MotherDuck’s Post

Guess who's just leveled up their data game? 🙋♂️ That's right, I’ve just completed the "Introduction to Importing Data in Python" course, and it was a blast! 🚀 Here’s a sneak peek at my new superpowers: 📂 Flat Files Fanatic: Mastering the art of importing data from .txt and .csv files with NumPy and pandas. Custom imports? Challenge accepted! 😎 🔄 File Type Wizardry: From Excel to SAS, Stata, HDF5, and MATLAB—if it holds data, I can import it! 📊✨ No file type stands a chance against my new skills. 💾 Database Dynamo: SQL queries, relational models, filtering, ordering, and even those tricky joins—I'm ready to dive deep and extract the most valuable insights from any database! 🗃️🔍 Want to see my journey in detail? Check out my GitHub repository https://lnkd.in/dC__8Xxv where I've documented all my notes and learnings. 📚💻 Let's connect and geek out over data! Share your tips, tricks, and stories—together, we can conquer the data universe! 🌌💪 #DataScience #Python #DataNerd #LearningJourney #DataImport #SQL #Pandas #NumPy #GitHub #LevelUp #ContinuousLearning

🚀 Day 3: Finding Missing Data with pandas 📊 Assessing data quality is crucial before analysis. Today, I learned how to go beyond the classic .info() method in pandas to identify missing data in my dataset. For this purpose, I looked at the San Francisco Building Permits dataset from Kaggle, which includes structural permits from January 1, 2013, to February 25, 2018. 🔍 The Classic Way Most of the time, after loading the data into a DataFrame, I then use the info() method for a summary. This command will provide a concise summary of the DataFrame, including the number of non-null entries per column. But I learned today, a piece of code that gives a clearer and more detailed picture of the missing data. 📈 Calculating Missing Data Percentage For a detailed view, we can actually calculate the percentage of missing entries for each column: missing_data_percentage = (sf_permits.isnull().sum() / sf_permits.isnull().count()).sort_values(ascending=False) print(missing_data_percentage) This code calculates the percentage of missing values for each column and sorts the results in descending order, making it easy to identify the columns with the highest proportion of missing data. 🔔 Conclusion Identifying the percentage of missing data in your dataset is an essential step in the data preparation process. By understanding the extent of missing values, you can make informed decisions on how to handle them, ensuring the accuracy and reliability of your data analysis. You can check my detailed article here on GitHub: https://lnkd.in/gGx2R2bq #DataAnalysis #DataScience #Pandas #Python #DataCleaning #SanFrancisco #BuildingPermits #Kaggle #LinkedInLearning #TechTips And you... what did you learn today? Let's keep growing together! 🚀

🎉 Just unlocked a new milestone in my data journey! Successfully completed the "Introduction to Data Visualization with Matplotlib" course as part of DataCamp's Associate Data Scientist track, enhancing my skills in creating insightful visualizations. 📊💻 🕒 Length: 4 hours 📅 Completed on: July 07, 2024 Thank you, DataCamp team! #DataCamp #Python #DataScience #DataVisualization #Matplotlib #AssociateDataScientist

I learned all types of JOIN and equivalent joins using subqueries. This was probably one of the most challenging courses, but I will revisit this after the next set of courses. Time to learn some Python!

Stumbled upon a fantastic free open source data manipulation & visualization tool during my internet exploration. What's great is that it supports Python and SQL, adding versatility to our workflow. I believe it'll boost our productivity. Give it a try! https://lnkd.in/g38Rxpbi #DataAnalytics #DataVisualization #OpenSource #Python #SQL #ProductivityTools #TechDiscovery #DataTools #DataScience #DataAnalysis

Pleased to announce the release of a new data engineering (ELT) end to end project on my YouTube channel. Here is what we will be doing : 1- We will use kaggle api to download data using python 2- Loading raw data into SQL server raw layer without any transformation. 3- Data cleaning , data modelling and transformations in SQL. 4- Load the cleaned 1 fact and 4 dimension tables in SQL staging layer. 5- Then answering 5 interesting questions on the datasets using SQL. You can follow along with me and create a portfolio project. Link in the comments section. #sql #python #analytics #project

This was another course that I took with datacamp. This one was pretty awesome! I learned how to use python methods like merge(), merge_ordered(), concat(). These function would allow you to merge together two or more tables into one. Also, by using matplotlib, you can then plot that new data into whatever graphical chart you like. What I lerned in this course would be very useful for my future data analytics journey.

Another step taken in this infinite stairway to knowledge, and to becoming a better professional as a Data Scientist. This time, I had the chance to practice with several datasets and learn how to merge them in many different ways. Each method has its advantages, and some can be useful to verify data integrity or even filter the database during merging. Fantastic course! DataCamp #Datascience #DataScientist #pandas #python

Empowering Data Excellence: From DataCamp to Leading the Way! 🚀 Explore our journey, where I've embraced the crucial role of data warehousing, unlocking the power of data for informed decisions, a vital necessity for any industry Some glimps of Data warehousing presentation 💪🏻👨🏻💻 #dataanalytics #businessanalyst #datawarehouse #datascience #ictbusiness #python #developer #presentations #weekend #saturday

🔍 What's a Lakehouse? A Lakehouse brings the best of both worlds the reliability of a data warehouse and the flexibility of a data lake. It's a unified platform that simplifies data management, making it easier to derive insights from diverse data sources. ➡️ Unified Analytics: Analyze data with SQL, Python, R, and Scala, all in one place. ➡️ Data Quality: Delta Lake ensures ACID transactions, data versioning, and schema enforcement. ➡️ Scalability: Handle massive datasets and scale effortlessly as your business grows. #Databricks #Lakehouse #DataManagement #Analytics #techrevolution