#Data Manipulation

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

Comment “project” for my full video that breaks each of these projects down in detail with examples from my own work. If you’re using the Titanic, Iris, or COVID-19 dataset for data analytics projects, STOP NOW! These are so boring and over used and scream “newbie”. You can find way more interesting datasets for FREE on public data sites and you can even make your own using ChatGPT or Claude! Here are the 3 types of projects you need: ↳Exploratory Data Analysis (EDA): Exploring a dataset to uncover insights through descriptive statistics (averages, ranges, distributions) and data visualization, including analyzing relationships between variables ↳Full Stack Data Analytics Project: An end-to-end project that covers the entire data pipeline: wrangling data from a database, cleaning and transforming it. It demonstrates proficiency across multiple tools, not just one. ↳Funnel Analysis: Tracking users or items move from point A to point B, and how many make it through each step in between. This demonstrates a deeper level of business thinking by analyzing the process from beginning to end and providing actionable recommendations to improve it Save this video for later + send to a data friend!

I won’t be mad if you copy this entire roadmap… #dataanalyst #dataanalysis #dataanalytics #data #analyst #techjobs #breakintotech #wfh #workfromhome #wfhjobs #remotejobs #remotework #excel #sql #tableau #python

The best data scientists I know understand the maths behind the ML algo. Comment “data” ansi will see you the links to the 4 maths resources. #data #students #ai

Example projects for data manipulation 😎 Data manipulation is the backbone of EVERY data related project. In order for you to do any fancy analysis or machine learning, your data needs to be cleaned and formatted. Without good data manipulation skills, you won’t even get to do the fun stuff! Follow for more free coding resources ✅ #code #coding #tech #learntocode #datascience

Real world data > Kaggle 👏🏻 time to get dirty with data #dataset #machinelearning

🚫 p-hacking 🚫 #statistics #science #estadística #investigation #math

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 📊

Python + Pandas = Data Analysis → YouTube: Corey Schafer, Data School, Krish Naik Python + scikit-learn = Machine Learning → YouTube: StatQuest, CodeBasics, freeCodeCamp Python + PyTorch = Deep Learning → YouTube: DeepLizard, Aladdin Persson, PyTorch official Python + TensorFlow = Deep Learning → YouTube: TensorFlow, The AI Epiphany, freeCodeCamp Python + FastAPI = Web Development (APIs) → YouTube: Tech With Tim, FastAPI Docs, freeCodeCamp Python + Django = Full-Stack Web Development → YouTube: Dennis Ivy, Traversy Media, Code With Stein Python + Flask = Lightweight Web Dev → YouTube: Tech With Tim, Corey Schafer, freeCodeCamp Python + NumPy + Matplotlib = Scientific Computing → YouTube: Data School, StatQuest, freeCodeCamp Python + Selenium = Web Automation → YouTube: Naveen AutomationLabs, Mukesh otwani Python + BeautifulSoup / Scrapy = Web Scraping → YouTube: CodeBasics, freeCodeCamp, John Watson Rooney Python + OpenCV = Computer Vision → YouTube: Murtaza's Workshop, Pysource, freeCodeCamp Python + NLTK / spaCy = NLP → YouTube: freeCodeCamp, CodeBasics, DataWithHarsh Python + Streamlit / Gradio = ML App Deployment → YouTube: Chanin Nantasenamat (Data Professor), freeCodeCamp Python + Airflow = Data Pipelines → YouTube: Data With Danny, freeCodeCamp, Astronomer Python + PySpark = Big Data → YouTube: Databricks, freeCodeCamp, Simplilearn Python + Kivy / PyQt = Desktop Apps → YouTube: Tech With Tim (Kivy), Parwiz Forogh (PyQt5) Python + boto3 = AWS Automation → YouTube: AWS Tutorials, Be A Better Dev Python + LangChain = AI Agents → YouTube: Prompt Engineering, Data Professor, LangChain Official 👉 Follow @freshgrad.com.officia and @datasciencebrain l for more updates on career-boosting courses! 🚀✨ . . . . . . . #datascience #machinelearning #python #ai #dataanalytics #artificialintelligence #deeplearning #bigdata #agenticai #aiagents #statistics #dataanalysis #datavisualization #analytics #datascientist #neuralnetworks #100daysofcode #genai #llms #datasciencebootcamp #AIagents #NoCode #LangChain #CrewAI #AutoGen #AItools #AgenticAI #TechStack2025 #DataScience #AIworkflow

You probably Googled “How to learn Data Analytics”… And got 100 tabs open. Courses, tools, bootcamps, blogs, YouTube videos, each saying “Start here.” But no one told you what not to do. No one gave you a starter kit that actually made sense. So I built. And now I use this same plan to guide beginners I mentor. Here’s the Data Analytics Starter Kit I wish everyone should have👇 1. Start with Excel → It’s not outdated, it’s underrated. → Master formulas, Pivot Tables, and charts. 2. Then SQL → Learn how to query real data. → SELECT, WHERE, GROUP BY, and JOIN. That’s 80% of your job. 3. Add one viz tool → Pick Tableau or Power BI. → Focus on storytelling, not fancy dashboards. 4. Forget 100-hour courses Instead, build 3 small projects: ⤷ A Sales Dashboard in Excel ⤷ A Customer Retention Report in SQL ⤷ A Visual Story in Tableau 5. Use GitHub + LinkedIn → Document your projects. → Share your process. → Visibility builds credibility. 6. Give it 6–8 weeks Learn 1 skill → Apply it → Move to the next. If you're just starting out, don't chase 10 tools. Build your foundation first. Want my full Data Analytics Starter Kit with a roadmap, tool list, and project ideas? Drop "Community" to join my community here. #datavisualization #dataanalyst #datascience #data #sql #excel #python #career #careerswitch #trending #learning #interviewtips #india #metricminds

Here are 3 unique data science projects you can build in a weekend (2026 World Cup) easy. a World Cup match outcome predictor. predict win, loss, or draw using historical FIFA data. tech stack: Python, Pandas, Scikit-learn, and Streamlit to deploy it. medium. a player performance dashboard. pull player stats from Transfermarkt, visualize everything, and cluster players by playing style. tech stack: Python, Pandas, Plotly, and Seaborn for visualization with KMeans for clustering. hard. a real time World Cup sentiment tracker. pull live tweets during matches, run sentiment analysis as goals happen, and visualize how public opinion shifts in real time. tech stack: Python, Tweepy for the Twitter API, HuggingFace Transformers for sentiment analysis, and Plotly Dash for the live dashboard. comment “World cup” for resources to help you out along the way. #machinelearning #datascience #ai #python #cs

The Cambridge Analytica data scandal revealed how data from millions of users was used to build psychological profiles and target messaging. Investigations into coordinated online activity (including reports tied to the Internet Research Agency) showed how digital communities can be shaped to influence conversations and group behaviour. Research from institutions like MIT has explored how information environments can amplify emotionally charged or misleading content during sensitive periods. Your feed isn’t random. Share it with someone who wants to think more critically about what they consume.