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Day 2 ✌️of 7 Days of #SQLwithFunmi @olufunmilola olapeju olaewe @innotech IT Consultancy.
https://t.co/KKu32ZxzrN
#tantobatechworld #dataanalyticsjourney #Excel #DataAnalytics #learningdataanalytics
Data Analysis Project
HOSPITAL ADMISSION
Day 1 of 7 Days of #SQLwithFunmi @olufunmilola olapeju olaewe @innotech IT Consultancy.
tantobatechworld #SQLwithFunmi #7DaysOfSQL #LearningInPublic #DataAnalytics #SQLSkills #DataJourney
Hey Data Fam 👋
I just completed the THINK LIKE AN ANALYST course on Maven Analytics @MavenBI — another step in my data learning journey! 🚀
#InnotechITConsultancy #LearningInPublic #DataAnalytics #MavenAnalytics #SQLwithFunmi #DataJourney #ContinuousLearning
Hey Data Fam 👋
I just completed Thinking like an analyst on Maven Analytics — another step in my data learning journey! 🚀
#InnotechITConsultancy #LearningInPublic #DataAnalytics #MavenAnalytics #SQLwithFunmi #DataJourney #ContinuousLearning
Day 23 of 30 Days of #SQLwithFunmi
Questions
1 What is the *total number
of transactions* per channel?
2.What is the *failure rate*
of each payment channel?
3.Which channel has the
*highest average transaction value* (A) for completed
Day 22 of 30 Days of #SQLwithFunmi
Question
1 Find the *average number of transactions per active user*.
2 Identify the *top 5 most active users* by number of total transactions (sent + received )
3. How many users have acted as *both sender and receiver*?
Day 20 of 30 Days of #SQLwithFunmi
#DataAnalytics
Today we dive into another data set of innotransfer
1 What is the total amount
of money transferred on the platform?
2 Identify the top 5
senders by total amount sent
3 Determine the most
frequently used payment channel.
Day 20 of 30 Days of #SQLwithFunmi
1 Calculate the total
revenue generated* from all sales.
2 Identify the top 5
customers based on their total payment amount.
3. Show the total revenue
contributed by each payment mode
Day 19 of 30 Days of #SQLwithFunmi
Questions
1Find customers who have
made *5 or more purchases*.
2 List customers who have
only used one payment mode
in all their transactions.
3 Show customers whose
total spend is above N50,000 but have never used POS.
Day 18 of 30 Days of #SQLwithFunmi
1 Approach: selected from the full sales payment data then selected the product name and use count function to get total product sold then grouped by the product name then used the having , and functions to get the. Result
Day 18 of 30 Days of #SQLwithFunmi
1 List all products with a price above N3,000 that have been sold more than 50 times.
2. Show the top 5 most
frequently sold products along with their total quantity
3 Find products that have been sold by fewer than 2 different staff
Day 10 of #SQLwithFunmi! 🔷 Mastered logical reasoning with SQL. Retrieved corps members based on complex conditions like age range, preferred state vs. origin state, and missing contact details. Discovered the importance of using IS NULL for missing values. 💻 #30DaysOfSQL #SQLS
🔷Day 9 of #SQLwithFunmi: I harnessed LIKE, IN, and BETWEEN to filter data with ease. Retrieved corps members by email, state, age, and name patterns. LIKE with wildcards (%) and IN for multiple values were total lifesavers! Now, querying data feels like a breeze!
#30DaysOfSQL
Day 8 of #SQLwithFunmi! 🔷 Mastered DISTINCT, HAVING, and LIMIT. Retrieved unique states, institutions, and courses. Filtered grouped results and displayed top records. 💻 #30DaysOfSQL #SQLSkills #DataAnalytics
Catching up on Day 7 of #SQLwithFunmi!
🔷 Mastered aggregation with MIN, MAX, COUNT, GROUP BY, and ORDER BY. Found youngest/oldest corps members and counted members by institution state. Powerful techniques for data analysis! 💻
#30DaysOfSQL
#SQLSkills
#DataAnalytics
Day 6 of #SQLwithFunmi! 🔷
Today was a lot! despite the busy day and the festivities, I was able to get to code a little.
Still on the NYSC database. 💻
#30DaysOfSQL #LearningInPublic #DataAnalytics
Day 4 of #SQLwithFunmi! 🔷
Mastered COUNT, GROUP BY, and AVERAGE. Retrieved corps members' count by state, preferred state, course, and average age. Levelling up day after day😌💻
#30DaysOfSQL #LearningInPublic #DataAnalytics
DAY 3 Report!
Day 3 of 30days
🔷 Mastered SELECT with LIKE & wildcard (%) for filtering. Successfully queried emails containing 'tran', names starting with 'A', and phone numbers starting with '080'. Leveled up my SQL skills! 💻
See ya tomorrow!❤️
#SQLwithFunmi #30DaysOfSQL
Day 2 Report – 30-Day SQL Challenge: Completed Basic SELECT Queries! 📌 Mastered extracting specific info, filtering records, and removing duplicates. Consistency is key! 💪 Keep practicing, Let's keep learning together! 🚀 #SQLwithfunmi #SQLChallenge #LearnSQLTogether
Day 2 Report – 30-Day SQL Challenge: Completed Basic SELECT Queries! 📌 Mastered extracting specific info, filtering records, and removing duplicates. Consistency is key! 💪 Keep practicing, Let's keep learning together! 🚀 #SQLwithfunmi #SQLChallenge #LearnSQLTogether
💡Ecited to share that I've joined a 30-day SQL challenge #SQLWithFunmi Although I missed Day 1 & 2 due to neps, I'm not letting that slow me down. I'm diving straight into Day 2 challenge today and planning to catch up quickly. Will give daily report here on X and LinkedIn.💻
💡Ecited to share that I've joined a 30-day SQL challenge #SQLWithFunmi Although I missed Day 1 & 2 due to neps, I'm not letting that slow me down. I'm diving straight into Day 2 challenge today and planning to catch up quickly. Will give daily report here on X and LinkedIn.💻
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