Best tweets about data science

You cannot know everything in Data Science ❌ Being in industry has taught me to learn along the way ⌛️ Onwards & Upwards 📈

For data science: JavaScript or Python? 👨‍💻👩‍💻

The hiring season will be here before you know it 💰 Make yourself Data Science interview ready with these resources ⏬

How to learn Data science skills effectively?👀🤔 • Watch/read/learn concepts first • Take notes, summarise the information • Practice/apply in datasets and solve problems • Communicate Or discuss with your peers what have you learned👍

I started my career in Data Science back in 2016. Here's a list of top Blogs/Newsletters that helped me keep up the pace 🚀

Data science cannot be fully automated. No amount of parameter tuning, benchmarking, automation, model comparison, automated feature engineering, etc., can automatically figure out what data column location_123_old contains and whether it should be a feature or not.

I started a self-taught data science journey five years ago. If I had to pick one place to learn SQL? mode .com/sql-tutorial/ What you get: • Full scope Analytics project • Beginner through advanced exercises • Virtual coding + dashboard environment

Pick an area you like, and improve on it ✅ I choose Data Science few years back, and it gave me a 8X growth in salary in 2 years📈 #DataScience

A career in Data Science is a journey, not a destination 📈#DataScience

TOP 10 Industries for data science👍💻 1. Finance 2. Retail 3. Technology 4. Healthcare 5. Pharma 6. Telecom 7. Automotive 8. Cybersecurity 9. Energy 10. Utilities

⌛️ Choosing Data Science as a career! Folks, who are working in different fields, and wants to shift to Data Science 👉 Start Up-Skilling yourself now.

Good data engineering is a pre-requisite for data science

Unpopular opinion: Data science is 90% data and 10% science.

Quickly learn both Python & JavaScript? ❍ Data Science ❍ AI/ML ❍ Web Development Exciting career choices. Wanna join? ⇩

Here are some Data Science Communities that you can be part of > Kaggle > Towards Data Science > Reddit Data Science Community > Learn AI Together > Open Data Science > IBM Data Science Community > C4ML

When I started learning data tech stack I thought 'pandas' is the boss of all data science libraries But after deep diving into Deep learning techniques, I found that 'Numpy' is the boss of all bosses.📈

I am starting #100DaysOfCode challenge again from tomorrow. Will be building more project around Python & Data Science and learning new things in the field. Wish me luck I can have consistency this time.

5 Github repositories that will help you in your Data science and Machine Learning Journey: 🚀

20 simple ideas I would tell anyone learning data science: 1. Building intuition for how techniques work is the most powerful tool you have. 2. A great individual model is better to have than an ensemble of decent models. (keep reading)

I've got a couple of "interesting" Data Science/Analytics Portfolio project ideas for anyone who is just starting out, looking to build their portfolio for job search in Nigeria or out. Happy to support/mentor on the project. Would this be interesting for anyone(s)😉? #data

Youtube channels posting detailed beginner level Tableau Tutorials: 📽️ Data Science Tutorials 📽️ stepbystepdatascience 📽️ Gronify 📽️ Abhishek Agarrwal 📽️ sqlbelle

Career paths you can pivot to after being a data analyst: 🔸Data Science 🔸Data Engineering 🔸Analytics Engineering 🔸Consulting 🔸Scrum master 🔸Product Owner 🔸Software engineering 🔸BI Developer 🔸Management Data analysis is not the end station. Think ahead!

Note to self: Statistics = Math Statistics + RStudio = Data Analytics Statistics + RStudio+ Model = Machine Learning Statistics + RStudio + Model + Domain Knowledge = Data Science

TOP 10 Roles for data science👍💻🚀 1. Data Scientist 2. ML engineer 3. Data analyst 4. ETL engineer 5. Data engineer 6. Analytics Manager 7. Tableau developer 8. Research scientist 9. BI analyst 10. Business analyst Which one are you targeting to build your career in??

I was laid off by Twitter today. I’m a data analytics researcher and engineer looking to possibly transition into DN engineering roles or other data science roles. Please DM me if you have any leads!

Tech stacks for different fields: Game Dev: Unity, Unreal engine, Three JS, C++, C# Web Dev: JS, HTML, CSS, Python, Node, Rails, Mongo Data Science: Python, Snowflake, MySQL, MOngo Apps: Swift, Kotlin, Flutter, React Native, Ionic What is your field and tech stack?

Learning Data Science is self-care. Learning Data Science is self-improvement. Learning Data Science is self-actualization.

What's one tip that seriously boosted your data science skills?

Nobody tells you this but data cleaning and data preparation is the single most important and hardest part of any data science project life cycle. Focus on learning data understanding and preparation than modeling.

An SQL Developer's Roadmap Helpful in, ❍ Database Development ❍ Database Administration ❍ Web Development ❍ Fullstack ❍ Data Science ❍ Data Analysis Beginner to Pro in a few days. ⇩

You don't have to spend $25,000+ for a Bootcamp to get started with Data Science 🚫 Instead, take these free courses from Google, learn the fundamentals & receive certificates 🌟

200+ Data Science interview questions 🔸 Supervised machine learning (linear models, trees, neural nets) 🔸 Feature selection, parameter tuning 🔸 Unsupervised learning (clustering, dim reduction) 🔸 Recommenders and search 🔸 SQL 🔸 Coding (Python), algorithms With answers 👇

I no longer swipe my rewards card at any other retailer. I want their data science team to feel the pain & mental gymnastics of sniffing through masked pan data to find out if I’m a loyal customer or an infrequent unknown.

How did you start your data science journey? For Me: Google + YouTube + Kaggle

5 types of python interview questions to prepare for data science job roles💻👨‍💻 1. Grouping, aggregation, and ordering data 2. Joining tables 3. Filtering data 4. Text manipulation 5. Date time manipulation

💎 SQL Roadmap Data Science Web/Mobile ⭣ Fullstack ⬊ ⭣ ⬋ 🛢 ⇨ Oracle ⇦ ⇨ MySQL ⇦ ⇨ SQL Server ⇦ ⇨ PostgreSQL ⇦ ⇨ MariaDB ⇦ ⇩

What do you want to learn or improve? 🌟 - Cloud ☁️ - DevOps 🛠️ - Data Science 📊 - Web Dev 💻 - Other 👇

Tools that cover 90% of data science use cases 🔸 Git 🔸 Bash 🔸 SQL 🔸 NumPy 🔸 Pandas 🔸 Scikit-Learn 🔸 Flask 🔸 Docker Focus on them first

I want some data demotivational posters... JSON - When you're too lazy to actually think about your data model Snowflake - When you want to screw over your future CFO Data Science - When you make a basic chart in Excel but want to be paid more. 😈

Lifecycle of a Data Science Project → Defining the Problem Statement → Data Collection → Data Cleaning → Feature Engineering → Feature Selection → Data Preparation(Train, Validation & Test Split) → Model Building & Evaluation → Deployment → Model Monitoring & Retraining