52.5K
NAWhat data do you actually need to make the best decision possible?
@nataliedawson
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#Data Driven Decisions
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DII am offering a complimentary Power BI data analytics tutorial 📊📈. Discover new skills 💡 swiftly with this 30-second guide ⏱️. Elevate your data proficiency 🚀. Join now! 💻📚🧠✨
#viral #education
@discovertalent143
1.7M
TR“Data-Driven” in name only. First, pick the decision. Then, pick the data. #corporate #data #digitalleadership #datadrivendecisions #dataanalytics
@transformationprincess
245.3K
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
349.7K
SUComment ‘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
@sundaskhalidd
307
VIHi..
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Your 2026 Data analytics roadmap starts here -- from zero to job-ready with @vinia_tech 🧿
@vinia_tech
125
SUWhat if yesterday's data suddenly changes? 🤯
If you've ever struggled to reproduce a machine learning model or debug a weird dashboard metric because the source data was mutated or deleted, you need Data Versioning. 🛠️
Just like Git revolutionized software development by tracking code changes, tools like DVC, lakeFS, and table formats like Apache Iceberg and Delta Lake are doing the same for Data Engineering.
Imagine being able to branch your data lake, run an experiment, and merge it safely. Or querying data exactly as it looked last Tuesday at noon (Time Travel ⏳).
Swipe through to understand the strategies and concepts behind versioning your data pipelines! 👉
Do you use time travel queries in your pipelines? Let me know in the comments!
Follow @subhadip.ca for more tips on Data Engineering, Cloud, and Architectures. 🚀
#dataengineering #datapipelines #dataversioning #machinelearning #deltalake #apacheiceberg #datascience #techtips #bigdata #dataarchitecture #apachehudi #mlops
@subhadip.ca
119.2K
VEIf you want to crack Data Science jobs in the next 30 days, here’s the three step process which you will follow which literally no one talks about.
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#datascience #data #interview
@vee_daily19
6.7M
ONAlthough each day in the life of a data analyst is different, here are 5 key responsibilities that a data analyst has:
Follow @onestopdata for data related content! Check the link in bio for details on my webinars and courses!
(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
#data #dataanalytics #datacareer #datajobs #datascience #onestopdata #datavisualizatio#reels #reelitfeelit #trending #explore #careerindata #reelkarofeelkaro #datacleaning #dataprocessing #datagathering #dashboard #reports #collaboration #sql #powerbi #excel #python
@onestopdata
23.7K
ASStatistics is overwhelming. There are a million concepts to learn.
But for your first Data Science job, you don’t need to know EVERYTHING.
I recommend focusing on this first list, AND pick 1-2 topics in the second list if you want to stand out.
✅ Statistics concepts you MUST KNOW to land your first Data Science job
1. Non-parametric tests – Kruskal-Wallis, Mann-Whitney
2. Bayesian hierarchical modeling
3. Time-series forecasting models (ARIMA, Holt-Winters)
4. Survival analysis, like Kaplan-Meier, Cox models
5. Advanced regression – LASSO, Ridge, ElasticNet
6. Mixed effects models
7. Deep learning – neural networks, CNNs, RNNs
8. Computer vision – image classification, object detection, segmentation
9. Natural language processing – embeddings, transformers, LLMs
10. Reinforcement learning – Q-learning, policy gradients
11. Advanced ML pipelines – feature stores, model registries, deployment
❌ Don’t worry about these concepts for now
1. Non-parametric tests – Kruskal-Wallis, Mann-Whitney
2. Bayesian hierarchical modeling
3. Time-series forecasting models (ARIMA, Holt-Winters)
4. Survival analysis, like Kaplan-Meier, Cox models
5. Advanced regression – LASSO, Ridge, ElasticNet
6. Mixed effects models
7. Deep learning – neural networks, CNNs, RNNs
8. Computer vision – image classification, object detection, segmentation
9. Natural language processing – embeddings, transformers, LLMs
10. Reinforcement learning – Q-learning, policy gradients
11. Advanced ML pipelines – feature stores, model registries, deployment
#datascience #datascientist #machinelearning #aiengineering #statistics
@askdatadawn
13.8K
PHwatch this if you want to become a data analyst in 2026, these are my top simple tips 📊
1. Learn SQL: its the tool you’ll use to get data from databases, and then use to analyse business performance
2. Learn Excel or something similar: it’s great for ad hoc analysis and building engaging charts and diagrams
3. Get familiar with a reporting tool, you don’t need to be great at this just an understanding is fine
4. The core skills are communicating your insights clearly and understanding business metrics
Save this and come back to it when you’re planning what to learn, I have links on my profile for courses/guides for each of these aspects!
@phoebeslifeindata
649.1K
FIEp44- Stop learning everything!!
Are you learning everything in data analytics??
that’sthe biggest mistake and the reason people stay stuck with out getting a job.
Interviews don’t test random topics. They test specific skills. Right tools and project scenario based knowledge.
As an experienced data analyst with over 8 years of experience i have created a detailed pdf from my data analyst journey on which topics needs to be covered. Which needs to be ignored. How to prepare your own project based portfolio. Answer questions with right tools and skill. Below are the details included in pdf.
✔️ What to learn (and what to skip)
✔️ Skills interviewers actually ask
✔️ Role-wise roadmap (Fresher → Job ready)
✔️ Project clarity + interview direction
This is only for serious learners. Hence i made it as a paid one which costs a minimal fee.
Follow and comment EP-44. I’ll send you the link directly.
[data analytics, journey, road map, data analyst, jobs]
#dataanalyst #journey #roadmap #skills #growth
@fitwit_krish
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