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Python Conference Programming

Track :

Programming

Lessons no : 82

For Free Certificate After Complete The Course

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What will you learn in this course?
  • Master Python conference planning, organization, and promotion techniques for successful events involving experts and researchers
  • Develop skills to coordinate Python conference schedules, speaker management, and attendee engagement strategies
  • Apply best practices for marketing and promoting Python conferences to target audiences and industry stakeholders
  • Implement effective registration, ticketing, and logistical arrangements for large-scale Python programming events
  • Utilize social media, digital marketing, and outreach tools to increase Python conference visibility and participation
  • Design engaging conference agendas that foster knowledge sharing, networking, and community building in Python programming
  • Evaluate venue options, technical requirements, and safety protocols for hosting Python conferences
  • Leverage Python tools and platforms to streamline conference management, communication, and attendee feedback
  • Create compelling content and presentations to attract speakers and sponsors for Python conferences
  • Coordinate volunteer and staff roles to ensure smooth conference operations and attendee experience
  • Apply project management skills to plan, execute, and evaluate Python conference events efficiently
  • Utilize data analytics to measure conference success, attendee satisfaction, and areas for improvement
  • Build partnerships with industry leaders, academic institutions, and Python community organizations
  • Develop strategies for post-conference engagement, content dissemination, and community growth

How to Get The Certificate

  • You must have an account Register
  • Watch All Lessons
  • Watch at least 50% of Lesson Duration
  • you can follow your course progress From Your Profile
  • You can Register With Any Course For Free
  • The Certificate is free !
Lessons | 82
AiiDA 1 0 A scalable simulation platform to manage high throughput high performance simulations
00:20:39

AiiDA 1 0 A scalable simulation platform to manage high throughput high performance simulations
Align Incentives Across Cases for Domain Specific Scientific Software |SciPy 2020| Benthall Seth
00:18:21

Align Incentives Across Cases for Domain Specific Scientific Software |SciPy 2020| Benthall Seth
Analyzing the Performance of Python Applications Using Multiple Levels of Parallelism |SciPy 2020|
00:24:56

Analyzing the Performance of Python Applications Using Multiple Levels of Parallelism |SciPy 2020|
Applying Probabilistic Inference to Astronomical Spectroscopy |SciPy2020| Michael Gully Santiago
00:27:22

Applying Probabilistic Inference to Astronomical Spectroscopy |SciPy2020| Michael Gully Santiago
Arkouda Terascale Data Science at Interactive Rates |SciPy 2020| Ben Albrecht
00:25:18

Arkouda Terascale Data Science at Interactive Rates |SciPy 2020| Ben Albrecht
Avocado Characterizing Human Epigenomics Through Imputation |SciPy 2020| Jacob Schreiber
00:26:21

Avocado Characterizing Human Epigenomics Through Imputation |SciPy 2020| Jacob Schreiber
Awkward Array Manipulating JSON like Data with NumPy like Idioms |SciPy 2020| Jim Pivarski
00:36:01

Awkward Array Manipulating JSON like Data with NumPy like Idioms |SciPy 2020| Jim Pivarski
Boost histogram High Performance Histograms as Objects |SciPy 2020| Schreiner Pivarski Dembinski
00:31:13

Boost histogram High Performance Histograms as Objects |SciPy 2020| Schreiner Pivarski Dembinski
Continuous Integration for Scientific Python Projects |SciPy 2020| Stanley Seibert
00:43:25

Continuous Integration for Scientific Python Projects |SciPy 2020| Stanley Seibert
cuSignal GPU Accelerating SciPy Signal with Numba and CuPy |SciPy 2020| Adam Thompson
00:27:59

cuSignal GPU Accelerating SciPy Signal with Numba and CuPy |SciPy 2020| Adam Thompson
Detecting Plastics in Coastal Waters with Satellite Data and Machine Learning |SciPy 2020| Lynn
00:25:36

Detecting Plastics in Coastal Waters with Satellite Data and Machine Learning |SciPy 2020| Lynn
Interactive Supercomputing with Jupyter at NERSC |SciPy 2020| Rollin Thomas
00:29:48

Interactive Supercomputing with Jupyter at NERSC |SciPy 2020| Rollin Thomas
Learning from Evolving Data Streams |SciPy 2020| Jacob Montiel
00:25:26

Learning from Evolving Data Streams |SciPy 2020| Jacob Montiel
Legate Numpy Accelerated and Distributed Array Computing |SciPy 2020| Michael Bauer
00:32:18

Legate Numpy Accelerated and Distributed Array Computing |SciPy 2020| Michael Bauer
Machine Learning Model Serving The Next Step |SciPy 2020| Simon Mo
00:21:16

Machine Learning Model Serving The Next Step |SciPy 2020| Simon Mo
Measuring OpenStreetMap Completeness using Human Settlement Layers |SciPy 2020| Orden
00:20:54

Measuring OpenStreetMap Completeness using Human Settlement Layers |SciPy 2020| Orden
Optimizing Humans and Machines to Advance Science | SciPy 2020 | Ana Comesana
00:21:07

Optimizing Humans and Machines to Advance Science | SciPy 2020 | Ana Comesana
pyoscode Fast Solutions of Oscillatory ODEs in Physics |SciPy 2020| Agocs Handley Hobson Lasenby
00:27:44

pyoscode Fast Solutions of Oscillatory ODEs in Physics |SciPy 2020| Agocs Handley Hobson Lasenby
Solving Spectroscopic Problems with Pythonic Workflows and Deep Learning | SciPy 2020 | Kelvin Lee
00:24:23

Solving Spectroscopic Problems with Pythonic Workflows and Deep Learning | SciPy 2020 | Kelvin Lee
Treating Gridded Geospatial Data as Point Data to Simplify Analytics |SciPy 2020| Christine Smit
00:24:57

Treating Gridded Geospatial Data as Point Data to Simplify Analytics |SciPy 2020| Christine Smit
Frictionless Data for Reproducible Biology |SciPy 2020| Lilly Winfree
00:23:37

Frictionless Data for Reproducible Biology |SciPy 2020| Lilly Winfree
Geomstats A Python Package for Riemannian Geometry in Machine Learning |SciPy 2020| Miolane
00:19:46

Geomstats A Python Package for Riemannian Geometry in Machine Learning |SciPy 2020| Miolane
Geospatial Analysis in the Cloud Using PODPAC and JupyterLab |SciPy 2020| Matt Ueckermann
00:25:30

Geospatial Analysis in the Cloud Using PODPAC and JupyterLab |SciPy 2020| Matt Ueckermann
High Dimensional Data Visualization with Clustergrammer2 |SciPy 2020| Nicolas Fernandez
00:29:15

High Dimensional Data Visualization with Clustergrammer2 |SciPy 2020| Nicolas Fernandez
Jupyter Notebooks for Spacecraft and Robot Dynamics Simulations |SciPy 2020| Gaut Cameron and Jain
00:20:36

Jupyter Notebooks for Spacecraft and Robot Dynamics Simulations |SciPy 2020| Gaut Cameron and Jain
Leading Magnetic Fusion Energy Science into the Big and Fast Data Lane |SciPy 2020| Ralph Kube
00:20:18

Leading Magnetic Fusion Energy Science into the Big and Fast Data Lane |SciPy 2020| Ralph Kube
Ray A System for Scalable Python and ML |SciPy 2020| Robert Nishihara
00:24:39

Ray A System for Scalable Python and ML |SciPy 2020| Robert Nishihara
Why Design Abstraction Matters for Analytics Tools Neuroimaging analysis with Neuro Learn
00:24:34

Why Design Abstraction Matters for Analytics Tools Neuroimaging analysis with Neuro Learn
pyhf A Pure Python Statistical Fitting Library with Tensors and Autograd |Scipy 2020| Feickert
00:22:02

pyhf A Pure Python Statistical Fitting Library with Tensors and Autograd |Scipy 2020| Feickert
dabl Automatic Machine Learning with a Human in the Loop |SciPy 2020| Andreas Mueller
00:25:44

dabl Automatic Machine Learning with a Human in the Loop |SciPy 2020| Andreas Mueller
SunPy Status of the Project and v1 0 Core Package |SciPy 2020| Monica Bobra and The SunPy Community
00:16:32

SunPy Status of the Project and v1 0 Core Package |SciPy 2020| Monica Bobra and The SunPy Community
Remote DataFrames and Interactive Exploration of Big Astronomical Datasets with Vaex |SciPy 2020|
00:30:06

Remote DataFrames and Interactive Exploration of Big Astronomical Datasets with Vaex |SciPy 2020|
Forecasting Solar Flares |SciPy 2020| Monica Bobra
00:16:54

Forecasting Solar Flares |SciPy 2020| Monica Bobra
Bringing GPU support to Datashader A RAPIDS case study |SciPy 2020| Jon Mease
00:20:59

Bringing GPU support to Datashader A RAPIDS case study |SciPy 2020| Jon Mease
GPU Accelerated Data Analytics in Python |SciPy 2020| Joe Eaton
00:28:23

GPU Accelerated Data Analytics in Python |SciPy 2020| Joe Eaton
JAX Accelerated Machine Learning Research | SciPy 2020 | VanderPlas
00:23:40

JAX Accelerated Machine Learning Research | SciPy 2020 | VanderPlas
Fluctuation X ray Scattering Real Time App |SciPy2020| Dujardin Slaugther Donatelli Zwart Yoon
00:23:21

Fluctuation X ray Scattering Real Time App |SciPy2020| Dujardin Slaugther Donatelli Zwart Yoon
Continual Updating of Evolutionary Estimates in the Open Tree of Life |SciPy 2020| Sanchez Reyes
00:18:00

Continual Updating of Evolutionary Estimates in the Open Tree of Life |SciPy 2020| Sanchez Reyes
A New Partitioning Algorithm for Optimizing Parallelization of Flow Networks |SciPy 2020| Tiernan
00:15:54

A New Partitioning Algorithm for Optimizing Parallelization of Flow Networks |SciPy 2020| Tiernan
PyRS Data Processing Software for Engineering Neutron Diffraction |SciPy 2020| Zhou
00:22:05

PyRS Data Processing Software for Engineering Neutron Diffraction |SciPy 2020| Zhou
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Hemanth kumar Mangu

may i know the total duration of the course it help to plan schedule my time for the course? Iam final year btech student i need to submit the self-learning course to complete my graduation. they required 40hrs+ duration course. please give me information 2023-05-12

Pulok Samanta

Good 2022-11-20

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Python Conferences are events arranged every year for discussion and promotion of the python programming language. Experts and researchers .