#30daysofdata Etiketinde En Popüler Paylaşımlar
Day 10 of #30DaysOfData! 🚀 Automating my database:🔹 Procedures: Saved code to easily reuse.
🔹 Triggers: Auto-copied new workers to a 2nd list.
🔹 Events: Timer to auto-delete old records. Working smarter! 💻✨ #SQL #DataAnalytics
🚀 Day 2 of #30DaysOfData! 📊
Mastered Excel sorting & filtering today! Analyzed student performance and uncovered a strong insight: 100% of students with <75% attendance failed their course.
Data speaks volumes! 💡
#DataAnalytics #Excel #LearningInPublic #Data
Week 6 = break week for my Data Analysis class
But since I’m playing catch, we move to Week 7 tomorrow.
No rest for the feed. New dashboards + insights loading 📊
#DataAnalytics #LearningInPublic #30DaysOfData #LearningwithTS
Good morning! Data Community!
My today’s goal: learn one new thing and let it compound.
What’s yours?
#dataanalyst #30DaysOfData #LearningWithTS #Excel #DataAnalytics #WomenInTech
Day 7/30 📈
One thing I’m learning already: data is everywhere. Every click, purchase, and decision generates data. The real skill is knowing how to make sense of it.
Happy New Week
#30DaysOfTech #LearningWithTS #30DaysOfData
The results are in!!🥳
We just wrapped our #30DaysOfData Challenge 2026 and our scholars absolutely delivered! 🔥
Congratulations to our TOP 3 🏆
🥇 Samuel Nzebor — 261,320 XP
🥈 Ibukun Egwuogu — 206,236 XP
🥉 Goodness Okoro — 195,596 XP
30 days. No excuses. Just growth. 💪🏽
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#30DaysofData with @DataFestAfrica || Day 14
In continuation of the dataset visualization exploration using Seaborn.
@DataCampDonates #DataCommunityAfrica #DCA #DCDonates #DataJourney #DataFestAfrica #DataScience
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#30DaysofData with @DataFestAfrica || Day 13
In today’s session, I explored data visualization using Seaborn. Although I didn’t cover as much as I did on previous days, ......
@DataCampDonates #DataCommunityAfrica #DCA #DCDonates #DataJourney #DataFestAfrica #DataScience
Things I’ve learned on Excel this week:
Working with data
Creating formulas
Working with tables
Managing and filtering data
Working with structured references which are easier to filter and understand.
#DataAnalysis #30daysofdata
🧵 Day 1 of #30DaysOfData #BuildInPublic Journey
Today marks the start of my data analysis consistency challenge! 🚀
✅ Loaded my dataset — Global Energy Data (OWID)
#DataScience #DataAnalysis #Python #JupyterNotebook #Pandas #Streamlit #Plotly #LearningInPublic #TechTwitter
Day 14 of #30DaysOfData
Raw data isn’t always model-ready.
🔹 Transform: make it usable (log, encode, scale)
🔹 Normalize: keep values on the same scale (Min–Max, Z-score)
These steps keep models fair and insights reliable.
#DataScience #MachineLearning #Analytics
Day 13 of #30DaysOfData
Outliers aren’t always errors sometimes they’re the insights that stand out for a reason.
🎯 Detect: Z-score, IQR, Boxplot, Isolation Forest
⚙️ Handle: Investigate, Transform, or Remove
Great data scientists don’t rush to delete. They ask why.
#DS
Day 13 of #30DaysOfData
Outliers aren’t always errors sometimes they’re the insights that stand out for a reason.
🎯 Detect: Z-score, IQR, Boxplot, Isolation Forest
⚙️ Handle: Investigate, Transform, or Remove
Great data scientists don’t rush to delete. They ask why.
#DS
Day 12 of #30DaysOfData
Missing values are inevitable, how you handle them shapes your model’s accuracy.
✅ Techniques:
Deletion (if few)
Imputation (Mean, Median, Mode, KNN)
Advanced: Multiple or Model-based methods
Handle them smartly. Data integrity drives good science.
#DS
Day 12 of #30DaysOfData
Missing values are inevitable, how you handle them shapes your model’s accuracy.
✅ Techniques:
Deletion (if few)
Imputation (Mean, Median, Mode, KNN)
Advanced: Multiple or Model-based methods
Handle them smartly. Data integrity drives good science.
#DS
Day 11 of #30DaysOfData ⚙️
Feature Engineering is where raw data becomes intelligence.
It’s about creating, transforming, and selecting the right variables to help models see patterns humans might miss.
Better features → Smarter models.
#DataScience #MachineLearning
Day 10 of #30DaysOfData
Probability is the backbone of Data Science, it helps us handle uncertainty and make better predictions.
🔹 Quantifies uncertainty
🔹 Powers ML models
🔹 Guides smarter decisions
Nothing in data is ever certain, probability helps us make sense of it.
#Ds
Day 9 of #30DaysOfData
Statistics is the backbone of data science - it helps us find meaning in uncertainty.
🔹 Mean – Central value
🔹 Median – Middle point, resists outliers
🔹 Variance – Measures data spread
🔹 Std. Deviation – Shows how far values deviate
#DataScience
Day 9 of #30DaysOfData
Statistics is the backbone of data science - it helps us find meaning in uncertainty.
🔹 Mean – Central value
🔹 Median – Middle point, resists outliers
🔹 Variance – Measures data spread
🔹 Std. Deviation – Shows how far values deviate
#DataScience
Day 8 of #30DaysOfData
Master variable types to analyze smarter.
🔹Qualitative Data – non-numeric, descriptive (e.g., opinions, colors, categories)
🔹Quantitative Data – numeric, measurable (e.g., age, income, scores)
Know your variables → better insights & models.
#datascience
Day 8 of #30DaysOfData
Master variable types to analyze smarter.
🔹Qualitative Data – non-numeric, descriptive (e.g., opinions, colors, categories)
🔹Quantitative Data – numeric, measurable (e.g., age, income, scores)
Know your variables → better insights & models.
#datascience
Day 7 of #30DaysOfData
Not all data are the same. Knowing the type guides how you clean, analyze & model it.
🔹 Structured: Tables (SQL, Excel)
🔹 Unstructured: Text, images, audio
🔹 Semi-structured: JSON, XML
Know your data. Choose the right method.
#DataScience
Day 7 of #30DaysOfData
Not all data are the same. Knowing the type guides how you clean, analyze & model it.
🔹 Structured: Tables (SQL, Excel)
🔹 Unstructured: Text, images, audio
🔹 Semi-structured: JSON, XML
Know your data. Choose the right method.
#DataScience
Day 6 of #30DaysOfData
Data tells a story but without visualization, it stays hidden.
Visualization reveals patterns, outliers & insights, making data clear for everyone.
Tools: Matplotlib, Seaborn, Power BI, Tableau.
A single chart can speak louder than rows of numbers.
#DS
Day 6 of #30DaysOfData
Data tells a story but without visualization, it stays hidden.
Visualization reveals patterns, outliers & insights, making data clear for everyone.
Tools: Matplotlib, Seaborn, Power BI, Tableau.
A single chart can speak louder than rows of numbers.
#DS
Day 5 of #30DaysOfData
Exploratory Data Analysis (EDA) is how we make sense of data before modeling:
🔹 Spot patterns
🔹 Detect anomalies
🔹 Test hypotheses
🔹 Guide feature selection
EDA turns data into understanding - without it, models risk being blind guesses.
#DataScience
Day 5 of #30DaysOfData
Exploratory Data Analysis (EDA) is how we make sense of data before modeling:
🔹 Spot patterns
🔹 Detect anomalies
🔹 Test hypotheses
🔹 Guide feature selection
EDA turns data into understanding - without it, models risk being blind guesses.
#DataScience
Day 4 of #30DaysOfData
Great models can’t fix bad data.
That’s why Data Cleaning is critical in Data Science:
✅Handle missing values
✅Remove duplicates
✅Correct errors
✅Standardize formats
It may not be glamorous, but clean data is the foundation of trustworthy insights.
#DS
Day 4 of #30DaysOfData
Great models can’t fix bad data.
That’s why Data Cleaning is critical in Data Science:
✅Handle missing values
✅Remove duplicates
✅Correct errors
✅Standardize formats
It may not be glamorous, but clean data is the foundation of trustworthy insights.
#DS
Day 3 of #30DaysOfData
The Data Science Lifecycle turns raw data into real impact:
1️⃣ Define the Problem
2️⃣ Collect Data
3️⃣ Clean & Prepare
4️⃣ Analyze
5️⃣ Model
6️⃣ Evaluate
7️⃣ Deploy & Monitor
Miss a step, and the project risks failure.Master them, and data drives transformation.
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