Discover and read the best of Twitter Threads about #datascientists

Most recents (24)

Ahoy #oceaners 👋

I thought to make all of your weekend more fun and exciting , so, The 2nd part of the @oceanprotocol megathread is here !

Grab an espresso or cappuccino or whatever drink you prefer and enjoy the beauty of what $ocean has created .

#web3 #AI #DataScience Image
In the previous megathread , I explained why we need to actually share data as it will bring a lot of value to humanity and lead to an acceleration in advancement of #AI . I also explained the glorious $ocean data market and how beautifully the way it functions .
I solely don't believe in this, even the World economic forum praised the work of @oceanprotocol by awarding them with the World economic forum Technology pioneer award and we also have big organisations like German central bank, @Unilever , Daimler-Mercedes Benz Singapore ...
Read 23 tweets
BIG NEWS: #ChatGPT breaks #Python vs #R Barriers in Data Science!

Data science teams everywhere rejoice.

A mind-blowing thread (with a FULL chatgpt prompt walkthrough). 🧵

#datascience #rstats

This is 1 example of how ChatGPT can speed up data science & GET R & PYTHON people working together.

(it blew my mind)
This example combines #R, #Python, and #Docker.

I created this example in under 10 minutes from start to finish.
Read 25 tweets
#DataScience #DataAnalytics #DataScientists #programming #Python #100DaysOfCode
Simple Thread 🧵🧵🧵
A data analyst is a professional who works with data to uncover insights and help businesses make informed decisions. Here are the steps to becoming a data analyst
✅ Obtain a degree: While it is possible to become a data analyst without a formal degree, having a degree in a related field, such as statistics, mathematics, computer science, or data science, can be beneficial.
✅ Learn programming languages: Programming languages such as Python, R, SQL, and Java are commonly used in data analysis. Learning these languages can help you analyze data more effectively.
Read 11 tweets
" Data Analyst Project on Hotel Booking "

That you can add in your resume or portfolio to showcase your skills 💯

◻ In Recent Year , City Hotel & Resort Hotel have seen High Cancellation Rates. Each Hotel is now Dealing with number of issue as result including Fewer #revenue & Less than ideal Hotel room use.
🔹 Insights :

1️⃣ More Cancellation occur when prices are higher
2️⃣ When there is Longer waiting list , Customer tend to Cancel more frequently
3️⃣ The majority of Clients are coming from a offline #travel agents to make their #reservations
Read 6 tweets
" Exploratory Data Analysis on Terrorism "

We are performed Exploratory Data Analysis on terrorism #dataset to find out the hot zone of #terrorism. #EDA nothing but #analyzing the given data & finding the #trends, patterns & making some assumptions. #DataVisualization #DataScience #MachineLearning
In this #dataset, there are many features including countries, states, regions, gang names, weapon types, target types, years, months, days, and many more features.
Read 8 tweets
Top 25 SQL Interview Questions and Answers

There are certain SQL concepts which you should be familiar with if you plan to attend an #SQL interview. No matter which RDBMS you use wether it is MySQL, Oracle, Microsoft SQL Server, #PostgreSQL or any other, these SQL concepts are common for all of the popular RDBMS.
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Are you a wine enthusiast? If you are, then you’ve probably heard of Wine Magazine.

🔥Let us solve one of the Advanced SQL interview questions on RANK function.

❓ Question is taken from Wine Magazine that involves clauses like PARTITION BY, UNION ALL, CASE statements, and Window functions.

We will use a 3-step framework to solve the problem that can be used to solve any coding problem.
Find the cheapest and the most expensive variety in each region. Output the region along with the corresponding most expensive and the cheapest variety. Be aware that there are 2 region columns, the price from that row applies to both of them.
Read 6 tweets
12 Best #SQL Online Course Certificate Programs for #DataScience in 2023 — compiled by @tut_ml
#Databases #BigData #DataScientists…
7 Best Advanced #SQL Courses & Training Online You Must Know in 2023 — compiled by @tut_ml
#BigData #DataScience #DataScientists #Coding #Database #Analytics…
Read 3 tweets
12 Best FREE #SQL Courses and Certifications Online in 2023 — compiled by @tut_ml
#Databases #DataScience #BigData #Analytics #DataScientists #100DaysOfCode…
#SQL Notes for Professionals:
[free 166-page PDF download]
#Database #DataScience #DataAnalytics #DataScientists #DataProfiling
Read 4 tweets
🚀 You can become a data analyst in 90 days without spending a cent! Yes, you heard that right.

With the right approach and dedication, anyone can become a #dataanalyst . Here are the steps to follow( Make sure the order is maintained)

Learn Excel - Time : 12 days
🎙️ Tutorials -
🏗️ Projects -
Learn Basic Statistics - Time : 3 days
🎙️ Tutorials -
Read 10 tweets
🐍Python is easy!

You can learn enough #Python in 8-10 hours to pick the rest up with active practice.

Here are my top 5 #free places to catch the basics:

1/ Codeacademy (where I first learnt Python) -
2/ MITx 6.0001 (this series is a brilliant intro to CS) -
Read 7 tweets
🤯¡Los datos ausentes están por todas partes!😜
👉Pueden invalidar los resultados de tu estudio
👉Muchas funciones utilizan métodos automáticos que pueden no ser óptimos
👉El impacto de la falta de datos es un tema que la mayoría quiere evitar, pero hoy no
¿Qué hacer con los NA?:
🎯Necesitas identificar los datos ausentes, averiguar por qué y cómo faltan:
- errores humanos
- interrupciones del flujo de datos (e.g. meses)
- problemas de privacidad
- sesgo (e.g. tipos de participantes del estudio que tienen >NA)

¡Es info clave para intentar solucionarlo!
Explora los datos con los paquetes:
✅ visdat
✅ naniar

Un ejemplo con los 3:… ImageImageImageImage
Read 9 tweets
Many courses teach #DataScience libraries such as pandas, matplotlib and seaborn, but @TedPetrou’s self-paced online course “Master Data Analysis with #Python” teaches BEST PRACTICES in using the libraries to help you become an expert!
Start here now:… Image
532-page #Python #coding book by @TedPetrou >> "Pandas Cookbook: Recipes for Scientific Computing, #TimeSeries Analysis, and Data Visualization" at
#100DaysOfCode #DataScience #DataScientists #DataViz #MachineLearning #ComputationalScience Image
Explore these self-paced online courses that help you master the tools of #DataScience:
1) Master #MachineLearning with #Python:…
2) Build a Data Analysis Library from Scratch in Python:…
3) All-Access Pass:… Image
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And the winner of the 2022 SAS Hackathon is...
(awarded by 100+ jurors from among 71 contributing teams from 50 countries, co-sponsored by Microsoft)
#SASVisionary #SASExplore @SASsoftware #AI #MachineLearning #DataScience #DataScientists
Keep alert for the 2023 #SASHackathon, coming in a few months.
@SASsoftware #SASExplore #SASVisionary #AI #MachineLearning #DataScience #DataScientists
Here's how I promoted it earlier this year...…
Congrats to the #SASHackathon Winner!
See more news on
@SASsoftware #SASExplore #SASVisionary
Read 5 tweets
#DataScientists, DataOps Engineers, Business Data Users/Analysts, #ML/#AI Engineers & more! Join me + 1000's of other Data Enthusiasts at @SASsoftware EXPLORE online for FREE => 100+ sessions, peer breakouts, & free training:…
#SASVisionary #ExploreSAS Image
Some cool @SASsoftware EXPLORE sessions TODAY:
* Leverage KT Charts with Streaming Data to Improve Industrial Processes
* Banking Hyperautomation with SAS Intelligent Decisioning
* Ensuring Fairness in Analytics-Driven Decision-Making
* Data Catalogs Ignite Your Analytics Journey Image
🌟By the way, the above are just 4 of the 100's of sessions this week at #SASExplore. We've just started. We are in the opening session now — there's tons more yet to come.
#SASVisonary @SASsoftware #AI #ML #DataScience #MLOps #Analytics Image
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If there's one conversation #DataScientists or #MachineLearning engineers dread, it's...

📊Explaining accuracy to a non-technical stakeholder

Too high-level, and they suspect you're hiding something. Too granular, and they'll be lost in the weeds.

My solve? Visualize F1 👇
First, I find F1 navigates simplicity and power. This lets me earn trust by briefly explaining the downside of traditional Accuracy:

"Imagine predicting fraud where only 1% of the transactions were fraudulent. If the model predicted that none were fraudulent, it would be..."
Your stakeholder will instantly understand -- "99% Accurate!"

Use that to explain the power of F1:

"F1 measures how well the model is doing at finding that 1%, and only that 1%."

I have yet to find a stakeholder that would at least hear me out after that. So next...
Read 16 tweets
------------------Feature Engineering----------------------

The success of all Machine Learning algorithms depends on how you present the data. Every model gets input data and gives us an output. When your goal is to get the best possible output from input,

You need to present the best data to the model. This is a problem that Feature Engineering solves. Feature Engineering refers to the process of using the domain of Knowledge to extract features from raw data.

In other words, Feature Engineering selects the most useful features from our raw data and presents them to our model, whereby we improve the performance of our model.

(hopefully, you get the point 😀).

Read 10 tweets
Let's assume you have three Features(age, height, salary) in your example.
The first feature varies from 1 to 90. The second one varies from 120 to 210 and the Third one varies from 1000 Euro to 4500 Euro.
As you can see the value of your features are in a different range. In this case, if you want to use gradient descent to find optimum parameters for your model( for instance linear regression), that leads to a slow speed of your model to converge. In this case,
you can utilize Feature Scaling to bring the value of features in a range from 0 to 1 depending on the Scaling technique, that you use. So you improve the speed of your model convergence.

Read 5 tweets
As I started to learn Data Science I didn't know what skills should I learn and where. That was a ton of content and I didn't know which one should I take. I have read more than a hundred articles and talk with some of my data scientist friend and gathered experience
during my journey. I want to share a roadmap and skills that you need as a junior Data Scientist and resources to learn.

1- Start with a language programming and best of all Python. You can learn Python from 3 resources.
Taking one of these courses is enough.

A- 2022 Complete Python Bootcamp From Zero to Hero in Python by Jose Portilla in Udemy.

Jose Portilla is my favorite instructor. This Course has a GitHub repo where you can access Codes there.…

Read 19 tweets
The 21st century is ruled by data and hence, the demand for efficient #DataScientists continues to rise exponentially.

Here are some critical data science skills that are required to be employable and excel in this profession.

🔹Save this for future reference.

🔹 A strong foundation of the fundamentals

Understanding the basics of ML, AI, and data science is essential. One has to understand the differences between DL and ML and the distinctions between business analytics, data science, and data engineering.
🔹 Proficiency in Programming

Python, Perl, C/C++, SQL, and Java are the various programming languages required to excel in data science roles. Due to high support for deep learning and the availability of libraries, Python is the most popular.
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“Data don’t lie”. But it typically requires a process of defining #research questions, hypotheses, methodology, interpreting and #dataviz that can introduce subjectivity and #bias. Scientific rigor and objectivity are key in #DataScience. Some #Tips for #DataScientists 🧵
Don’t dive straight into a dataset, domain knowledge is critical. Good #Science requires a theoretical understanding of a topic while #ignorance introduces bias. Sound domain knowledge enables you to ask the right questions and give relevant answers with #DataScience
Investigate the alternate hypothesis. Business questions asked to #DataScientists are often directive, as there already is a hypothesis. Don’t confirm this hypothesis without properly investigating the alternate option.
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Preparing for a technical interview for a #DataScience position? These are some of the questions that typically allow me as an interviewer to quickly distinguish between juniors and mediors, including some quick tips 🧵. #Python #pythonprogramming #DataScientist #Jobs
All questions about SQL. Not the hardest thing to learn, but many #DataScientists only start to learn the value of SQL when they actually become part of a dev team. I’m not only talking about SELECT * FROM table, but also about joins, truncates, partitions and constraints.
Interacting with an API. Make sure you know your requests (GET, POST, PUT, DELETE, PATCH), as well as the #Python requests library.
Read 10 tweets

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