“The 22 Most-Used Python Packages in the World” by Erik van Baaren https://link.medium.com/urafNGaY36
Medium
The 22 Most-Used Python Packages in the World
Educational and surprising insights into how Python is used
A beginner-friendly list of data science projects
1) Rainfall in India
Project type: Visualization
Link to dataset : https://www.kaggle.com/rajanand/rainfall-in-india
2) Global Suicide Rates
Project type: Exploratory Data Analysis
Link to dataset : https://www.kaggle.com/russellyates88/suicide-rates-overview-1985-to-2016
3) Summer Olympic Medals
Project type: Exploratory Data Analysis
Link to dataset : https://www.kaggle.com/divyansh22/summer-olympics-medals
4) World Happiness Report
Project type: Exploratory Data Analysis
Link to dataset : https://www.kaggle.com/unsdsn/world-happiness
5) Pollution in the United States
Project type: Visualization
Link to dataset : https://www.kaggle.com/sogun3/uspollution
6) Nutrition Facts for McDonald’s Menu
Project type: Exploratory Data Analysis
Link to dataset : https://www.kaggle.com/mcdonalds/nutrition-facts
7) Red Wine Quality
Project type: Prediction Modeling
Link to dataset : https://www.kaggle.com/uciml/red-wine-quality-cortez-et-al-2009
1) Rainfall in India
Project type: Visualization
Link to dataset : https://www.kaggle.com/rajanand/rainfall-in-india
2) Global Suicide Rates
Project type: Exploratory Data Analysis
Link to dataset : https://www.kaggle.com/russellyates88/suicide-rates-overview-1985-to-2016
3) Summer Olympic Medals
Project type: Exploratory Data Analysis
Link to dataset : https://www.kaggle.com/divyansh22/summer-olympics-medals
4) World Happiness Report
Project type: Exploratory Data Analysis
Link to dataset : https://www.kaggle.com/unsdsn/world-happiness
5) Pollution in the United States
Project type: Visualization
Link to dataset : https://www.kaggle.com/sogun3/uspollution
6) Nutrition Facts for McDonald’s Menu
Project type: Exploratory Data Analysis
Link to dataset : https://www.kaggle.com/mcdonalds/nutrition-facts
7) Red Wine Quality
Project type: Prediction Modeling
Link to dataset : https://www.kaggle.com/uciml/red-wine-quality-cortez-et-al-2009
Kaggle
Rainfall in India
Sub-division wise monthly data for 115 years from 1901-2015.
😃Huge List of Free Machine Learning resources like 620K+ Datasets, 150+ Notebooks, & many more......
Link : https://aihub.cloud.google.com/u/0/
Link : https://aihub.cloud.google.com/u/0/
What is the output of the following code snippet?
func = lambda x: return x print(func(2))
func = lambda x: return x print(func(2))
Anonymous Quiz
6%
x
30%
SyntaxError
21%
2.0
41%
2
3%
0
“How to start writing Data Science blogs?” by Rashi Desai https://link.medium.com/PCRKp4sZ76
Medium
How to start writing Data Science blogs?
Thinking of blogging on Data Science? This one is for you.
👍 Best TED Talks for Data Science
1. How not to be ignorant about the world
Topic: Data Visualization
Speaker: Hans Rosling, author of “Factfulness”
Link : https://www.ted.com/talks/hans_rosling_the_best_stats_you_ve_ever_seen?language=en
2. The Beauty of Data Visualization
Topic: Data Visualization
Speaker: David McCandless, renowned data journalists.
Link : https://www.ted.com/talks/david_mccandless_the_beauty_of_data_visualization?language=en
3. Three ways to spot a bad statistic
Topic: Statistics
Speaker: Mona Chalabi, data journalist
Link : https://www.ted.com/talks/mona_chalabi_3_ways_to_spot_a_bad_statistic?referrer=playlist-making_sense_of_too_much_data
4. Big Data is better data
Topic: Big Data
Speaker: Kenneth Cukier, data analyst for The Economist
Link : https://www.ted.com/talks/kenneth_cukier_big_data_is_better_data
5. The human insight missing from Big Data
Topic: Big Data
Speaker: Tricia Wang, global tech ethnographer
Link : https://www.ted.com/talks/tricia_wang_the_human_insights_missing_from_big_data?referrer=playlist-making_sense_of_too_much_data
6. Your company’s data could end world hunger
Topic: The value of data
Speaker: Dr. Mallory Freeman, Lead Data Scientist in the UPS Advanced Technology Group
Link : https://www.ted.com/talks/mallory_freeman_your_company_s_data_could_help_end_world_hunger?referrer=playlist-making_sense_of_too_much_data
7. Who Controls the World
Topic: Complexity Theory
Speaker: James B. Glattfelder, Swiss Scientist
Link : https://www.ted.com/talks/james_b_glattfelder_who_controls_the_world?language=en
1. How not to be ignorant about the world
Topic: Data Visualization
Speaker: Hans Rosling, author of “Factfulness”
Link : https://www.ted.com/talks/hans_rosling_the_best_stats_you_ve_ever_seen?language=en
2. The Beauty of Data Visualization
Topic: Data Visualization
Speaker: David McCandless, renowned data journalists.
Link : https://www.ted.com/talks/david_mccandless_the_beauty_of_data_visualization?language=en
3. Three ways to spot a bad statistic
Topic: Statistics
Speaker: Mona Chalabi, data journalist
Link : https://www.ted.com/talks/mona_chalabi_3_ways_to_spot_a_bad_statistic?referrer=playlist-making_sense_of_too_much_data
4. Big Data is better data
Topic: Big Data
Speaker: Kenneth Cukier, data analyst for The Economist
Link : https://www.ted.com/talks/kenneth_cukier_big_data_is_better_data
5. The human insight missing from Big Data
Topic: Big Data
Speaker: Tricia Wang, global tech ethnographer
Link : https://www.ted.com/talks/tricia_wang_the_human_insights_missing_from_big_data?referrer=playlist-making_sense_of_too_much_data
6. Your company’s data could end world hunger
Topic: The value of data
Speaker: Dr. Mallory Freeman, Lead Data Scientist in the UPS Advanced Technology Group
Link : https://www.ted.com/talks/mallory_freeman_your_company_s_data_could_help_end_world_hunger?referrer=playlist-making_sense_of_too_much_data
7. Who Controls the World
Topic: Complexity Theory
Speaker: James B. Glattfelder, Swiss Scientist
Link : https://www.ted.com/talks/james_b_glattfelder_who_controls_the_world?language=en
Ted
The best stats you've ever seen
You've never seen data presented like this. With the drama and urgency of a sportscaster, statistics guru Hans Rosling debunks myths about the so-called "developing world."
List a is defined as follows:
a = [1, 2, 3, 4, 5] Select all of the following statements that remove the middle element 3 from a so that it equals [1, 2, 4, 5]:
a = [1, 2, 3, 4, 5] Select all of the following statements that remove the middle element 3 from a so that it equals [1, 2, 4, 5]:
Anonymous Poll
58%
a.remove(3)
21%
a[2] = [ ]
19%
a[2:3] = [ ]
50%
del a[2]
7%
a[2:2] = [ ]
❤1
a, b, c = (1, 2, 3, 4, 5, 6, 7, 8, 9)[1::3]
Following execution of this statement, what is the value of b:
Following execution of this statement, what is the value of b:
Anonymous Quiz
19%
6
32%
2
26%
4
23%
5
What is the result of this statement:
>>> z = {'b', 'a', 'r'} & set('qux') . >>> print(z)
>>> z = {'b', 'a', 'r'} & set('qux') . >>> print(z)
Anonymous Quiz
9%
{ )
22%
set( )
49%
{'q', 'r', 'x', 'u', 'b', 'a'}
19%
{'b', 'r', 'a'}
Really cool work out of Microsoft called hummingbird! You can convert traditional #ML models to #Tensor #computations to take advantage of #hardware acceleration like GPUs and TPUs.
Here they convert a random forest model to PyTorch
Link : https://t.co/yT2xUmtLiP
Here they convert a random forest model to PyTorch
Link : https://t.co/yT2xUmtLiP
GitHub
GitHub - microsoft/hummingbird: Hummingbird compiles trained ML models into tensor computation for faster inference.
Hummingbird compiles trained ML models into tensor computation for faster inference. - GitHub - microsoft/hummingbird: Hummingbird compiles trained ML models into tensor computation for faster infe...
[#AI #MachineLearning #DataScience]
Paper Explained-Video👏
👇
Learning To Classify Images Without Labels
New combination of representation learning, clustering & self-labeling with high accuracy on benchmark datasets
Paper link : https://arxiv.org/abs/2005.12320
YouTube Link : https://t.co/Fo3T4iSM3g
Paper Explained-Video👏
👇
Learning To Classify Images Without Labels
New combination of representation learning, clustering & self-labeling with high accuracy on benchmark datasets
Paper link : https://arxiv.org/abs/2005.12320
YouTube Link : https://t.co/Fo3T4iSM3g
arXiv.org
SCAN: Learning to Classify Images without Labels
Can we automatically group images into semantically meaningful clusters when ground-truth annotations are absent? The task of unsupervised image classification remains an important, and open...
Overview of Data distributions
Link : https://www.kdnuggets.com/2020/06/overview-data-distributions.html
Link : https://www.kdnuggets.com/2020/06/overview-data-distributions.html
IIT Madras Invites Applications for Internships in Artificial Intelligence and Data Science, Stipend Rs 40,000
Link : https://www.google.com/amp/s/www.dqindia.com/iit-madras-invites-applications-internships-artificial-intelligence-data-science-stipend-rs-40000/amp/
Link : https://www.google.com/amp/s/www.dqindia.com/iit-madras-invites-applications-internships-artificial-intelligence-data-science-stipend-rs-40000/amp/