Some useful telegram channels to learn data analytics & data science
Python interview books
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https://t.iss.one/dsabooks
Data Analyst Interviews
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https://t.iss.one/DataAnalystInterview
SQL for data analysis
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https://t.iss.one/sqlanalyst
Data Science & Machine Learning
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https://t.iss.one/datasciencefun
Data Science Projects
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https://t.iss.one/pythonspecialist
Python for data analysis
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https://t.iss.one/pythonanalyst
Excel for data analysis
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https://t.iss.one/excel_analyst
Power BI/ Tableau
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https://t.iss.one/PowerBI_analyst
Data Analysis Books
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https://t.iss.one/learndataanalysis
Python interview books
👇👇
https://t.iss.one/dsabooks
Data Analyst Interviews
👇👇
https://t.iss.one/DataAnalystInterview
SQL for data analysis
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https://t.iss.one/sqlanalyst
Data Science & Machine Learning
👇👇
https://t.iss.one/datasciencefun
Data Science Projects
👇👇
https://t.iss.one/pythonspecialist
Python for data analysis
👇👇
https://t.iss.one/pythonanalyst
Excel for data analysis
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https://t.iss.one/excel_analyst
Power BI/ Tableau
👇👇
https://t.iss.one/PowerBI_analyst
Data Analysis Books
👇👇
https://t.iss.one/learndataanalysis
Telegram
Data Analyst Interview Resources
Join our telegram channel to learn how data analysis can reveal fascinating patterns, trends, and stories hidden within the numbers! 📊
For ads & suggestions: @love_data
For ads & suggestions: @love_data
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Hi guys, this is a group for people who want to discuss, solve, and work on a kaggle competitions & projects.
👇👇
https://t.iss.one/Kaggle_Group
👇👇
https://t.iss.one/Kaggle_Group
Telegram
Data Science & Analytics Group
The group aims to help people who want to start their career in data science or data analytics.
Feel free to discuss competitions, ask for help and share ideas for your next projects.
Admin: @love_data
Feel free to discuss competitions, ask for help and share ideas for your next projects.
Admin: @love_data
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2301.04856.pdf
39.1 MB
Multimodal Deep Learning
This book is the result of a seminar in which we reviewed multimodal approaches and attempted to create a solid overview of the field, starting with the current state-of-the-art approaches in the two subfields of Deep Learning individually.
This book is the result of a seminar in which we reviewed multimodal approaches and attempted to create a solid overview of the field, starting with the current state-of-the-art approaches in the two subfields of Deep Learning individually.
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Free Books, Courses & Certificates to learn Data Analytics & Data Science for beginners
Free Courses, Projects & Internship for data analytics
FREE Data Analytics Online Courses from Udacity
Free courses to learn Data Science in 2023
Complete Roadmap with Free Resources to become a data analyst
Free Resources to learn Python
Free Certification Courses from Microsoft to try in 2023
Share our channel for more free resources: https://t.iss.one/udacityfreecourse
#datascience #dataanalytics
Free Courses, Projects & Internship for data analytics
FREE Data Analytics Online Courses from Udacity
Free courses to learn Data Science in 2023
Complete Roadmap with Free Resources to become a data analyst
Free Resources to learn Python
Free Certification Courses from Microsoft to try in 2023
Share our channel for more free resources: https://t.iss.one/udacityfreecourse
#datascience #dataanalytics
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ChatGPT for Data Scientist
👇👇
https://www.linkedin.com/posts/sql-analysts_chatgpt-for-data-science-activity-7128583314378043393-kYzL
👇👇
https://www.linkedin.com/posts/sql-analysts_chatgpt-for-data-science-activity-7128583314378043393-kYzL
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Machine Learning & Artificial Intelligence | Data Science Free Courses
ChatGPT for Data Scientist 👇👇 https://www.linkedin.com/posts/sql-analysts_chatgpt-for-data-science-activity-7128583314378043393-kYzL
Like the post and share it with your friends so that it reaches more data aspirants 😄
Useful Telegram Channels to boost your career 😄👇
Free Courses with Certificate
Web Development
Data Science & Machine Learning
Programming books
Python Free Courses
Data Analytics
Ethical Hacking & Cyber Security
English Speaking & Communication
Stock Marketing & Investment Banking
Excel
ChatGPT Hacks
SQL
Tableau & Power BI
Coding Projects
Data Science Projects
Jobs & Internship Opportunities
Coding Interviews
Udemy Free Courses with Certificate
Cryptocurrency & Bitcoin
Python Projects
Data Analyst Interview
Data Analyst Jobs
Python Interview
ChatGPT Hacks
ENJOY LEARNING 👍👍
Free Courses with Certificate
Web Development
Data Science & Machine Learning
Programming books
Python Free Courses
Data Analytics
Ethical Hacking & Cyber Security
English Speaking & Communication
Stock Marketing & Investment Banking
Excel
ChatGPT Hacks
SQL
Tableau & Power BI
Coding Projects
Data Science Projects
Jobs & Internship Opportunities
Coding Interviews
Udemy Free Courses with Certificate
Cryptocurrency & Bitcoin
Python Projects
Data Analyst Interview
Data Analyst Jobs
Python Interview
ChatGPT Hacks
ENJOY LEARNING 👍👍
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How to enter into Data Science
👉Start with the basics: Learn programming languages like Python and R to master data analysis and machine learning techniques. Familiarize yourself with tools such as TensorFlow, sci-kit-learn, and Tableau to build a strong foundation.
👉Choose your target field: From healthcare to finance, marketing, and more, data scientists play a pivotal role in extracting valuable insights from data. You should choose which field you want to become a data scientist in and start learning more about it.
👉Build a portfolio: Start building small projects and add them to your portfolio. This will help you build credibility and showcase your skills.
👉Start with the basics: Learn programming languages like Python and R to master data analysis and machine learning techniques. Familiarize yourself with tools such as TensorFlow, sci-kit-learn, and Tableau to build a strong foundation.
👉Choose your target field: From healthcare to finance, marketing, and more, data scientists play a pivotal role in extracting valuable insights from data. You should choose which field you want to become a data scientist in and start learning more about it.
👉Build a portfolio: Start building small projects and add them to your portfolio. This will help you build credibility and showcase your skills.
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This channels is for Programmers, Coders, Software Engineers.
0- Python
1- Data Science
2- Machine Learning
3- Data Visualization
4- Artificial Intelligence
5- Data Analysis
6- Statistics
7- Deep Learning
8- programming Languages
✅ Best channels on Telegram:
https://t.iss.one/addlist/JbC2D8X2g700ZGMx
✅ Free Courses with Certificate:
https://t.iss.one/free4unow_backup
0- Python
1- Data Science
2- Machine Learning
3- Data Visualization
4- Artificial Intelligence
5- Data Analysis
6- Statistics
7- Deep Learning
8- programming Languages
✅ Best channels on Telegram:
https://t.iss.one/addlist/JbC2D8X2g700ZGMx
✅ Free Courses with Certificate:
https://t.iss.one/free4unow_backup
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The Data Science skill no one talks about...
Every aspiring data scientist I talk to thinks their job starts when someone else gives them:
1. a dataset, and
2. a clearly defined metric to optimize for, e.g. accuracy
But it doesn’t.
It starts with a business problem you need to understand, frame, and solve. This is the key data science skill that separates senior from junior professionals.
Let’s go through an example.
Example
Imagine you are a data scientist at Uber. And your product lead tells you:
We say that a user churns when she decides to stop using Uber.
But why?
There are different reasons why a user would stop using Uber. For example:
1. “Lyft is offering better prices for that geo” (pricing problem)
2. “Car waiting times are too long” (supply problem)
3. “The Android version of the app is very slow” (client-app performance problem)
You build this list ↑ by asking the right questions to the rest of the team. You need to understand the user’s experience using the app, from HER point of view.
Typically there is no single reason behind churn, but a combination of a few of these. The question is: which one should you focus on?
This is when you pull out your great data science skills and EXPLORE THE DATA 🔎.
You explore the data to understand how plausible each of the above explanations is. The output from this analysis is a single hypothesis you should consider further. Depending on the hypothesis, you will solve the data science problem differently.
For example…
Scenario 1: “Lyft Is Offering Better Prices” (Pricing Problem)
One solution would be to detect/predict the segment of users who are likely to churn (possibly using an ML Model) and send personalized discounts via push notifications. To test your solution works, you will need to run an A/B test, so you will split a percentage of Uber users into 2 groups:
The A group. No user in this group will receive any discount.
The B group. Users from this group that the model thinks are likely to churn, will receive a price discount in their next trip.
You could add more groups (e.g. C, D, E…) to test different pricing points.
1. Translating business problems into data science problems is the key data science skill that separates a senior from a junior data scientist.
2. Ask the right questions, list possible solutions, and explore the data to narrow down the list to one.
3. Solve this one data science problem
Every aspiring data scientist I talk to thinks their job starts when someone else gives them:
1. a dataset, and
2. a clearly defined metric to optimize for, e.g. accuracy
But it doesn’t.
It starts with a business problem you need to understand, frame, and solve. This is the key data science skill that separates senior from junior professionals.
Let’s go through an example.
Example
Imagine you are a data scientist at Uber. And your product lead tells you:
👩💼: “We want to decrease user churn by 5% this quarter”
We say that a user churns when she decides to stop using Uber.
But why?
There are different reasons why a user would stop using Uber. For example:
1. “Lyft is offering better prices for that geo” (pricing problem)
2. “Car waiting times are too long” (supply problem)
3. “The Android version of the app is very slow” (client-app performance problem)
You build this list ↑ by asking the right questions to the rest of the team. You need to understand the user’s experience using the app, from HER point of view.
Typically there is no single reason behind churn, but a combination of a few of these. The question is: which one should you focus on?
This is when you pull out your great data science skills and EXPLORE THE DATA 🔎.
You explore the data to understand how plausible each of the above explanations is. The output from this analysis is a single hypothesis you should consider further. Depending on the hypothesis, you will solve the data science problem differently.
For example…
Scenario 1: “Lyft Is Offering Better Prices” (Pricing Problem)
One solution would be to detect/predict the segment of users who are likely to churn (possibly using an ML Model) and send personalized discounts via push notifications. To test your solution works, you will need to run an A/B test, so you will split a percentage of Uber users into 2 groups:
The A group. No user in this group will receive any discount.
The B group. Users from this group that the model thinks are likely to churn, will receive a price discount in their next trip.
You could add more groups (e.g. C, D, E…) to test different pricing points.
In a nutshell
1. Translating business problems into data science problems is the key data science skill that separates a senior from a junior data scientist.
2. Ask the right questions, list possible solutions, and explore the data to narrow down the list to one.
3. Solve this one data science problem
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