Data With Luis

Power BI is one of the top data visualization tools to have in your arsenal as a data analyst. By mastering Power BI, data analysts can efficiently process, transform, and visualize data, enabling them to uncover valuable insights and trends. Power BI's interactive dashboards and reports empower analysts to present complex information in a user-friendly manner, fostering effective communication and understanding among stakeholders. Additionally, Power BI's integration with other Microsoft tools and its wide adoption in the industry make it a valuable skill that can boost a data analyst's career prospects and opportunities. Here are my favorite FREE resources to get started with Power BI: Download PowerBI -  Downloads | Microsoft Power BI Microsoft Learn:  Microsoft Learn offers traditional e-learning, finish the following learning paths to learn all the theory and best practices for Power BI directly from Microsoft.  Get started building with Power BI - Training | M...

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...

If you're looking to break into the field of data analytics, networking is essential for success. While having the right skills and education is important, building a strong professional network can help you gain valuable insights, find new job opportunities, and accelerate your career growth. Here are some reasons why networking is crucial when breaking into data analytics: Access to Job Opportunities: more and more job openings  are not advertised and are filled through networking . By building relationships with other professionals in the field, you may gain access to job openings that you wouldn't have found otherwise. Learning About New Trends and Technologies: The field of data analytics is constantly evolving, with new technologies and trends emerging all the time. Networking can help you stay up to date with the latest developments and learn from other professionals who are working on cutting-edge projects.  Getting Advice from Experienced Professionals: Networking pr...

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....