Agile methodology is a way of making software that’s flexible and team-focused. It helps teams work better and deliver what customers really want.
In short, Agile helps teams create better software by working together closely, being flexible, and always focusing on what the customer really needs.
This will be a collection of some of my go to sites for learning. If you have a site that you think I should check out and add to this list then please send it to me.
]]>To explore if there is a correlation between product ratings and return rates, we analyzed the available data but were unable to see an impact on returns based on the ratings given by customers. The ratings only deviated by 3%, and products with mediocre ratings had the highest sales. This suggests that factors other than quality may influence customer ratings.
We would need to work with the business/it and capture verbatims, shipping delays and follow up with customers to get a better understanding of what is important to the customer.
Check out the excel version Project
Check out the SQL tab to see some of the sql I used to pull in the data.
]]>The primary objective of this project was to analyze Zillow data to provide strategic recommendations for launching a real estate company. Given the drastic downward trend with average homes sold dropping 30% since 2020, I recommend a limited scope launch.
Launching in Los Angeles offers a balanced approach to entering the real estate market, leveraging strong local performance while minimizing broader market risks. This strategy allows for establishing a solid operational foundation and market presence, setting the stage for future growth and expansion.
This project was to look at Zillow data and provide recommendations on how to launch a real estate company. All data used for this project came from Zillow. This one is a bit different from other projects that I have built as I tailored it for a presentation with the thought of converting it to a tracking tool if the company went live.
I began by analyzing the data to identify trends based on area. While graphing the data, I noticed a drastic change from 2020 to 2023. The Covid lockdowns drove a spike in sales due to increased remote work, but this also led to a subsequent downturn, making it a challenging environment to launch a real estate business.
There were three options to consider: a nationwide launch, a limited scope launch, or no launch at all. Given the 30% decrease in home sales since 2020, launching nationwide carries a high degree of risk. My recommendation is to launch in a single location, such as Los Angeles. Los Angeles still has a strong average number of homes sold per month and a lower mean number of days to close. This approach allows the company to establish its culture, define its market presence, and then expand into other markets.
The last couple of pages outline potential future offerings and data sets that could help the company grow and dive deeper into the business.
Used 2 dax calculations to determine the difference between 2023 and 2020. Set it up to ensure that if they changed the date filter it wouldnt break the results
2023 price = CALCULATE([AVG Sale Price],FILTER(all(‘Calendar Cust’),’Calendar Cust’[Year] = 2023 ))
Calendar function - tied to min /max date off of data project so that if data was updated the calendar updated as well. Also broke out the date so that I could filter properly and the months would not filter in wrong order.
Calendar Cust = ADDCOLUMNS ( CALENDAR (MIN(‘Client Analyst Data Project - Sales Count’[Month]), max(‘Client Analyst Data Project - Sales Count’[Month])), “Calendar Date”, [Date], “Year”, YEAR ( [Date] ), “Calendar Month”, FORMAT ( [Date], “MMM” ) & “ “ & right(format([Date], “yyyy”),2) , “Calendar Week”, “CY “ & right(format([Date], “yyyy”),2) & “ - “ &FORMAT ( [Date], “MMM” ) & “ WK “ &weeknum([Date]), “Month Number”, VALUE(YEAR ( [Date] ) & format(MONTH([Date]),”00”)), “Month Sort”,MONTH([Date]), “Month Name”, FORMAT ( [Date], “MMMM” ), “Day”, DAY ( [Date] ), “Day of Week Number”, WEEKDAY ( [Date] ), “Day of Week Name”, FORMAT ( [Date], “dddd” ) )
We are successfully maintaining our no-kill shelter status by staying above the 90% threshold, currently achieving a 93.9% live release rate. While this is commendable, it marks an 8% decrease from the previous year.
The primary factor contributing to this decline is the incidence of rabies within the “other” classification of animals. To address this issue, it is recommended to:
By addressing these areas, we can work towards improving our live release rate and ensure the ongoing success of our no-kill shelter.
Since I have been volunteering with an animal shelter, this data was fun to peek at to understand more about how shelters work.
This particular data set threw some unique challenges. I expected some duplication within the data as some animals could come through the system multiple times. To check I performed a ranking function within power query to see if that would remove duplications based on animal id and date. There were still duplications but since it was only 90 records out of 160 k, I just had power query remove duplicates.
To compare this year vs last year crafted 2 dax measures and set them up so if they changed filters it wouldnt change the value. Here is one of them. Current Year = CALCULATE([Cases], filter(all(Austin_Animal_Center_Outcomes_20240818),year(Austin_Animal_Center_Outcomes_20240818[DateTime])=year(today())&& Austin_Animal_Center_Outcomes_20240818[Outcome Type] = “Euthanasia” )) / CALCULATE([Cases], filter(all(Austin_Animal_Center_Outcomes_20240818),year(Austin_Animal_Center_Outcomes_20240818[DateTime])=year(today())))
How to do ranking within power query
]]>Our initial goal was to provide recommendations for business improvements based on the sales data. However, during our analysis, we identified several data discrepancies that need to be addressed before moving forward. Key issues include:
The initial objective of this project was to analyze sales trends over time, focusing on different types of pizzas down to the ingredient level. The insights gathered would have been utilized for forecasting ingredient usage and optimizing stocking levels.
During the data build-out, I noticed that the data appeared unusually uniform. This prompted a deeper investigation, revealing several anomalies that raised concerns about the accuracy of the data:
In preparation for a more detailed analysis, I aimed to explore the topping distribution in depth. To achieve this, I:
This process revealed another inconsistency: garlic emerged as the most frequent topping, which was unexpected.
Given these discrepancies, the following actions are recommended:
This experience reinforced the importance of trusting instincts when data appears abnormal. Continued diligence in investigating anomalies ensures the integrity of the analysis.
]]>_posts directory. Go ahead and edit it and re-build the site to see your changes. You can rebuild the site in many different ways, but the most common way is to run jekyll serve, which launches a web server and auto-regenerates your site when a file is updated.
Jekyll requires blog post files to be named according to the following format:
YEAR-MONTH-DAY-title.MARKUP
Where YEAR is a four-digit number, MONTH and DAY are both two-digit numbers, and MARKUP is the file extension representing the format used in the file. After that, include the necessary front matter. Take a look at the source for this post to get an idea about how it works.
Jekyll also offers powerful support for code snippets:
def print_hi(name)
puts "Hi, #{name}"
end
print_hi('Tom')
#=> prints 'Hi, Tom' to STDOUT.Check out the Jekyll docs for more info on how to get the most out of Jekyll. File all bugs/feature requests at Jekyll’s GitHub repo. If you have questions, you can ask them on Jekyll Talk.
]]>Writing about your projects is crucial to giving context for the work. Writing an impactful article requires a structured approach that captures readers’ attention and delivers valuable insights. I use my BARN framework to achieve this goal: Background, Approach, Results, and Next Steps. In this article, we will delve into each element of the BARN framework and explore how it can enhance your writing skills.
The Background section of an article serves as the foundation, providing context and establishing the relevance of the topic. It sets the stage for readers to understand the problem or subject matter being addressed. To effectively craft this section, consider the following:
1. Hook the readers: Begin with an engaging hook or anecdote that grabs readers’ attention and piques their curiosity.
2. Problem statement: Clearly define the problem or questions that your project aims to address. Explain why it is significant and how it relates to the readers’ interests or concerns.
The Approach section outlines the methodology or approach used to investigate the problem or answer the problem statement. It is essential to provide readers with a clear understanding of how the research or analysis was conducted. Consider the following when constructing this section:
1. Methodology: Describe the research design, data collection methods, and any tools or techniques employed. Ensure your explanation is concise yet comprehensive enough to allow readers to undestand your process.
2. Data analysis: If applicable, detail the analysis techniques used to interpret the gathered data. This could include statistical methods, qualitative analysis, or any other relevant procedures.
3. Assumptions and limitations: Acknowledge any assumptions made during the process and highlight the potential limitations. Call out any challenges you faced. This demonstrates transparency and helps readers evaluate the validity of the results.
The Results section presents the key findings and outcomes of your research or analysis. It is crucial to present this information in a clear and organized manner to facilitate readers’ understanding. Consider the following tips:
1. Summarize findings: Provide a concise summary of the main findings, focusing on the most important and impactful results.
2. Visual aids: Utilize charts, graphs, tables, or screenshots to present complex data in a visually appealing and accessible format. This enhances reader comprehension and engagement.
3. Interpretation: Offer a thoughtful interpretation of the results, highlighting their significance in relation to the problem statement and the existing body of knowledge. Consider addressing unexpected or conflicting findings and proposing potential explanations.
The Next Steps section outlines potential future directions, implications, or recommendations based on the findings of your article. This section demonstrates your forward-thinking approach and encourages further exploration of the topic. Consider the following aspects:
1. Implications: Discuss the broader implications of your findings and how they contribute to the field of study or the problem at hand. Consider societal, economic, or practical implications.
2. Recommendations: Offer recommendations for further research or actions that can be taken based on your results. This could include areas that require additional investigation, potential applications, or suggested policy changes.
3. Conclusion: Summarize the key points discussed in your article, emphasizing the value and impact of your project.
Mastering the art of writing about your project requires a systematic and well-structured approach. The BARN framework, encompassing Background, Approach, Results, and Next Steps, provides a roadmap for creating engaging and informative articles. By leveraging this framework, you can effectively convey the context, methodology, findings, and future implications of your work, capturing readers’ attention and leaving a lasting impact in your field of expertise. So, pick up your pen or open your word processor, and let the BARN framework guide you on your journey to becoming a skilled and influential article writer.
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