Fraud detection challenges and data skepticism using LIME (Shir Meir Lador)
https://www.youtube.com/watch?v=HcaAKI1tVGM
https://github.com/marcotcr/lime
- To view a random sample of dataframe:
df.sample(frac=1).head()
Bayesian optimisation with scikit-learn (Thomas Huijskens)
https://www.youtube.com/watch?v=jtRPxRnOXnk
- Bayesian Optimization increases computational overhead, so only makes sense to use when the number of hyperparameters is very high, or it is computationally very expensive to evaluate the out of sample performance.
- GPs are the generalization of a Gaussian distribution to a distribution over functions, instead of random variables.
- Packages: Spearming, Hyperopt, MOE, Hyperband (model-free), SMAC (model-free)
Ten Steps to Keras (Valerio Maggio)
https://www.youtube.com/watch?v=FrkYu2zVUyM
Bayesian Deep Learning with Edward (and a trick using Dropout) (Andrew Rowan)
https://www.youtube.com/watch?v=I09QVNrUS3Q
Next generation of word embeddings in Gensim (Lev Konstantinovskiy)
https://www.youtube.com/watch?v=7530Tn2J0Mc
Dimension Reduction and Extracting Topics - A Gentle Introduction (Tariq Rashid)
https://www.youtube.com/watch?v=Bxlzbck51SU
http://makeyourowntextminingtoolkit.blogspot.co.uk/
Make your research interactive with Jupyter Dashboards (Pavlo Andriychenko)
https://www.youtube.com/watch?v=qQaBv7gw8vw
Kubernetes for Pythonistas (Kelsey Hightower)
https://www.youtube.com/watch?v=u_iAXzy3xBA
- (Really fun Tetris example of scheduling)
Probabilistic Programming with PyMC3 (Christopher Fonnesbeck)
https://www.youtube.com/watch?v=5TyvJ6jXHYE
The Python Visualization Landscape (Jake VanderPlas)
https://www.youtube.com/watch?v=FytuB8nFHPQ
Optimizing Pandas Code for Speed and Efficiency (Sofia Heisler)
https://www.youtube.com/watch?v=HN5d490_KKk
Keynote (Jake Vanderplas)
https://www.youtube.com/watch?v=ZyjCqQEUa8o
The Fastest FizzBuzz in the West (Dustin Ingram)
https://www.youtube.com/watch?v=ApgUrtCrmV8
def fizzbuzz(n):
for i in range(n):
print(i%3//2*'fizz'+i%5//4*'buzz' or i+1)An Introduction to Reinforcement Learning (Jessica Forde)
https://www.youtube.com/watch?v=k1UuTyW2uFc
Human Machine Collaboration for Improved Analytical Processes (Tony Ojeda)
https://www.youtube.com/watch?v=s0u_UkVecx0
The Dictionary Even Mightier (Brandon Rhodes)
https://www.youtube.com/watch?v=66P5FMkWoVU
Modern Python Dictionaries -- A confluence of a dozen great ideas (Raymond Hettinger)
https://www.youtube.com/watch?v=npw4s1QTmPg
What's new in Python 3.6 (Brett Cannon)
https://www.youtube.com/watch?v=c2rEbbGLPQc
Readability Counts (Trey Hunner)
https://www.youtube.com/watch?v=knMg6G9_XCg
The Wild West of Data Wrangling (Sarah Guido)
https://www.youtube.com/watch?v=xn9sTXR3Cp8
Dask: A Pythonic Distributed Data Science Framework (Matthew Rocklin)
https://www.youtube.com/watch?v=RA_2qdipVng
Factory Automation with Python Stories about Robots, Serial Ports, and Barcode Readers (Jonas Neubert)
https://www.youtube.com/watch?v=cEyVfiix1Lw
Snakes on a Hyperplane Python Machine Learning in Production (Jessica Lundin)
https://www.youtube.com/watch?v=oV-cm0Loefg
A gentle introduction to deep learning with TensorFlow (Michelle Fullwood)
https://www.youtube.com/watch?v=5e0TbyCkbCY
Looping Like a Pro in Python (David DB Baumgold)
https://www.youtube.com/watch?v=u8g9scXeAcI
Instagram Filters in 15 Lines of Python (Michele Pratusevich)
https://www.youtube.com/watch?v=otLGDpBglEA
Passing Exceptions 101 Paradigms in Error Handling (Amandine Lee)
https://www.youtube.com/watch?v=BMtJbrvwlmo
Python Epiphanies [Tutorial] (Stuart Williams)
https://www.youtube.com/watch?v=oQca6eDcjA8
Intro to Bayesian Machine Learning with PyMC3 and Edward [Tutorial] (Torsten Scholak, Diego Maniloff)
https://www.youtube.com/watch?v=fR5Wvb86-IU
http://tscholak.github.io/assets/PyConEdward/#/
Parallel Data Analysis [Tutorial] (Ben Zaitlen, Matthew Rocklin, Min Ragan Kelley, Olivier Grisel)
https://www.youtube.com/watch?v=KIXACCJHtDg
Build a data pipeline with Luigi [Tutorial] (Aaron Knight)
https://www.youtube.com/watch?v=jpkZGXrhZJ8
Faster Python Programs Measure, don't Guess [Tutorial] (Mike Müller)
https://www.youtube.com/watch?v=xmuEsYut9Pc
Python and Jupyter in Depth: High productivity, interactive Python [Tutorial] (Matthias Bussonnier, Mike Bright, Min Ragan-Kelley)
https://www.youtube.com/watch?v=VQBZ2MqWBZI
Introduction to Digital Signal Processing [Tutorial] (Allen Downey)
https://www.youtube.com/watch?v=UOIllEyajGs
Deploy and scale containers with Docker native, open source orchestration [Tutorial] (Jerome Petazzoni, AJ Bowen)
https://www.youtube.com/watch?v=EuzoEaE6Cqs
Hands On Intro to Python for New Programmers [Tutorial] (Trey Hunner)
https://www.youtube.com/watch?v=6zu8lrYn6t8
Time Series Analysis (Aileen Nielsen)
https://www.youtube.com/watch?v=zmfe2RaX-14
Decorators and descriptors decoded (Luciano Ramalho)
https://www.youtube.com/watch?v=81S01c9zytE
Fantastic Data and Where To Find Them: An introduction to APIs, RSS, and Scraping (Nicole Donnelly, Tony Ojeda, Will Voorhees)
https://www.youtube.com/watch?v=A42voDYkFZw
Introduction to Statistical Modeling with Python (Christopher Fonnesbeck)
https://www.youtube.com/watch?v=TMmSESkhRtI
bokeh: Data Visualization in Python (Chalmer Lowe)
https://www.youtube.com/watch?v=xId9B1BVusA
Exploratory data analysis in pytho (Chloe Mawer, Jonathan Whitmore)
https://www.youtube.com/watch?v=W5WE9Db2RLU
https://github.com/cmawer/pycon-2017-eda-tutorial/
Using Functional Programming for efficient Data Processing and Analysis (Reuben Cummings)
https://www.youtube.com/watch?v=9kDUTJahXBM
https://speakerdeck.com/reubano/using-functional-programming-for-efficient-data-processing-and-analysis
https://github.com/reubano/pycon17-tute
SaaaS - Sampling as an Algorithm Service (Vincent D. Warmerdam)
https://www.youtube.com/watch?v=8g6oF8vUqTU
http://koaning.io/switching-to-sampling-in-order-to-switch.html
http://koaning.io/elimination-via-inference.html
- (Approachable introduction to MCMC)
- Uses the Kaggle Santa's Uncertain Bags Competition as an example
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Open Data Use Cases (Ulrike Thalheim)
https://www.youtube.com/watch?v=kNT-MaVWJmM
- Overview of Open Data
- State of Open Data in Germany
- Use cases of Open Data
- Environmental data - Air quality
- Election Data
- Wikidata
- Other projects
- How to get involved
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Use Python to Load & Prepare Data Analytics (Raymond Hettinger)
https://www.youtube.com/watch?v=nO78ECRighw
from collections import defaultdict
from pprint import pprint
d = defaultdict(list)
d['a'].append('apple')
d = dict(d) # to convert to regular dict
pprint(d)
# start @ 11:52The Secret Life Of Rolling Pandas (Jaime Fernandez del Rio)
https://www.youtube.com/watch?v=XM_r5La-1tA
Keynote (Travis Oliphant)
https://www.youtube.com/watch?v=NBliyFXnWeo
A Beginners Guide to Weather & Climate Data (Margriet Groenendijk)
https://www.youtube.com/watch?v=U_Aq2cPxwss
NASA Space APPS Challenge: Asteroid prediction impact (Gema Parreño)
https://www.youtube.com/watch?v=8h3uyQfiMNc
Security for Data Scientists (David Arcos)
https://www.youtube.com/watch?v=aicyYd8MZWo
Web Scraping with Python (Carles Illa)
https://www.youtube.com/watch?v=aTwvQZjEZhk
Marketing Data Science (Joaquin Pais)
https://www.youtube.com/watch?v=jzuQJg3bFy8
Python for visualization of HPC applications (Miguel Zavala-Ake)
https://www.youtube.com/watch?v=0ni90h8EErQ
TensorFlow Wide & Deep: Advanced Classification the easy way (Yufeng Guo)
https://www.youtube.com/watch?v=WKgNNC0VLhM
Feature Importance and Ensemble Methods : a new perspective (Constant Bridon)
https://www.youtube.com/watch?v=qdEhJ0uT7wk
Extending Jupyter with Google Cloud Storage file system backend (Egor Bulychev)
https://www.youtube.com/watch?v=uLC0t5JZBZs
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Tutorial: Web Scraping with Python (Lingqiang Kong)
https://github.com/klq/python-webscrapes
Tutorial: D’oh! Unevenly spaced time series analysis of The Simpsons in Pandas (Joe McCarthy)
https://pydata.org/seattle2017/schedule/presentation/104/
Tutorial: From Novice to Data Ninja (Valentina Staneva)
Tutorial: So you want to be a Python expert? (James Powell)
Tutorial: Introduction to data analytics with pandas (Quentin Caudron)
Tutorial: pomegranate, fast and flexible probabilistic modeling in python (Maxwell W Libbrecht)
http://noble.gs.washington.edu/~maxwl/2017-07-05%20pydata%20pomegranate.pdf
Tutorial: Data Visualization and Exploration with Python (Stephen Elston)
https://github.com/StephenElston/ExploringDataWithPython
Tutorial: Vocabulary Analysis of Job Descriptions (Alex Thomas)
Keynote 1 (Katrina Riehl)
Provenance for Reproducible Data Science (Andreas Schreiber)
Monitoring Displacement Crises with Python (George Richardson)
Designing for Guidance in Machine Learning (Olivia Gunton)
Automatic Citation generation with Natural Language Processing (Claire Kelley, Sarah Kelley)
Practical Optimization for Stats Nerds (Ryan J. O'Neil)
Pandas, Pipelines, and Custom Transformers (Julie Michelman)
Robust Automated Forecasting in Python and R (Pranav Bahl, Jonathan Stacks)
How to be a 10x Data Scientist (Stephanie Kim)
- Code Stubs
- Exception handling
- Unit Tests
Learning Neural Nets Through Implementation and Examples (Kyle Shaffer)
Forecasting Time Series Data at scale with the TICK stack (Nathaniel Cook)
Sirbarksalot: Bark Detection in Python (Nicholas Kridler)
High Fidelity Web Crawling in Python (Josh Weissbock)
Implementing and Training Predictive Customer Lifetime Value Models in Python (Jean-Rene Gauthier, Ben Van Dyke)
Learn to be a painter using Neural Style Painting (Pramit Choudhary)
Scaling Scikit-Learn (Stephen Hoover)
Interactive Data Analysis (Jeffrey Heer)
JupyterLab+Real Time Collaboration (Brian Granger, Chris Colbert & Ian Rose)
We came, we saw, we hacked. How to win a Big Data hackathon (Eloisa Tran)
Bokeh and Friends (Bryan Van de ven)
Using Machine Learning and Brain Waves to Detect Errors in Human Problem Solving (Katie Porterfield)
Robust Algorithms for Machine Learning (Tom Radcliffe)
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PyData Seattle Diversity Panel - How diversity drives excellence in our data driven tech world (Lingqiang Kong, Andrea Brice, Charlotte Flanagan, Tina-Marie Gulley, James Powell)
https://www.youtube.com/watch?v=xOcbLqnFUa8
- Ling: First PyData conference, noticed there were lines at the men's restroom but not the women's.
- What obstacles have you encountered in building a diverse community?
- Ling: 1) Building a diverse community not always a priority (especially when hiring one at a time). 2) Really hard to find diverse candidates. Why? Not building from scratch; building from an already established tech community. So, general lack of role models. Work on building mentorships and role models.
- What lessons can you share about overcoming these obstackes?
- Andrea: Find people who are adaptable, not try to use some objective measure of competency.
- What actionalble advice do you have someone trying to increase undersatanding the value of diversity?
- Tina: Show up for people who don't look like you. Understand unconscious bias. Change mindset so it is a business issue as well as a human issue. As we create job descriptions, challenge the list of "must haves".
- What examples have you encountered where a project was improved by having a diverse team?
- James: (Handed off to Brett Cannon, CPython Core Contributor) - After the first two female core core contributors made it much easier to get momentum.
- In the wake of high profile HR scandals in tech, how can we as a tech community build more inclusive workplaces?
- James: We all know the overt discrimination is wrong, we need to scrutinize the unconscious biases.
- Charlotte: The Atlantic - Why Silicon Valley So Awful To Women
- What tips do you have for diversity candidates when they're trying to find a job
- Charlotte: I don't know if my advice would be that different to anyone. Network like crazy. Don't limit yourself by what is listed on the job discription.
- What tops do you have for teams trying to avoid hiring bias?
- Charlotte: There is tech that will help ensure job postings attarct a more diverse candidate pool. Train interviewers. Work hard to avoid the "like-me" bias.
- What advice do you have regarding performance reviews or promotions?
- Ling: Salary differences due to asking promotions, and different perceptions due to whether men or women are asking for a promotion. Need to have a more consistent career check-in with manager.
- What advice do you have managers to create a trusting workplace?
- Tina: Leader needs to serve as role model; transparent. Learn more about individuals. Listen with empathy.
- What resources or social networks do you when you don't know how to resolve a challenging workplace situation?
- Andrea: "Nevertheless, she persisted." Be the amazing team-mate to the solo person who is having the challenge.
- If you frame diversity as good for business instead of as a social justice, does it risk getting less resources?
- Ling: Diversity does drive the bottom line. In ML you ensemble diverse models!
- What are practical steps you've taken to deal with imposter syndrome?
- Charlotte: Talk things through with your network. Power pose before interviews. (Note: The science the TED Talk was based on has not been able to be replicated.)
- Andrea: Get used to not knowing, develop the skill of jumping in getting going.
- Ling: Dunning-Krueger Effect - The less you know, the more you over-estimate your skills. Imposter Syndrome means your human and probably more skilled than you think.
- Can we encourage people to be Person #1
- Andrea: Walk towards your fears. Otherwise you're not growing.
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