This is my attempt at the Forage Accenture Data Analytics Virtual Experience which you can find here.
Forage Accenture Data Analytics Virtual Experience is a intership simulation which presents some real industry tasks to be resolved by the candidate.
In this specific assignment the candidate is a Data Analyst who is expected to understand the client and business problem at hand and provide important insights using the avaliable data sets.
Task 1: Project Understanding
You’ve been assigned to a new project and the first thing you must do is get up to speed with the business problem that this project is tackling, the requirements that need to be fulfilled as deliverables and the alignment of internal teams with the client.
You will be working within a large team at Accenture and there will be several people on your team doing different roles to make this project a success.
As a data analyst, it is imperative that you have a solid understanding of the project as quickly as possible. A data analyst sits between the business and the data, so it’s important that you have a deep understanding from both sides to provide insights. You need to use the client brief to:
- Understand the client and business problem at hand.
- Identify the requirements that need to be delivered for this project.
- Identify which tasks you should focus on.
To make sure that you have understood the project, you will be tested on an outline of the business problem, the requirements and the delegation of tasks.
Here is your task:
1. Client background
Before attempting to complete this task, you should take your time to fully understand the client brief.
2. Internal background
After this, you should read through the organization map and each individual's role and responsibilities. This will give you a holistic understanding of the Accenture team working on this project and each individuals’ role and responsibilities.
3. Project understanding
Once you’ve read the resources, make sure you have a confident understanding of the following three questions:
- What is the business problem that Accenture is trying to tackle for this client?
- Based on this business problem, what are the requirements that Accenture is expected to deliver?
- Finally, based on these requirements, what tasks are most likely to be relevant to you as the data analyst?
Task 2: Data Cleaning & Modeling
You have been given a set of data sets, all containing different columns and values, as well as a data model. A data model shows the relationships between all of the data sets, as well as any links that you can use to merge tables.
It is your job to use these data sets as well as the data model, to create your own data set that you can use to fulfill the requirements of this task.
This task is essential because it is one of the specific requirements that the client has asked for, so we are relying on the data analyst to work with the data and create accurate, reliable insights that can be backed up during the final presentation.
Here is your task:
1. Requirements gathering
To complete this task, you’ll first want to look at the task that you’ve been assigned and think about which available data sets may be useful to fulfill the requirements. It may be helpful for you to write the data sets down that you think will be best for the task. You should take time to fully understand the data model and how the tables link together. Then you should think about which tables are most relevant.
2. Data cleaning
Before we begin to work with the relevant 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. This includes removing columns that have a high number of missing values, removing rows that have values which are erroneous, changing the data type of some values within a column and also removing columns which are not relevant to this task. Your end result should be a set of relevant data sets that are clean with each data set containing only the columns which are relevant to the completion of this task.
Think about how each column that you choose can be used to fulfill the requirements of this task. If you can’t think of why a column may be useful, it may not be worth including it. It may be helpful to write down which columns you think would be important to keep.
3. Data modeling
Finally, use this knowledge to create a final data set containing all of the columns that you will need to complete the task. You can use Excel or any other tool of your choice to create your final data set. Based on which columns you think will be most useful, you may be required to merge tables together by using the Unique Keys within tables. You should spend some time modeling the data to create your final data set for this task.
All of the data sets will be provided as CSV files and you can do the data modeling in Excel or any other tool that you wish. Your final submission should be a single CSV or Excel spreadsheet containing the columns and data that you think will fulfill the requirements of this task best.
Task 3: Data Visualization & Storytelling
As a data analyst, it is important that you work with both the business and the data. Now that you have gained the business understanding, it’s time to put your analysis skills to the test.
The next step will be bringing the data set to life. A large component of being a data analyst, includes the ability to find relevant insights and ensure that they are clearly communicated to the business and client. These insights should be synthesized and placed in a presentation for the client that is engaging and easily understood.
There is an art to selecting the correct visualizations for your presentation and it’s important that corresponding communications tell a powerful story. For this reason, the powerful tools of data visualization & storytelling will be key to this task.
The analysis that you provide will play a key part in the fulfillment of this requirement, so you will play an important role in this project!
Here is your task:
1. Data visualization
You will need to use your data set to create insightful visualizations to address the requirements of the project. Use your available data set to create a series of visualizations that address the task that you’re working on. Be creative when visualizing your data but remember that results and clarity should take precedence – don’t trade functionality for fancy appearance! Experiment with visualizations and decide on which ones to move forward with.
2. Create a presentation
It is your job to ensure that the presentation of the results is clear and can be easily understood by your internal team and the client. You should create a PowerPoint slide deck that presents your results with accompanying slides and visualizations. Not all slides need to contain visualizations – use some slides to set up the story that you’re telling! It is important to make sure that your presentation follows a clear structure and takes your client on a journey through their data and business problems. Your slide deck should be concise, approximately 10 slides. You should also include an introduction slide where you introduce yourself and your role on the project.
3. Storytelling
Make sure that throughout your presentation you incorporate storytelling. Make use of the techniques outlined in the additional resources and try to make the presentation as engaging and persuasive as possible. Using soft and hard skills, such as presentation and statistical analysis respectively, is key to a data analysts’ job.
My notes
Virtual enviroment:
- To create: python -m venv <name_env>
- To activate: source <name_env>/bin/activate
Project requirements
- Pandas: pip install pandas
- Matplotlib: pip install matplot
- Seaborn: pip install seaborn