7. Don’t try to learn everything The field is huge You don’t need to master every ML framework or tool Focus on the fundamentals — depth beats breadth

6. Communication is crucial If you can’t explain your findings in simple terms, your work won’t be valued Storytelling with data is more powerful than technical complexity

5. Visualization makes the difference A clear, compelling chart can communicate insights in seconds Learn tools like ggplot2, matplotlib, or even Power BI Good visuals = more impact

4. Statistics is a must ML isn’t just plugging models into data You need to understand distributions, p-values, hypothesis testing, confidence intervals... Otherwise, you might draw the wrong conclusions

3. SQL is not optional I thought Python or R would be enough But most data lives in databases Knowing SQL is just as important as knowing how to code

2. Models aren't everything A model with 99% accuracy that no one uses is worthless Focus on solving business problems Learn to communicate the value of your work and understand what stakeholders actually need

1. Data cleaning is 80% of the job I thought I'd be building deep learning models from day one... In reality, most of your time will be spent cleaning and prepping data Master pandas, dplyr, and preprocessing techniques.

💡 Things I wish I knew before becoming a Data Scientist 🚀 A thread for beginners and aspiring data scientists: 👇 🧵 #datascience #statistics #tech #DataScientist

Your model accuracy doesn't matter if your data is garbage. Data quality > algorithm choice. Always #DataScience

🧼 Pro tip: Always check the structure of your data first with str() or glimpse() from {dplyr}

📌 filter() + select() Basic but powerful to filter rows and select columns They keep your dataset focused and relevant

📌 pivot_wider() The opposite of pivot_longer() — spread rows into columns Helpful when each observation should be in a single row

📌 pivot_longer() Convert columns into rows. Perfect for making data tidy This is essential for working with time series or repeated measurements

📌 case_when() The tidyverse version of ifelse(), but more readable

📌 across() Apply transformations to multiple columns Use it inside mutate() or summarise() to save time and write cleaner code

📌 mutate() Add or transform columns You can create new variables on the fly and use existing ones in the same call

💡 dplyr and tidyr are part of the tidyverse They make it easy to transform, reshape, and clean data in a readable and consistent way Let’s explore some of the most powerful functions

🧵 How to clean data like a pro with dplyr and tidyr in R If you're still struggling with messy datasets or spending hours on manual cleanup… This thread is your shortcut to clean, tidy, analysis-ready data 👇 #rstats #DataCleaning #DataScience

💡 Design flows, pipelines, or strategies? 🛠️ Try Excalidraw: a minimalist, open-source digital whiteboard for fast, clean diagrams. Perfect for: 🔁 Data flows 🔍 Analysis strategies 🧠 Mind maps 📦 Product & dashboard design 🔗 https://t.co/jG3OEYngTm

🔍 Whether you're just getting started or thinking of switching roles: 👉 Which profile looks most like you today? 👉 Which one would you love to grow into? #datascience #MachineLearning #AI #CareerInData