2.4M
MORelaxing Python & Pygame Creations #coding #programming
@mohcinale
#Data Analysis Tools Like Python
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JEComment “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!
@jessramosdata
6.4K
DI3 AI tools you need if you hate doing data analysis work!
Of course, this is AI so please exercise critical thinking with AI generated reports or analysis
#dataanalysis #aitools
@digitalsamaritan
95.7K
THYou only need to learn these 10 Python Topics to crack any data analyst interview #python #sql #dataanalyst
@thedataguy16
12.3K
VOComment "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 📊
@volkan.js
118.6K
ASFREE Data Analytics learning resources.
Seriously, start here before paying for any courses.
These are FREE & a great introduction for any skill you want to learn.
- SQL: https://www.youtube.com/watch?v=7S_tz1z_5bA
- Excel: https://www.youtube.com/watch?v=pCJ15nGFgVg
- Tableau: https://www.youtube.com/watch?v=aHaOIvR00So
- Python: https://www.youtube.com/watch?v=LHBE6Q9XlzI
#dataanalytics #dataanalyst #datascience #womenintech #aiengineering #techcareers
@askdatadawn
35.9K
SOGetting started in quantitative analysis 😎
The yfinance package in Python allows you to grab historical financial data straight from yahoo finance. If you can search for a company on yahoo finance, you can get historical data from it!
Historical data is the foundation of machine learning/predictive models in the financial world. Patterns tend to repeat themselves, but picking out the significant patterns can be tricky!
Follow for more free coding resources ✅
#code #coding #learntocode #data #tech
@softwarewithnick
122.3K
MAComment PROJECT to access my step-by-step Python tutorial that anyone can follow to build your very first geospatial dashboard web app! 🌍📊
A good number of portfolio projects is 3–5, and the types of projects you choose should reflect the kind of data role you’re going after.
A data analyst portfolio should look very different from a machine learning engineer one. Even within data science, a product/decision data scientist portfolio should focus on A/B testing and metrics storytelling—while an algorithm data scientist portfolio might highlight modeling and experimentation.
✨ Especially if you’re building your very first project, prioritize:
🌱 Real-world messiness (not polished Kaggle sets)
🌱 Business context and decision-making
🌱 Clear documentation (what you did and why)
🌱Visuals to help your work stand out
No one’s asking for perfection—they want to see how you think.
#datascienceportfolio #dataanalyst #learnpython #codingjourney #techcareers
@maggieindata
82.1K
PR1. FastAPI – Build lightning-fast APIs with automatic docs and modern Python features.
2. Pydantic – Ensure clean, validated data using Python type hints.
3. Logging – Track your app’s behavior and debug issues with structured logs.
4. Testing – Catch bugs early with automated checks for your code and APIs.
5. Async Programming – Handle more with less waiting write non-blocking, efficient code.
6. Database Management – Store, query, and manage data reliably using ORMs and SQL tools.
#datascience #machinelearning #womeninstem #learningtogether #progresseveryday #tech #consistency
@priyal.py
7.3K
MA💻R for Data Analysis
R is an open-source language that provides a wide range of powerful tools and libraries for statistical analysis.
Libraries like tidyverse and ggplot will help you to clean, preprocess, and visualize your data
With libraries such as stats and lme4, you can perform a range of statistical analyses.
R also has a community that actively contributes to the development of new tools and libraries. This has led to a rich ecosystem of packages and tools for various data analysis tasks.
Follow @ai.marina.io if you want to know more insights for effective data analysis
#datascientist #datascience #dataanalytics #womenwhocode #womenintech #code #datasciencejobs #datasciencejobs #datasciencecareers #programming #r #python #startcareer #careerdataanalyst
@marina.petzel.tech
128.8K
LOThere's a one line Python command that replaces hours of manual EDA work 📊
Most analysts start every project typing df.info, df.describe, checking duplicates, plotting histograms one by one. It's boring, slow, and easy to miss things.
Here's the smarter way:
Install ydata-profiling. Run one line of code on your dataframe. It automatically builds a full interactive HTML dashboard. Distributions, correlations, missing values, duplicates, all in one place.
The difference between junior and senior analysts isn't just skill. It's knowing which tools save you hours so you can focus on actual insights.
Comment "CODE" for the full script and save this before your next project 🎯
#PythonForDataScience #ExploratoryDataAnalysis #PandasProfiling #DataAnalyticsTips
@loresowhat
139.8K
JU5 Python Libraries for Algo Trading!🐍
Are there any other libraries that you use? Let me know in the comments and follow for more!
#optionstrading #finance #options #stock
@julias.algos
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