#Data Processing

Computer ka dimaag kaise kaam karta hai? 💻⚡ Ye hai data processing ka real game!” . . #ComputerBasics #DataProcessing #HowComputerWorks #CPU #digital_explore_v

The best projects serve a real use case Comment “data” for all the links and project descriptions #tech #data #datascience #ml #explore

Types of Data Structure . Video by @codingwithjd . . . #coding #cppproject #cplusplusprogramming #codinglife #codingbootcamp #codingisfun #codingninjas #coder #coderlife #coderslife #codersofinstagram #programming #programmingproblems #programmers #codingdays #codingchallenge #assembly #instagramgrowth #asciiart #cmd #cmdprompt #batchprocessing #aiartcommunity #artificialintelligence #deepseek #openai #meta #metaverse

Comment “DATA” for all projects & links! #coding #datascience #machinelearning #university #student

Comment ‘Projects’ to get 5 Data Scientist Project ideas and a plan 👩🏻‍💻 ♻️ repost to share with friends. Here is how to become a data scientist in 2026 and beyond 📈 the original video was 4 min Andi had to cut it down to 3 because instagram. Should I do a part 3v what are other skills that you would add to the list and let me know what I should cover in the next video 👩🏻‍💻 #datascientist #datascience #python #machinelearning #sql #ai

Data is the new gold. Big data describes large and diverse datasets that are huge in volume and also rapidly grow in size over time. Big data is used in machine learning, predictive modeling, and other advanced analytics to solve business problems and make informed decisions. A data engineer develops, builds, maintains, and manages data pipelines. This requires working with large datasets, databases , and the software used to analyze them – including cloud systems like AWS or Azure. The primary focus of a data engineer is to ensure that data flows smoothly from its source to its destination efficiently and securely. The data engineer is the first line of data ingestion, cleaning and wrangling, and transformation using tools such as Python, PySpark and SQL. #dataengineer #tech #corporategirlie #motivation #fyp

Statistics is NOT just for statisticians. It’s the secret weapon of every Data Analyst. Each dataset hides a story, and distributions help us decode it. 👉 A quick cheat sheet for you (save this!): 1. Normal = classic bell curve 2. Uniform = equal chance 3. Binomial/Bernoulli = success vs failure 4. Poisson = rare events 5. Log Normal = skewed data 6. Gamma/Beta = flexible shapes 7. Geometric = time until first success ⚡ Knowing the right distribution = better insights, smarter decisions. Ask yourself: What story is my data’s distribution telling me? Which of these do you use most? -- Follow @jayenthakker and @metricminds.in ➕ Dedicated to helping aspiring data analysts thrive in their careers. -- #dataanalytics #datascience #data #metricminds #datavisualization #analytics #artificialintelligence #python #ml #careers #sql #careerswitch #trendingreels #foryoupage #learning

want to become a data analyst in 2026? you don’t actually need a degree in maths or loads of prior experience 🚀 most companies care more about whether you can actually work with data to understand business problems than what you studied at uni or what niche skills you have. learn the basics: SQL, Excel and a repot building tool and learn how to pull insights and recommendations using these tools. if you’re looking to break into data analytics, switch careers into data, or land your first data analyst role - focus on building practical skills and learning how to explain your insights clearly. follow for more realistic career advice from a non-tech girlie working as a data analyst in the book industry #dataanalyst #womenindata #bookindustry #careerchange

Day 3: Importing Data into Power BI (+ importing data from the web!) #dataanalyst #dataanalysis #dataanalytics #powerbi #powerquery

Comment "DATA" for the links. You Will Never Struggle With Data Science Again 📌 Learn the most important foundations with these beginner-friendly resources: 1️⃣ Learn Python for Data Science – FreeCodeCamp’s full beginner course 2️⃣ Essence of Linear Algebra – 3Blue1Brown’s visual, intuitive playlist 3️⃣ Statistics – A Full Lecture (2025) – step-by-step breakdown of core stats concepts Stop feeling overwhelmed by Python, statistics, or linear algebra. These tutorials simplify the fundamentals of Data Science with clear explanations, visuals, and real-world examples. Whether you’re preparing for a career in Data Science, getting into machine learning, or just curious about data analysis, this is the fastest way to finally understand how it all fits together. Save this post, share it, and turn confusion into clarity with Python, Stats, and Linear Algebra for Data Science 📊

A data warehouse is a single source of truth that helps business functions perform their data analysis operations easier. Here's what a simple data warehouse looks like: 1. Data sources 2. Bronze layer 3. Silver layer 4. Gold layer 5. Analytics There's so much more that goes into a data warehouse (e.g. ingestion frequency, data governance policies, data validation checks etc), but this is a high level design you can start with. Different companies may configure the stages in different ways according to their users' unique requirements, but the generic workflow applies to all! #dataanalytics #dataengineering #datascience #techtok #dejavu

This is the EXACT order I would learn Data Science in 2026. Hi 😊 my name is Dawn. I’ve been a Data Scientist at Meta, Patreon and other startups. And have coached 20+ clients into landing their dream Data jobs in the past year. 1️⃣ Learn SQL SQL is a must-have skill for every data professional because it’s the primary way you get data OUT of a database. It’s also a very easy coding language to learn, so I would start there. Use Interview Master to learn and practice SQL (link in bio): → Learn SQL: www.interviewmaster.ai/content/sql → Practice SQL: www.interviewmaster.ai/home 2️⃣ Start building Product Sense & Business Sense Product sense & business sense basically means you know how to use Data to solve real problems. I would start building this “soft” skill early because (1) it takes time to really learn this, and (2) as you’re learning Stats and Python, you already have context on how these might be used in the real world. I found the book: Cracking the PM Career to be super helpful before I landed my first Data Science job. 3️⃣ Learn Statistics How much Stats do you need for Data Science? Just the foundations, but you need to know it really really well. → Descriptive statistics → Common distributions → Probability and Bayes’ Theorem → Basic Machine Learning models → Experimentation concepts → A/B experiment design Check out Stanford’s Introduction to Statistics, which is free on Coursera. 4️⃣ Learn Python Python is the #1 skill for Data Scientists in 2025, but I put it 4th on this list because I find that it builds on skills 1-3. I learned Python on my own using DataCamp’s Python Data Fundamentals (link in bio). 5️⃣ Use AI-assisted coding tools Many data scientists are already using tools, like Claude Code & Cursor, to 2x their productivity. And also many companies are evaluating you on your use of AI during interviews. #datascience #datascientist