Sadie's (Mercedez) Blog

This week is the last week in the course, and to wrap it up all blog posts are to be made into a word document and turned into Dropbox.  

This week chapter 8 was assigned. Chapter 8 is the final chapter in the “Data Discovery” book it focused on the Tableau program. This is a program that has been brought up in several different chapters, so I am not completely unfamiliar with it. It works by connecting data sources like Excel spreadsheets, databases, or cloud services. After data is connected users can create visualizations by dragging and dropping fields into rows and columns. Bar charts, line charts, scatter plots, heat maps, and regular maps are just some of the visualization types that Tableau supports. Some of Tableau’s main features are data connection, being able to use many different sources, visualization made easy, combining data, providing different charts, interactive, tools, and sharing. Is popular because it is easy to use, flexible, interactive, and fast. 

 This week chapter 7 was assigned.  Chapter 7 was focused on “Looker Data Studio” Which is a powerful data visualization tool, that was developed by Google. This platform helps users turn data into interactive, dashboards, and reports, this allows analyzation to be easier. Some of the common data sources that Looker Studio uses are  BigQuery, Google analytics, and other Google sources. The dragon drop interface allows, transforming data into charts, tables, maps, and other visualizations easy. Looker Studio is a popular platform, mainly because of its easy integration with other Google products, easy to use interface, and ability to remain free to the public. It is used by small and medium sized businesses, and more. Looker Studio connect to multiple different data sources, allows filtering, calculations, and cleaning up the data. The dashboards are interactive, reports can be customized, and collaboration is easy with sharing. In comparison with Tableau it is just about ...

 This week chapter 6 was assigned. In this chapter, several tools and techniques were discussed, each of them to help analyze and visualize data. Spreadsheets were listed as a common tool for basic data analysis, spreadsheets, allow users to store their data, perform calculations, filter, their data, and create different charts and graphs. Google spreadsheets is one of the spreadsheets listed as an example. BI platforms, or business intelligence platforms, help with advanced data analysis. Tableau was one of the BIP platforms listed. In infographics are visual representations of data that are supposed to make complicated information. Easier to understand. Canva was the infographic that was listed. Data visualization is another helpful tool. Using consistent colors, keeping design, simple, and labeling charts clearly are all things that could help create an effective visualization. Most of the platforms listed for need data visualizations to keep their audiences interested. This cha...

This week chapter 5 was assigned to be read. Chapter 5 explored data-driven marketing, predictive analysis, customer segmentation, supplying chain management, and risk management. Data-driven marketing Uses data from customers to make personalized marketing campaigns. An example would be a beauty company improving their email marketing by sending personalized emails. Predictive analysis uses historical data To predict what might happen in the future. For example, A grocery store could analyze its past sales and predict what kind of demand they have for certain products. Customer segmentation divides customers up based on different characteristics, like their age, or gender. An example of this could be a fashion organization grouping customers based on their interests, and creating personalized shopping experiences for them. Supply chain management uses data to track inventory levels, managed logistics, and forecast demand, which helps products be available when they’re needed, saving m...

This week chapters 3 and 4 were assigned. Chapter 3 focused on understanding where data comes from, the types of data, and how it is managed. Data is sourced from internal or external sources. Internal sources are within the organization, like sales data, or employee data, while external sources are from outside of the organization, Like social media platforms, or research firms. Some ways that data is collected is with surveys and questionnaires, getting data from websites, sharing data through systems and applications, and collecting data through sensors. The quality of data matters. The accuracy, complete Ness, consistency, timelines, and relevance are all important to the quality of data collected.  Chapter 4 introduced data pipelines, warehousing, and modern data infrastructure. Data pipelines are series of steps, which move data to a data warehouse from its original source. The function of a pipeline is to extract data, change it into a usable, format, and load it into storag...

This week chapters 1, and 2 in the "Discovering Data" were assigned this week. Chapter 1 of the book explains how analyzing, interpreting, and presenting data are essential skills needed for organizations, and individuals in an organizations should possess those skills for the organization to run well. If a person does possess those skills, they are likely in high demand. These skills are so important because of the data in the world, and the ability to use that data is important. Many different industries use data, some uses of it would be decision-making, market analysis, customer segmentation, risk management, and performance measurement. Since data is so important to so many different industries there have been jobs made focusing on data collection. Some of those jobs would be data analysis, data scientist, data engineers, and data intelligence analysts. These jobs focus on using data to improve efficiency, optimize operations, and improve growth within a business.   Chap...