Data With Luis

I am a huge advocate for hands-on practice when it comes to learning data skills. Whether it's SQL , PowerBI , or Tableau , I will always recommend completing hands-on exercises as part of your learning to become a Data Analyst. As I covered in one of my latest posts  Data Preparation is a crucial skill for data analysts to have in their arsenal, and in this post I will be discussing what I believe to be the best website to practice your data preparation skills, Preppindata.com The website offers a range of challenges and exercises that simulate real-world data scenarios, allowing analysts to practice and refine their data preparation techniques. By providing step-by-step solutions and fostering community discussions, preppindata.com creates an environment for learning and collaboration among data professionals. The platform is important for data analysts because it helps them become more proficient in cleaning, formatting, and transforming raw data, which are critical steps in ens...

As a data analyst, your job is to turn raw data into valuable insights that can inform business decisions. However, before you can even begin analyzing the data, you must first prepare it. This process involves cleaning, formatting, and transforming the data to ensure it is accurate and consistent. Without proper data preparation, your analysis could be flawed and unreliable. Therefore, mastering data preparation is a crucial skill for any data analyst. It will not only save you time and frustration but also ensure that your findings are accurate and trustworthy.  Data Preparation can be done in a number of ways, whether it's by using SQL, with tools like Tableau Prep and Alteryx, PowerQuery in PowerBI or Excel, or using Python.  Here are some of the more common tasks that you'll do during data preparation: Changing column names Changing column data types Splitting columns Removing columns Adding/Removing Prefixes/Suffixes to rows Joining Tables Unioning Tables Watch the video...

As part of the data cleaning process, you will most likely run into a situation where you will have to merge data that is stored in multiple columns.  Some common scenarios I have seen are: Merging separate First Name, and Last Name columns into one "Full Name" or "Customer Name" Column. Creating a "Full Address" column out of separate "Street Address", "City", "State", and "ZipCode" columns. Merging a "CustomerID" column and an "OrderID" column to create a unique identifier. Watch the video below to learn how to merge multiple columns using PowerQuery in PowerBI. @datawithluis How merge in columns in PowerBI. #DataAnalyst #analyst #powerbi #analytics #dashboard #powerquery #powerbitips #datavisualization #businessintelligence ♬ original sound - Luis - Data Analyst Leave me a comment on TikTok if you have any questions! 

PowerQuery has a great set of tools called the "Data Profiling" tools that allow you to quickly understand the quality, distribution, and profile (descriptive statistics) of your dataset.  The Column Quality tool allows you to see the percent rows that are valid, blank, or have errors (typically an error row doesn't match the column data type). The Column Distribution tool gives you a quick visual of the value distribution in each column, along with a count of distinct and unique values contained in every column. I find this tool to be super helpful for data validation as you can use it to quickly spot data errors like 'USA' and 'USA ', where the second one has an extra blank. The Column Profile tool gives you a summary of all the statistics regarding your column sample, it includes the minimum value, max value, standard deviation, unique values, distinct values, and the number of values. It can be very helpful when trying to spot outliers or wrong values....