Top Tweets for #30daysofdata
Day 10 of #30DaysOfData! ๐ Automating my database:๐น Procedures: Saved code to easily reuse.
๐น Triggers: Auto-copied new workers to a 2nd list.
๐น Events: Timer to auto-delete old records. Working smarter! ๐ปโจ #SQL #DataAnalytics
๐ Day 2 of #30DaysOfData! ๐
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! ๐ก
#DataAnalytics #Excel #LearningInPublic #Data
Week 6 = break week for my Data Analysis class
But since Iโm playing catch, we move to Week 7 tomorrow.
No rest for the feed. New dashboards + insights loading ๐
#DataAnalytics #LearningInPublic #30DaysOfData #LearningwithTS
Good morning! Data Community!
My todayโs goal: learn one new thing and let it compound.
Whatโs yours?
#dataanalyst #30DaysOfData #LearningWithTS #Excel #DataAnalytics #WomenInTech
Day 7/30 ๐
One thing Iโm 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!!๐ฅณ
We just wrapped our #30DaysOfData Challenge 2026 and our scholars absolutely delivered! ๐ฅ
Congratulations to our TOP 3 ๐
๐ฅ Samuel Nzebor โ 261,320 XP
๐ฅ Ibukun Egwuogu โ 206,236 XP
๐ฅ Goodness Okoro โ 195,596 XP
30 days. No excuses. Just growth. ๐ช๐ฝ
1/2
#30DaysofData with @DataFestAfrica || Day 14
In continuation of the dataset visualization exploration using Seaborn.
@DataCampDonates #DataCommunityAfrica #DCA #DCDonates #DataJourney #DataFestAfrica #DataScience
1/2
#30DaysofData with @DataFestAfrica || Day 13
In todayโs session, I explored data visualization using Seaborn. Although I didnโt cover as much as I did on previous days, ......
@DataCampDonates #DataCommunityAfrica #DCA #DCDonates #DataJourney #DataFestAfrica #DataScience
Things Iโve 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
๐งต Day 1 of #30DaysOfData #BuildInPublic Journey
Today marks the start of my data analysis consistency challenge! ๐
โ
Loaded my dataset โ Global Energy Data (OWID)
#DataScience #DataAnalysis #Python #JupyterNotebook #Pandas #Streamlit #Plotly #LearningInPublic #TechTwitter
Day 14 of #30DaysOfData
Raw data isnโt always model-ready.
๐น Transform: make it usable (log, encode, scale)
๐น Normalize: keep values on the same scale (MinโMax, Z-score)
These steps keep models fair and insights reliable.
#DataScience #MachineLearning #Analytics
Day 13 of #30DaysOfData
Outliers arenโt always errors sometimes theyโre the insights that stand out for a reason.
๐ฏ Detect: Z-score, IQR, Boxplot, Isolation Forest
โ๏ธ Handle: Investigate, Transform, or Remove
Great data scientists donโt rush to delete. They ask why.
#DS
Day 13 of #30DaysOfData
Outliers arenโt always errors sometimes theyโre the insights that stand out for a reason.
๐ฏ Detect: Z-score, IQR, Boxplot, Isolation Forest
โ๏ธ Handle: Investigate, Transform, or Remove
Great data scientists donโt rush to delete. They ask why.
#DS
Day 12 of #30DaysOfData
Missing values are inevitable, how you handle them shapes your modelโs accuracy.
โ
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 modelโs accuracy.
โ
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 โ๏ธ
Feature Engineering is where raw data becomes intelligence.
Itโs about creating, transforming, and selecting the right variables to help models see patterns humans might miss.
Better features โ Smarter models.
#DataScience #MachineLearning
Day 10 of #30DaysOfData
Probability is the backbone of Data Science, it helps us handle uncertainty and make better predictions.
๐น Quantifies uncertainty
๐น Powers ML models
๐น 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.
๐น Mean โ Central value
๐น Median โ Middle point, resists outliers
๐น Variance โ Measures data spread
๐น Std. Deviation โ Shows how far values deviate
#DataScience
Day 9 of #30DaysOfData
Statistics is the backbone of data science - it helps us find meaning in uncertainty.
๐น Mean โ Central value
๐น Median โ Middle point, resists outliers
๐น Variance โ Measures data spread
๐น Std. Deviation โ Shows how far values deviate
#DataScience
Day 8 of #30DaysOfData
Master variable types to analyze smarter.
๐นQualitative Data โ non-numeric, descriptive (e.g., opinions, colors, categories)
๐นQuantitative Data โ numeric, measurable (e.g., age, income, scores)
Know your variables โ better insights & models.
#datascience
Day 8 of #30DaysOfData
Master variable types to analyze smarter.
๐นQualitative Data โ non-numeric, descriptive (e.g., opinions, colors, categories)
๐นQuantitative Data โ numeric, measurable (e.g., age, income, scores)
Know your variables โ better insights & models.
#datascience
Day 7 of #30DaysOfData
Not all data are the same. Knowing the type guides how you clean, analyze & model it.
๐น Structured: Tables (SQL, Excel)
๐น Unstructured: Text, images, audio
๐น 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.
๐น Structured: Tables (SQL, Excel)
๐น Unstructured: Text, images, audio
๐น 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:
๐น Spot patterns
๐น Detect anomalies
๐น Test hypotheses
๐น 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:
๐น Spot patterns
๐น Detect anomalies
๐น Test hypotheses
๐น Guide feature selection
EDA turns data into understanding - without it, models risk being blind guesses.
#DataScience
Day 4 of #30DaysOfData
Great models canโt fix bad data.
Thatโs why Data Cleaning is critical in Data Science:
โ
Handle missing values
โ
Remove duplicates
โ
Correct errors
โ
Standardize formats
It may not be glamorous, but clean data is the foundation of trustworthy insights.
#DS
Day 4 of #30DaysOfData
Great models canโt fix bad data.
Thatโs why Data Cleaning is critical in Data Science:
โ
Handle missing values
โ
Remove duplicates
โ
Correct errors
โ
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:
1๏ธโฃ Define the Problem
2๏ธโฃ Collect Data
3๏ธโฃ Clean & Prepare
4๏ธโฃ Analyze
5๏ธโฃ Model
6๏ธโฃ Evaluate
7๏ธโฃ Deploy & Monitor
Miss a step, and the project risks failure.Master them, and data drives transformation.
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