555.0K
CHThe best projects serve a real use case
Comment “data” for all the links and project descriptions
#tech #data #datascience #ml #explore
@chrisoh.zip
#Data Lab
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ONYou cannot become a data analyst if you can’t do these things (shared the tools I use in the end)🔥🔥
Follow @onestopdata for data related content!
✅The most imp thing data analysts do is to understand the business requirements.
(1) Gathering Data
This means collecting data from different sources. Many a times this is done in collaboration with data engineers and architects hence usually the data analyst doesn’t have to do a lot in this.
(2) Cleaning Data
Going through the data and trying to understand it, making corrections where needed such as removing outliers or data that should not be included in the analysis. This step can take a lot of time, but understanding the data is crucial before you start to process it.
(3) Processing data
The data processing part of the process is where I use my skills and tools to analyze the work and come up with solutions for the problem at hand.
(4) Creating reports for business leaders
As an analyst, a lot of my time goes into creating and maintaining reports/dashboards for stakeholders and business leaders. This means showing the metrics and KPIs in the best manner possible to help drive business decisions.
The best analysts are those that can use data to tell a story.
(5) Collaborating with people
This one is my favorite! As a data analyst, you work with many people across departments, both senior and junior. You’ll also likely collaborate closely with other people who work in data science like data architects and database developers.
Tools I use: Excel,PowerBI,SQL and Python(sometimes)
#dataanalytics #onestopdata #datacleaning #dataprocessing #dashboard #reports #sql #powerbi #excel #python
@onestopdata
14.2K
ASThis 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
@askdatadawn
1.7K
CU¡Aún puedes ser parte de Data Lab!
Rompe el ciclo en el programa de prácticas preprofesionales de Interseguro, en donde pasarás a la acción real con especialistas en data y nuestro aliado estratégico Google.
¿Por qué ser un crack en data con nosotros?
✔️ 6 meses de entrenamiento práctico
✔️ Workshop exclusivo con Google
✔️ Ejecución de proyectos reales de la compañía
¡El reloj corre! Postula hasta el 13 de marzo AQUÍ: https://forms.gle/sU66T8HA9KWAetA46
#Datalab #Interseguro #CarrerasSTEM #Prácticas2026 #CulturaInPulso
@culturainterseguro
243.4K
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
111.1K
CHHere is a full roadmap on how to get started with Data Science. Comment “DATA” for the full roadmap pdf.
#datascience #machinelearning #coding #ai #university
@chrispathway
1.8M
MAI 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
@marytheanalyst
36.1K
YOWhere are all our data scientists at! 👀👇🏻
#young4stem #datascience #job #reel #stem #computerscience
@young4stem
1.5M
VOComment "Link" to get the links!
You Will Never Struggle With Data Structures & Algorithms Again
🔗 Explore these free visualization tools:
1️⃣ visualgo.net
2️⃣ cs.usfca.edu
3️⃣ csvistool.com
Stop memorizing code blindly. See every algorithm in action — arrays, linked lists, stacks, queues, trees, graphs, sorting, searching, and more. These interactive platforms show step-by-step exactly how data flows and how operations work.
Whether you’re preparing for coding interviews, studying computer science, or just starting with DSA, this is the fastest way to master the fundamentals.
Save this, share it, and turn complex algorithms into simple visuals you’ll never forget.
@volkan.js
28.7K
SDA 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
@sdw.online
42.2K
WO📍How to prepare for Data Scientist role in 2026 🚀
CORE SKILLS YOU MUST MASTER: Programming You must be fluent in:
● Python
● NumPy
● Pandas
● Scikit-learn
Writing clean, readable, bug free code
Data transformations without IDE help
Expect:
● Data cleaning
● Feature extraction
● Aggregations
● Writing logic heavy code
SQL
Almost every Data Science role tests SQL. You should be comfortable with:
● Joins - inner, left, self
● Window functions
● Grouping & aggregations
● Subqueries
● Handling NULLs
Statistics & Probability:
● Probability distributions
● Hypothesis testing
● Confidence intervals
● A/B testing
● Correlation vs causation
● Sampling bias
Machine Learning Fundamentals. You must know:
● Supervised vs Unsupervised learning
● Regression & Classification
● Bias Variance tradeoff
● Overfitting / Underfitting
Evaluation metrics:
● Accuracy
● Precision / Recall
● F1-score
● ROC-AUC
● RMSE
FEATURE ENGINEERING & DATA UNDERSTANDING:
● This is where strong candidates stand out.
● Handling missing data
● Encoding categorical variables
● Feature scaling
● Outlier treatment
CORE SKILLS YOU MUST MASTER: Programming You must be fluent in:
● Python
● NumPy
● Pandas
● Scikit-learn
Writing clean, readable, bug free code
Data transformations without IDE help
Expect:
● Data cleaning
● Feature extraction
● Aggregations
● Writing logic heavy code
SQL
Almost every Data Science role tests SQL. You should be comfortable with:
● Joins - inner, left, self
● Window functions
● Grouping & aggregations
● Subqueries
● Handling NULLs
Statistics & Probability:
● Probability distributions
● Hypothesis testing
● Confidence intervals
● A/B testing
● Correlation vs causation
● Sampling bias
Machine Learning Fundamentals. You must know:
● Supervised vs Unsupervised learning
● Regression & Classification
● Bias Variance tradeoff
● Overfitting / Underfitting
Evaluation metrics:
● Accuracy
● Precision / Recall
● F1-score
● ROC-AUC
● RMSE
+++ for more look at the comment
#datascientist #aiengineer #softwareengineer #datascience #dataengineer
@woman.engineer
26.1K
ADYour home lab is the ultimate networking playground! 🛠️ Whether it’s VLANs, OSPF, or automation, building a lab helps you gain real-world skills and stand out in interviews. Ready to level up? Drop your lab setup ideas below! 👇 #networking #it #informationtechnology #technology #network #homelab
@adjacentnode
✨ #Data Lab Discovery Guide
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Discover the latest #Data Lab content without logging in. The most impressive reels under this tag, especially from @marytheanalyst, @volkan.js and @chrisoh.zip, are gaining massive attention. View them in HD quality and download to your device.
What's trending in #Data Lab? The most watched Reels videos and viral content are featured above. Explore the gallery to discover creative storytelling, popular moments, and content that's capturing millions of views worldwide.
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💡 Top performing posts average 1.0M views (2.7x above average). Moderate competition - consistent posting builds momentum.
Post consistently 3-5 times/week at times when your audience is most active
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💡 Top performing content gets over 10K views - focus on engaging first 3 seconds
✍️ Detailed captions with story work well - average caption length is 809 characters
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