Max's BALT 4361 Blog

Looker Studio Formerly known as Google Data Studio, Looker Studio is a powerful data visualization and reporting tool. This is a great tool that is free and widely used and can be an excellent skill addition for resumes. Looker Studio uses data from sources such as Google Analytics, Google Sheets, Google Ads, and BigQuery. There are also many more it can pull from, however, it uses a drag-and-drop interface that is easy to use for everyone no matter the skill set. Functions Over the years, Looker Studio has gained popularity among small and medium-sized businesses, marketers, and data analysts. Its seamless integration with other Google products makes it an easy choice for use and recommendation. With it being free, it's a no-brainer for companies that may be small and lack the budget for another program, essentially being risk-free. Some functions available are integration, transformation, interactive dashboards, customization, and collaboration and sharing. Comparison Another dat...

Ways Data Can Be Applied Visualizing data is more important than anything because, without organized data, it's a mess of vague numbers. Spreadsheets are the most basic of data analysis and some of the most common such as Microsoft Excel or Google Sheets. From these, graphs and charts are able to be created allowing for easier interpretation. A function of Google Sheets is that it has a lookup section where you can look up how to use functions in your dataset.  BI Platforms More advanced analysis requires specialized software such as business intelligence (BI) platforms. Some notable ones are Tableau and Power BI as they provide more advanced data analysis capabilities where you can perform data modeling, predictive analysis, and data mining. Some visualization tools within BI platforms allow for the creation of interactive dashboards that allow you to better understand your data. There can be multiple charts and interactive visualizations in one place on a dashboard that provides ...

Business Data Marketers can now leverage data to analyze customer behavior, preferences, and buying patterns to make targeted campaigns. An example given in the text is related to beauty marketing where they collect data related to customer preferences, purchase history, and email engagement. They then use it to identify patterns and create personalized and relevant campaigns. From this, they sent out targeted emails and saw a 20% increase in the conversion rate of their email campaigns. Predictive Analytics Predictive analytics shows us that historical data helps us predict future events. This is especially useful in company forecasting to identify trends and anticipate customer behavior, which in turn helps companies make better decisions to reduce costs and improve overall performance. Supply chain predictive analytics can help companies understand how to improve inventory levels and reduce overall waste. This method uses a predictive analytics model through a machine learning platf...

The Source of Data Data comes from many different sources both internal and external and can be categorized into two major types: structured and unstructured. Structured data is organized specifically in tables with rows and columns, which is what most of us know as Excel, however, it can also be seen in databases and CSV files. Unstructured data on the other hand lacks formatting and organization. Examples of this can include emails, text docs, images, audio files, and videos. This type of data is harder to analyze and requires the use of natural language processing and machine learning to analyze effectively. Methods of Collection Collecting this data is seen through the use of surveys and questionnaires, web scraping, application programming interfaces (APIs), and sensors and IoT devices. All of these techniques have their benefits and some may prove more useful than others when it comes to their use cases and application. For example, in my line of work in the market research indus...

Chapters 1 & 2: Data & Roles Data skills are vital and growing in demand in the workplace. Making sense of large sets of data is essential for accurate analysis, allowing companies to maximize their value and gain a competitive edge through optimized forecasting. Decision-making, market analysis, customer segmentation, risk management, and performance measurement are all ways we analyze data. This can make companies more efficient overall when it comes to synthesizing real-time data for necessary changes. Major data roles have been emerging over the past couple of decades, however, they are more important than ever as so much of what companies do on a daily basis revolves around data analysis. Data Analysts for example are the ones who collect, process, and analyze data for decision-making purposes. Data scientists possess advanced skills and technical knowledge of machine learning algorithms to analyze and model complex data sets. This is especially useful for predictive model...

 Hello, my name is Maxwell Welu and this is my blog for BALT 4361.  I'm from Naperville, IL, and am in one of my final semesters at Benedictine. I've taken another AI class with Professor Kelsey before and cannot wait to start with this one. Professor Kelsey is a great professor who is passionate about what he teaches and facilitates growth with his students. Enjoy the blog posts to come.