#Data Scientist Analyzing Data

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#Data Scientist Analyzing Data Reel by @sundaskhalidd (verified account) - Comment 'Projects' to get 5 Data Scientist Project ideas and a plan 👩🏻‍💻 ♻️ repost to share with friends. Here is how to become a data scientist i
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@sundaskhalidd
Comment ‘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
#Data Scientist Analyzing Data Reel by @askdatadawn (verified account) - This 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. An
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@askdatadawn
This 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
#Data Scientist Analyzing Data Reel by @penelope_data - Life of a data scientist. Comment "Github" and I'll send you 10 real word datasets with data project ideas #data #students
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@penelope_data
Life of a data scientist. Comment “Github” and I’ll send you 10 real word datasets with data project ideas #data #students
#Data Scientist Analyzing Data Reel by @chrisoh.zip - The best projects serve a real use case Comment "data" for all the links and project descriptions #tech #data #datascience #ml #explore
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@chrisoh.zip
The best projects serve a real use case Comment “data” for all the links and project descriptions #tech #data #datascience #ml #explore
#Data Scientist Analyzing Data Reel by @emrcodes (verified account) - 3 data science projects you can do in a weekend. If you're learning data science, one of the best ways to improve is by working through real examples
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@emrcodes
3 data science projects you can do in a weekend. If you’re learning data science, one of the best ways to improve is by working through real examples. Here are three Kaggle notebooks you can explore: • Loan Prediction – predicting whether a loan gets approved based on applicant data. • Bank Churn – analyzing which customers are likely to leave a bank. • House Price Prediction – estimating house prices from property features. You can study the notebooks and also try solving the same problems yourself using the same datasets. It’s a great way to practice and see different ways people approach the same problem. Comment “DATA” and I’ll send you the notebooks. #coding #datascience #university
#Data Scientist Analyzing Data Reel by @sdw.online (verified account) - 🟠Comment '30' and I'll send you data project tutorials on YouTube I made for you🟠
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@sdw.online
🟠Comment '30' and I'll send you data project tutorials on YouTube I made for you🟠
#Data Scientist Analyzing Data Reel by @tech_jroshan - 🎯 Hypothesis Testing in Data Science - A Must-Know Skill! 📊 Whether you're analyzing experiments, comparing A/B test results, or validating assumpt
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@tech_jroshan
🎯 Hypothesis Testing in Data Science – A Must-Know Skill! 📊 Whether you're analyzing experiments, comparing A/B test results, or validating assumptions in machine learning — hypothesis testing is foundational to sound decision-making. Here's a quick breakdown of the most common types of hypothesis tests every analyst and data scientist should know: 🔍 Common Types of Hypothesis Testing 1️⃣ Z-Test ✔️ Used when population variance is known and sample size is large (n greator than 30) 📌 Example: Testing average delivery times of a logistics company vs. claimed value. 2️⃣ T-Test ✔️ Used when population variance is unknown or sample size is small One-sample t-test: Compare sample mean to population mean Two-sample t-test: Compare means of two independent samples Paired t-test: Compare means from the same group at two different times 3️⃣ Chi-Square Test ✔️ Used for categorical data Goodness of Fit: Is observed distribution different from expected? Test of Independence: Are two categorical variables related? 4️⃣ ANOVA (Analysis of Variance) ✔️ Compares means across 3 or more groups 📌 Example: Comparing test scores across multiple classrooms or products. 💡 When to Use What? Parametric tests: Use when data is normally distributed Non-parametric tests: Use when data is skewed or ordinal 📈 Key Concepts to Remember: Null hypothesis (H₀): The status quo or no effect Alternative hypothesis (H₁): What you’re testing for p-value: Probability of observing your results if H₀ is true Significance level (α): Typically 0.05 #HypothesisTesting #DataScience #Statistics #ABTesting #Analytics #Python #MachineLearning #BusinessIntelligence #DataDriven #problemsolvingskills #CareerGrowth #Analytics #pvalue #tTest #ChiSquare #ZTest #ANOVA
#Data Scientist Analyzing Data Reel by @jessramosdata (verified account) - 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, Ir
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@jessramosdata
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! Save this video for later + send to a data friend! #data #dataanalytics #project 🏷️ data analytics, data analytics project, data project, dataset
#Data Scientist Analyzing Data Reel by @digitalsamaritan (verified account) - 3 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 ana
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@digitalsamaritan
3 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
#Data Scientist Analyzing Data Reel by @chrispathway (verified account) - Here is a full roadmap on how to get started with Data Science. Comment "DATA" for the full roadmap pdf. #datascience #machinelearning #coding #ai #u
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@chrispathway
Here is a full roadmap on how to get started with Data Science. Comment “DATA” for the full roadmap pdf. #datascience #machinelearning #coding #ai #university
#Data Scientist Analyzing Data Reel by @codewithboi - I hear this a lot… and honestly, it always makes me smile a little. But why do we have to compare or compete? Why should we compete about who suffer
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@codewithboi
I hear this a lot… and honestly, it always makes me smile a little. But why do we have to compare or compete? Why should we compete about who suffers more in tech.? Here is what Data science is: • cleaning datasets that look perfectly fine… until you open them • building data pipelines that have to run reliably at 2 AM • searching for patterns and asking uncomfortable questions hidden inside the data • translating messy real-world problems into something machines can learn from • designing end products that actually scale up systems or policies, help people make decisions One day you’re deep in data cleaning. Next day you’re tuning a model. Next thing you’re building a full UI for stakeholders who “just want a simple chart.” Versatility is the job. So no, it’s not about being harder or easier. It’s about being multidisciplinary, analytical, and dangerously adaptable. And the people in this field know… the real work starts where the clean tutorial datasets end #datascience #programming #tech #ai #study Tags (Coding, programming, python, machine learning, AI developer, study, data-scientist, data-science, student, data, design, software, information technology, AI projects, learning, growth, motivation, inspiration )
#Data Scientist Analyzing Data Reel by @onestopdata - Although 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 r
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@onestopdata
Although 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

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#Data Scientist Analyzing Data is one of the most engaging trends on Instagram right now. With over thousands of posts in this category, creators like @onestopdata, @chrisoh.zip and @sdw.online are leading the way with their viral content. Browse these popular videos anonymously on Pictame.

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