double batch throughput when adding GPUs, pipeline parallelism… | ||
| Tricking Neural Networks : Explore Adversarial Attacks | Bernice Waweru | 21m |
Shows concrete NLP attacks: a sentiment model flips when perfect -> spotless flips sentiment, a translator breaks with imperceptible unicode triggers change… | ||
| Orchestrating Generative AI Workflows to Deliver Business Value | Hugo Anderson | 34m |
Presents a practical framework for embedding LLMs into an existing Python stack using open source tooling: Metaflow as an orchestration and versioning layer… | ||
| Prefect Workflows for Scaling Acoustic Fisheries Survey Pipelines | Soham Butala | 29m |
Claims a working open-source ecoflow package that uses Prefect 2.0 for orchestration, YAML-configured pipelines, and native dask cluster parallelism. Shows… | ||
| Leveraging open-source LLMs for production | Andrey Cheptsov | 29m |
A practical survey of what actually works: LLaMA 2 7B/13B/70B and Mistral 7B appear as usable bases, Code Llama for code, and Hugging Face benchmarks show a… | ||
| LLMs: Beyond the Hype - A Practical Journey to Scale | Shashank Shekhar | 30m |
Describes concrete cost-quality tradeoffs: use smaller models or LLM cascading and request hedging to route simple queries to cheap models and reserve… | ||
| Map of Open-Source Science (MOSS) | Tim Bonnemann, Jonathan Starr | 29m |
Map of Open‑Source Science (MOSS) claims an interactive graph of OSS research tools that already maps NumFOCUS projects, packages, people and papers… | ||
| Xclim: Climate Data Processing and Analysis for Everyone | Pascal Bourgault, Trevor James Smith | 27m |
Library claims reproducible climate indicators at scale and demonstrates it: built on xarray and dask with Numba and Pint integrations, Xclim implements 187… | ||
| A practical guide to analysis & interactive visualization of massive datasets | Eswaramoorthy, Pevey | 01h 49m |
Hands-on walk-through uses a Nebari cloud lab with 50 instances to analyze the US DOT Airline On‑Time Performance 70 GB dataset stored as Parquet, showing Dask… | ||
| A Roadmap for Turning Theory and Data Projects into Python Packages | Ramon Perez | 02h 05m |
Implements a handmade Lasso algorithm, compares predictions to scikit‑learn (small mean absolute difference), packs it into a Python package named lasso_reg… | ||
| Xorbits Inference: Model Serving Made Easy | Jon Wang | 21m |
Xinference claims to simplify LLM serving with concrete features: automatic engine selection across NVIDIA AMD Apple silicon, OpenAI‑compatible API and web UI… | ||
| VocalPy: a core Python package for acoustic comms research | David Nicholson | 22m |
VocalPy packages domain types (Audio, Spectrogram, Annotation), wraps cetta, uses dask for parallel processing and exports datasets as a sqlite single-file… | ||
| Unlock the Full Potential of Jupyter Notebooks | Nir Barazida | 28m |
Notionally useful tips become actionable when the talk names the concrete fixes: move code into modules with pure functions and run unit tests, use Git and… | ||
| Unified batch and stream processing in python | Arthur Andres | 29m |
Claims a single Python library lets one codebase run both batch and microbatch streaming jobs: use beavers to orchestrate a DAG of functions, use Apache Arrow… | ||
| Understanding reactive execution in Shiny | Gordon Shotwell | 25m |
Shiny for Python claims to infer component dependencies and minimally rerender by building a computation graph via runtime tracing rather than manual… | ||
| We rewrote tsfresh in Polars and why you should too | Mathieu Cayssol, Chris Lo | 26m |
A full rewrite of tsfresh becomes functime built on Polars and Rust and it actually ships hundreds of reimplemented features that run much faster and use less… | ||
| Unravelling Hidden Technical Debt in ML: A Pythonic Approach to Robust Systems | Ravi Singh | 28m |
Claims six core ML challenges illustrated by a churn‑prediction case and shows how quick notebook→Lambda deployment produces training‑serving skew and brittle… | ||
| DDataflow: An open-source end to end testing from machine learning pipelines | Machado, Meynard | 23m |
DDataflow is an open-source tool that enables end-to-end tests by applying decentralized sampling per data source so CI runs the real pipeline against a small… | ||
| Leveraging Git for ML Experiment Management | Eryk Lewinson | 27m |
Claims a reproducible, Git-first way to manage the dozens or tens or hundreds of experiments data teams run: use Git for code + DVC for data, models and… | ||
| Exploring Tech Career in Data Analytics and Science | Pearl Prakash | 29m |
Seven years of hands‑on experience across consulting and startups, concrete tools like SQL Python Excel and cloud pieces such as BigQuery and Airflow, and… | ||
| Leveraging LLM for text augmentation in Topic Modeling | Ariel Gamiño | 35m |
Demonstrates generating synthetic text with GPT‑3.5 Turbo to expand small seeds (300 TweetEval examples and subsets of OpenFoodFacts), filters out low‑quality… | ||
| Taking Career to the next level | Sahana Venkatesh | 31m |
Career advice lists concrete actions and outcomes: build side projects teach accountability (blogs, tools), take named online courses (Coursera Google Amazon)… | ||
| Best practices and solution design using data versioning for machine learning | Yonatan Alexander | 01h 25m |
Claims a Git-first, branch-and-tag system that versions raw data, splits, models and monitoring so reproducibility is literal; demo uses Zab (Git-backed data… | ||
| How to write your way into a data job | Elliot Gunn | 01h 15m |
A single well-crafted article launches careers here: one published piece serves as one good article that recruiters and conference contacts actually click. The… | ||
| The Data Science skills you already own | Pedro Araujo | 43m |
Workshop claims that many lab and engineering tasks map to transferable roles and gives a seven-question worksheet to track progress over six months. Case… | ||
| Head first... into open source | Jonathan Starr | 32m |
Argues that people who do not code become core contributors by using community design, writing, and events rather than commits; claims BOINC aggregates into a… | ||
| Impact Scholars Opening Session | C stosa | 38m |
Four-day Impact Scholars program runs now through Saturday with Saturday as the biggest non-overlapping day; today has two talks and a networking session at… | ||
| Open-source contribution: the new way to build your portfolio! | Mert Bozkir | 29m |
Claims that building projects publicly and contributing to open source yields concrete hiring signals — two or three job messages on LinkedIn after doing this… | ||
| How to think about your career in Data science | Bertil Hatt | 01h 03m |
Covers a framework linking transaction-cost economics (Ronald Coase) and data patterns to concrete career choices, evaluates specific tools and patterns like… | ||
| nothing matches | ||