StrataScratch

Simplify list operations in Python with this comprehensive guide. Learn efficient techniques to add and remove elements seamlessly, enhancing your coding skills 👇🏻 . . . . #PythonProgramming #CodingTips #LearnPython #DataScience #ProgrammingTutorials #TechSkills #StrataScratch #PythonLists #PythonDeveloper #CodingJourney

#SQLproblem Damn sure you will be surprised solving this, A Data Analytics based question related to the marketing domain question listed on StrataScratch 📊 Calculating Spotify's Active User Penetration Rate by Country 🌍 Market penetration is crucial for understanding Spotify's performance and growth potential in different regions. As part of the analytics team at Spotify, you have to calculate the active user penetration rate in specific countries. Here's how they define an "active user": last_active_date: The user must have interacted with Spotify within the last 30 days. sessions: The user must have engaged with Spotify for at least 5 sessions. listening_hours: The user must have spent at least 10 hours listening on Spotify. Based on these criteria, they calculate the active user penetration rate using the formula: 🔢 Active User Penetration Rate = (Number of Active Spotify Users in the Country / Total Users in the Country) * 100 Note: Penetration rate or segment reach etc it can be called by different name but this is how you should approach. This metric helps business to understand how well Spotify engages its users in different regions. The output will include the country and the active_user_penetration_rate rounded to two decimals. Let's assume the current day is January 31, 2024, for this calculation. Understanding these metrics allows to tailor our strategies to enhance user engagement and growth in specific markets. Stay tuned for more insights on data analytics and how we leverage data to drive decisions at Spotify! #DataAnalytics #Spotify #MarketPenetration #UserEngagement #DataDriven #BusinessIntelligence #SQL #DataScience #GrowthStrategy Question Link: https://lnkd.in/g5sZj-Mj

I solved this SQL query today from StrataScratch. For this query, I had to find the advertising channel with the smallest maximum yearly spending that still brings in more than 1500 customers each year. 💡 I used CTEs, window functions, and aggregate functions to solve this query. 💡 Here is the table that was used to solve this query: 🎯 uber_advertising: - year (int): the year of the Uber advertisement - advertising_channel (int): the channel of the Uber advertisement - money_spent (int): the amount of money spent on the Uber advertisement - customers_acquired (int): the number of customers that the Uber advertisement brought in 💡 Here is a breakdown of the CTEs that I used in this query: 🎯 agg_table: - This CTE calculates the total number of customers acquired and the total money spent per year for each advertising channel. - I used the SUM function on the customers_acquired and money_spent columns. - I grouped the aggregation by the advertising_channel and year columns. 🎯 agg_table_ranks: - This CTE ranks the agg_table output by the largest total money_spent in ascending order. - I used the RANK() window function and the MAX function on the total_money_spent column of the agg_table to order the rankings. - I grouped the aggregation by the advertising_channel column - I filtered the output to advertising channels with the least total customers acquired greater than 1500. 💡 Finally, I selected the required columns from the agg_table_ranks CTE and filtered the output to ranking numbers equal to one. #sql #windowfunction #cte #stratascratch #uber

Good Morning Folks! Today I've solved a Interview question of Meta from StrataScratch of Python. 💻Problem: What is the overall friend acceptance rate by date? Your output should have the rate of acceptances by the date the request was sent. Order by the earliest date to latest. Assume that each friend request starts by a user sending (i.e., user_id_sender) a friend request to another user (i.e., user_id_receiver) that's logged in the table with action = 'sent'. If the request is accepted, the table logs action = 'accepted'. If the request is not accepted, no record of action = 'accepted' is logged. Approach : 1. To calculate the overall friend acceptance rate by date, we need to merge two dataframes: one containing the friend requests that were sent (action = 'sent') and another containing the friend requests that were accepted (action = 'accepted'). We can then group the merged dataframe by date and count the number of accepted requests for each date. 2. To merge the two dataframes, we can use the pandas merge function. First, create two new dataframes: one for the sent requests and another for the accepted requests. Use the condition  fb_friend_requests.action == 'sent' to filter the rows for the sent requests, and fb_friend_requests.action == 'accepted' to filter the rows for the accepted requests. Then, merge the two dataframes using the merge function, specifying the columns to merge on (user_id_sender and user_id_receiver) and the merge type (how='left'). 3. To calculate the acceptance rate, group the merged dataframe by the date the request was sent (date_x), and then count the number of accepted requests for each date. Reset the index of the resulting dataframe to remove the grouping. Finally, calculate the acceptance rate by dividing the count of accepted requests (action_y) by the count of sent requests (action_x). Create a new column in the dataframe called acceptance_rate to store the calculated values. . . . . . . . Hope you find it helpful 😊 happy learning! #stratascratch #python #problem #solving #dataanalytics #datascience #businessanalytics #sql #datasciencejourney #meta #python

#SQL Type Conversion Made Easy We know the pain points of SQL data conversions. Our comprehensive guide on casting INT in SQL will streamline your process. Make your queries more efficient with our step-by-step guide! . . . . #DataScience #DataManagement #TechTips #LearnSQL #BigData #DataEngineering #Programming #DataAnalysis #TechCommunity #WomenWhoCode

Landing a Data Engineer Role: Free Courses and Certifications Is it possible to learn data engineering for free? I claim it is and present the evidence for that in the form of 10 free data engineering courses.

Take On Pearson's Data Challenge! Use #Python to analyze student performance and transform education with Pearson's latest project! . . . . . #DataScience #DataAnalysis #Education #PredictiveModeling #ChiSquare #RegressionAnalysis #DataVisualization #PearsonChallenge #DataProject #StrataScratch

Tools Every Data Scientist Should Know: A Practical Guide Discover the essential tools every data scientist should know to elevate their data science game, from Python and R to SQL and advanced visualization tools.

String concatenation in #Python is more than just a basic skill—it's a game-changer for efficient #programming and #datamanipulation. Our latest article covers the + operator, join(), f-strings, and the format() method. Don't miss out on these practical examples! . . . . #Coding #DataScience #LearnPython #TechTips #SoftwareDevelopment #DataAnalysis #PythonProgramming #CodingSkills #CodeBetter #PythonTips #DeveloperCommunity #MachineLearning

After days of practicing SQL and solving as many problems as I could, today my face is full of joy. I tackled a medium-level question on StrataScratch I finally took a lesson from my mistakes and tried to shorten the code as much as possible, only resorting to writing long code when necessary. I arrived at the same solution without introducing subqueries into my code. I guess those who know better were right when they said there are multiple ways to solve the same problem with code.