In 2023 disappear for 6 months; learn these 8 skills, and you'll be a millionaire by 2028…

SQL is the most important skill to develop if you are aspiring to be in the Data Industry. Use this as your reference next time working with it

SQL is easy to learn but hard to master. Here's a roadmap that can help you learn it: /🧵/

Microsoft Excel is crucial in Finance. I've built more than 100 Financial models in Excel over the years. Here are all shortcut keys you should know:

Most Data Analysts find it hard to select the right visuals for a PowerBi report. Here are 4 mistakes to avoid when selecting a PowerBi chart visual. A Thread.

Worried about AI taking technical jobs? Here are my thoughts: 🔻 Concerns Surrounding AI taking jobs: 1️⃣ Automation of repetitive tasks: AI algorithms can streamline data cleaning, aggregation, and basic analysis, potentially reducing the need for human intervention and

Microsoft Build 2023 event was a game-changer. The operating system game is about to change. Here are 5 things you need to know:

Right now, I believe we are in an AI hype cycle. There's an impression that AI is going to solve all of our problems while simultaneously take all of our jobs in the next few years. In the next 6 months I think we will be seeing more and more companies actually trying to

Training an ML model inside a Jupyter notebook is something every data scientist knows 🏋️ But do you know how to create a real-world ML service that makes a difference for the company you work for? 📈 If the answer is NO, this thread is for you 🤗🧵↓

Most ML courses teach you how to build the perfect ML model... ... and only then start thinking about deploying it. And this is why most ML prototypes in real-world projects do not make it into production. Is there a better way? 🤔 Yes, there is. Let me explain ↓

If you are using jupyter notebooks for Python and Data Science, try these 7 magic commands that will save you a ton of time: 🧵👇 1. Chat with ChatGPT directly from Jupyter Notebooks

Machine Learning Tools 🤖 Development • PyCharm • VScode • Jupyter Training • PyTorch Lightning • Scikit-Learn • TensorFlow • LightGBM • XGBoost • PyTorch Tuning • Keras tuner • Hyperopt • Optuna Optimization • SparseML Deployment / Packaging • Hugging Face

I started my career in machine learning back in 2017 ⌛️ Here are 5 critical skills for landing a job in the industry ✨ --A Thread-- 🧵

What does an 𝗘𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗘𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗘𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁 look like? MLOps practices are there to improve Machine Learning Product development velocity, the biggest bottlenecks happen when Experimentation Environments

Convert PDF to docx using Python

A few days back, 2 Data Analyst newbies showed me mistakes they encountered during their analysis process. It dawned on me that lots of times, we don't diligently follow through the data analysis process, especially the cleaning phase. In this short🧵, I break down this process.

If I were to start Data Science in 2023, - Harvard - Stanford - MIT - Microsoft - Google - IBM ❯ Python cs50.harvard.edu/python/2022/ ❯ SQL online.stanford.edu/courses/soe-yd… ❯ Excel, PowerBI learn.microsoft.com/en-gb/training… ❯ R cognitiveclass.ai/courses/r-101 ❯ Mathematics

SQL BASICS

Here is a very realistic breakdown of where you'll spend your time as a Data Analyst: 📥Data Collection and Access (15%): This involves understanding the client's data needs, identifying the necessary data sources, and getting access to this data. This often involves arguing

Excel has played a pivotal role in my career. Excel may just be software to some, but for me, it's an enabler of data stories waiting to be told. I had so much fun creating this dashboard. (I wonder what we would do without free data) link.medium.com/96qE9dX59Ab