I engaged in a project involving data analysis and visualization while working as a Data Analyst at Accenture. In a diverse team, each with unique roles. We got a project for client "Social Buzz (Client industry: Social media & content creation)". This project provides a chance for me to demonstrate my expertise in data analysis and visualization, which I believe could contribute to my career advancement. This exciting opportunity aligns perfectly with my aspirations a enables me to display my skills effectively within the team.
Action: I carefully cleaned the dataset, removing outliers and handling missing data. Using Navigating Numbers, I created impactful visualizations, choosing appropriate chart types. I collaborated with teammates to set SMART project goals and relevant KPIs. Result: Through my efforts, the dataset became more accurate for analysis. The visualizations effectively conveyed insights to both technical and non-technical audiences. Clear project goals and KPIs facilitated project tracking and alignment with objectives.
The team has been assigned a new project for a client called Social Buzz
About Client : Social Buzz
One of Accenture’s Managing Directors, Mae Mulligan, is the client lead for Social Buzz. She has reviewed the brief provided by Social Buzz and has assembled a diverse team of Accenture experts to deliver the project. I was assigned to address missing data, outliers, and data quality concerns in a dataset. Additionally, I had to utilize Navigating Numbers' visualization tools, define project goals, and select key performance indicators (KPIs).
- Creation of an up-to-date big data best practices presentation
- Extraction of sample data sets using SQL
- On-site audit of their data center
- Merging of sample data set tables
- Virtual session with the Social Buzz team to present previous client success stories relevant to them
- Preparation of best practice document for IPO
- Loading of sample data sets into the Accenture sandbox database
- Technology architecture workshop with Social Buzz Data Team to understand their technology landscape
- Stress testing of their technology to identify weak spots
- Communication with previous IPO companies within our client base for reference stories - Analysis of sample data sets with visualizations
- Full documentation of the process that we can guide them through for IPO
- The Business refers to the client and internal team members who won’t be involved in detailed data analysis.
- They rely on our analysis to make strategic business decisions.
- Importantly, not everyone will have a strong understanding of data. Our job is to communicate your data findings simply and clearly for everyone to understand.
- The Data refers to the relevant data sources that we will clean, process, and use to generate interesting insights for the business.
Accenture Project Team
The client has sent through:
- 7 data sets - each data set contains different columns and values
- A data model - this shows the relationships between all of the data sets, as well as any links that you can use to merge tables.
There is a lot of information here and it’s easy to get lost in the data. So, to make sure you are using the right data to answer the business questions we’ll follow these steps:
- Requirements gathering
- Data cleaning
- Data modeling
7 datasets and a data model. Often you won’t need all these datasets to find what you’re looking for. So, the first step is to use this data model to identify which datasets will be required to answer your business question - which is to figure out the top 5 categories with the largest popularity.
Data sets - Quick Explanation To clarify why we made this selection:
- The client wanted to see “An analysis of their content categories showing the top 5 categories with the largest popularity”.
- As explained in the data model, popularity is quantified by the “Score” given to each reaction type.
- We therefore need data showing the content ID, category, content type, reaction type, and reaction score.
- So, to figure out popularity, we’ll have to add up which content categories have the largest score.
But! Before we begin to work with the data sets, we’ll need to ensure that the data is clean and ready for analysis…
Data cleaning is a common and very important task when working with data. First: Open the three data sets below
Second: Cleaned the dataset by:
- Removing rows that have values that are missing,
- Changing the data type of some values within a column, and
- Removing columns that are not relevant to this task.
- Think about how each column might be relevant to the business question you’re investigating. If you can’t think of why a column may be useful, it may not be worth including it.
Make the Powerpoint presentation as per the given template
Powerpoint Presentation : Download File Here
Present powerpoint presentation to the client and deliver the insights of your analysis