p.y.b:
Here is a list of what I believe are the 10 Practical Steps for #DataScience:
1. Programming
a. Python - https://lnkd.in/gGQ7cuv
b. R - https://lnkd.in/giMGbph
c. SQL - https://lnkd.in/gM8nMNP
d. Command Line - https://lnkd.in/e3EQuis
2. Stats/Prob/Math
a. Coursera's Statistics w/ R - https://lnkd.in/gGT9NEf
b. edX's Probability - https://lnkd.in/gpUyC3P
c. Khan Academy Linear Algebra - https://lnkd.in/gMshbX4
3. Data Viz
a. Python Matplotlib- https://lnkd.in/gr3ifNt
b. R ggplot2 - https://lnkd.in/eThJXNr
4. Data Manipulation
a. Python Pandas - https://lnkd.in/g9kfpX4
b. R dplyr - https://lnkd.in/gAWusih
5. #MachineLearning
a. Google Crash Course - https://lnkd.in/gSgkVcT
b. Stanford Coursera - https://lnkd.in/g8ZG557
c. ISLR Book - https://lnkd.in/gk8GPZC
6. Experimental Design
a. Udacity A/B Testing - https://lnkd.in/gCerh4f
7. Business Sense
a. Metrics - https://lnkd.in/gZAG7bS
8. Communication
a. Storytelling - https://lnkd.in/gwjxVUu
9. Profile Building
a. GitHub - https://lnkd.in/g4r9naJ
b. LinkedIn - https://lnkd.in/g-KHHEC
c. Kaggle - https://lnkd.in/gBC77Hu
d. DS Resume - https://lnkd.in/gU8WVAF
π 10. Job Search
a. Daily Expert Tips & Advice - https://lnkd.in/g8z-xXD
---
Hope this helps! π
Updated on my site - https://www.claoudml.co/
Here is a list of what I believe are the 10 Practical Steps for #DataScience:
1. Programming
a. Python - https://lnkd.in/gGQ7cuv
b. R - https://lnkd.in/giMGbph
c. SQL - https://lnkd.in/gM8nMNP
d. Command Line - https://lnkd.in/e3EQuis
2. Stats/Prob/Math
a. Coursera's Statistics w/ R - https://lnkd.in/gGT9NEf
b. edX's Probability - https://lnkd.in/gpUyC3P
c. Khan Academy Linear Algebra - https://lnkd.in/gMshbX4
3. Data Viz
a. Python Matplotlib- https://lnkd.in/gr3ifNt
b. R ggplot2 - https://lnkd.in/eThJXNr
4. Data Manipulation
a. Python Pandas - https://lnkd.in/g9kfpX4
b. R dplyr - https://lnkd.in/gAWusih
5. #MachineLearning
a. Google Crash Course - https://lnkd.in/gSgkVcT
b. Stanford Coursera - https://lnkd.in/g8ZG557
c. ISLR Book - https://lnkd.in/gk8GPZC
6. Experimental Design
a. Udacity A/B Testing - https://lnkd.in/gCerh4f
7. Business Sense
a. Metrics - https://lnkd.in/gZAG7bS
8. Communication
a. Storytelling - https://lnkd.in/gwjxVUu
9. Profile Building
a. GitHub - https://lnkd.in/g4r9naJ
b. LinkedIn - https://lnkd.in/g-KHHEC
c. Kaggle - https://lnkd.in/gBC77Hu
d. DS Resume - https://lnkd.in/gU8WVAF
π 10. Job Search
a. Daily Expert Tips & Advice - https://lnkd.in/g8z-xXD
---
Hope this helps! π
Updated on my site - https://www.claoudml.co/
lnkd.in
LinkedIn
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Forwarded from Deleted Account
mlearning.pdf
2.6 MB
ODSC is bringing you Blockbuster workshop in Quantitative Finance+ Data Science absolutely FREE. The workshop has three presenters from diverse domains coming together to deliver it to you on June 29th ..Hurry Up!!! Limited seats Only.
Pankaj is Quantitative Finance researcher for State Street who is also one CFA level 2 candidate
Abinash Panda is CEO and Founder of Prodios is the Founding member of the famous pgmpy package. He has also written two books for Pakt publications in Probabilistic Graphical Models and Markov Models
Usha Rengaraju is an expert in Quantitative Finance and Bayesian Networks.
The workshop will also be followed by the AMA session by Swiggy Data Science Leaders.
RSVP here : https://bit.ly/2IiAzGc
#datascience #odsc #openai #neuralnetworks #ml #deeplearning #analytics #machinelearning #ai #artificialintelligence
@Machine_learn
Pankaj is Quantitative Finance researcher for State Street who is also one CFA level 2 candidate
Abinash Panda is CEO and Founder of Prodios is the Founding member of the famous pgmpy package. He has also written two books for Pakt publications in Probabilistic Graphical Models and Markov Models
Usha Rengaraju is an expert in Quantitative Finance and Bayesian Networks.
The workshop will also be followed by the AMA session by Swiggy Data Science Leaders.
RSVP here : https://bit.ly/2IiAzGc
#datascience #odsc #openai #neuralnetworks #ml #deeplearning #analytics #machinelearning #ai #artificialintelligence
@Machine_learn
Meetup
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Forwarded from Omid
I gladly announce my first online course on #Statistics and #Mathematics for #MachineLearning and #DeepLearning.
The course will be in English, QA sessions with instructor will be in Turkish, Azerbaijani , or English. TA sessions will be in English.
This is the first course of tribology courses to help attendees to capture foundations and mathematics behind ML,DL models.
The courses are listed as follow:
1. Statistics Foundation for ML
2. Introduction to Statistical Learning for ML
3. Advanced Statistical Learning for DL
The course starts on 15 Jan 2022, at 13:00 to 15:00 (Istanbul time):
Course Fee:
Free for unemployed attendees. :)
200 USD for employed candidates :).
Course contents:
https://lnkd.in/dcXKxUjE
Course Registration:
https://lnkd.in/dMpzMfMG
Please kindly share with the ones who are interested.
The course will be in English, QA sessions with instructor will be in Turkish, Azerbaijani , or English. TA sessions will be in English.
This is the first course of tribology courses to help attendees to capture foundations and mathematics behind ML,DL models.
The courses are listed as follow:
1. Statistics Foundation for ML
2. Introduction to Statistical Learning for ML
3. Advanced Statistical Learning for DL
The course starts on 15 Jan 2022, at 13:00 to 15:00 (Istanbul time):
Course Fee:
Free for unemployed attendees. :)
200 USD for employed candidates :).
Course contents:
https://lnkd.in/dcXKxUjE
Course Registration:
https://lnkd.in/dMpzMfMG
Please kindly share with the ones who are interested.
lnkd.in
LinkedIn
This link will take you to a page thatβs not on LinkedIn
π1