45
COLearn how to build and validate Multiple Linear Regression models in R using real diagnostic tools and prediction techniques.
Hashtags:
#R #Statistics #LinearRegression #DataScience #Analytics
@codevisium
#Data Analysis Visualization Modern
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123
KEEach category has subtypes, and understanding these distinctions is critical for choosing the right statistical methods and visualizations.
#DataScience #DataAnalytics #dataanalytics
@kelvin_the_analyst
118
TEUnderstanding data becomes easier when you see the big picture 📊✨
This visual breakdown of Types of Data clearly explains the difference between Qualitative and Quantitative data, along with their subcategories and graphical representations. Perfect for statistics, research methods, and exam revision!
#Statistics #TypesOfData #QualitativeData #QuantitativeData #FrequencyDistribution
@techidealogy
146
WIVisualizing your data analytics journey! 🚀✨
If you’ve been wanting to learn SQL but are feeling overwhelmed, this blueprint is for you. This isometric diagram makes complex relational database concepts feel intuitive.
It’s all about creating structure and commanding the flow:
👉 It all starts with understanding your data’s home: structured tables, rows, and columns.
👉 The "anchor" icon is for Primary Keys – those vital, unique identifiers for every single record.
👉 Then, the pipeline logic: selecting data Select, filtering it like a funnel with the WHERE clause, and sorting it with ORDER BY for final visualization.
It’s the first big step on your path from raw data to actionable insight. Save this post for the next time you need a refresher on the fundamentals! 📌
What's the one thing you’re hoping to achieve by learning SQL? Let me know below! 👇
#SQL #DataAnalytics #DataVisualization #CareerGoals #TechCommunity #LearnData #DataScienceTips #DatabaseArchitecture
@wipdata_my
249
MIWhile chapter 2 covers vector data, chapter 7 is all about raster data:
The book: https://janosovm.gumroad.com/l/nzgud
Sample: https://janosovm.gumroad.com/l/rbkcu
Reviews: https://www.goodreads.com/book/show/219977270-geospatial-data-science-essentials
Overview: https://youtu.be/1IY_CGjd-js
@milanjanosov_science
60
COClean data means better models. Learn missing value handling, encoding, scaling, outlier treatment in R with code examples — skills every data scientist uses daily.
#RProgramming #DataScience #Analytics #CodingTips #InterviewPrep
@codevisium
285
TH💻📍 Where Code Meets Cartography
Using R for GIS & Remote Sensing to map and analyze spatial patterns across East Africa.
From data wrangling to spatial visualization, R transforms raw coordinates into meaningful geographic insights.
With packages like sf, terra, ggplot2, and dplyr, we can:
✔️ Process satellite & climate datasets
✔️ Perform spatial analysis & statistics
✔️ Visualize study distributions
✔️ Reproject data (WGS84 – EPSG:4326)
✔️ Integrate research with reproducible workflows
In modern geospatial science, coding is not optional — it’s essential.
GIS + R = Scalable, reproducible, data-driven research. 🌍
#GIS #RemoteSensing #GISRS #RStats #RStudio SpatialAnalysis Geospatial Cartography EarthObservation ClimateData DataScience ReproducibleResearch sfpackage ggplot2 TerraPackage EastAfrica Tanzania ArdhiUniversity GeoTech TheGISPreacher 💻🌍
@thegispreacher
9
NE🧩 Think all ERDs are the same? That’s the mistake most beginners make.
The truth is, every ERD has three versions — and each one tells a different story.
📍 In this video, we reveal the zoom levels of data modeling:
• Conceptual — see the big picture
• Logical — structure your ideas
• Physical — build the real thing
You’ll finally understand how to separate the what from the how, and why skipping these layers causes misaligned databases, broken joins, and poor performance.
🎯 Master the difference between models before you ever define a single column.
⚡ Subscribe, follow, and check out all my links here:
👉 https://www.hopp.bio/nexionanalytics
Helpful Links:
https://www.visual-paradigm.com/guide/data-modeling/what-is-entity-relationship-diagram/
https://www.lucidchart.com/blog/er-diagram-symbols-and-notation
#NexionAnalytics
🎵 Music: “Butterflys” by Panda Beats
Provided by Pixabay — https://pixabay.com/music/
Artist: https://pixabay.com/users/panda-beats-39035500/
@nexionanalytics
223
MAWhy should you care about PostGIS? 🐘 #geospatial #gis #datascience #postgis #sql
@matt_forrest
226
COLearn how to analyze time series in R — from decomposition, stationarity checks, ARIMA modeling, to forecasting future values.
These are skills every data scientist needs for real-world forecasting.
#TimeSeries #RProgramming #DataScience #Analytics #Forecasting
@codevisium
167
ITPostgreSQL Database Data Design — Short & Meaningful (Structured)
1️⃣ What is Data Design?
Data design is the process of organizing data in a PostgreSQL database so it is efficient, consistent, and easy to query.
2️⃣ Define Requirements
Understand what data the application needs
Identify entities (e.g., Users, Orders, Products)
Determine relationships between them
3️⃣ Create Tables (Schema Design)
Each entity becomes a table
Define appropriate columns and data types
Use clear and consistent naming conventions
4️⃣ Set Primary Keys
Every table should have a primary key
Ensures each record is unique
Commonly uses id
5️⃣ Establish Relationships (Foreign Keys)
Link related tables using foreign keys
Maintain referential integrity
Examples: one-to-many, many-to-many
6️⃣ Normalize the Database
Remove duplicate data
Organize into logical tables
Follow normalization rules (1NF, 2NF, 3NF)
7️⃣ Add Indexes for Performance
Create indexes on frequently searched columns
Improves query speed
Avoid over-indexing
8️⃣ Apply Constraints
Use NOT NULL, UNIQUE, CHECK, DEFAULT
Protect data integrity
Prevent invalid data entry
9️⃣ Plan for Scalability
Design for future growth
Consider partitioning if data becomes large
Optimize queries early
Good PostgreSQL data design ensures data is well-structured, consistent, fast to query, and scalable for future needs.
@itz_arko.420
106
SMWhat is Data Visualization?
In this video, learn how raw data turns into charts, graphs, and visual insights you can understand instantly.
Discover how bar charts, line charts, heatmaps, and maps help reveal trends, comparisons, and patterns fast.
Follow for tech concepts explained simply.
#smartlearnxpress #datavisualization #datascience #dataanalytics #sql #dataanalysis #datasecurity #datadriven #insights #tech #learndataanalysis
@smart_learn_xpress
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