Accenture North America - Data Analytics and Visualization

Project Overview:

This project was completed as part of the Accenture North America Data Analytics and Visualization Job Simulation on Forage. It focused on advising a hypothetical social media client, Social Buzz, on data analytics strategies.

Deliverables:

1. Excel Dashboard Image: Dashboard created for Social Buzz.
2. Cleaned Datasets: CSV files containing cleaned datasets used for analysis.
3. PowerPoint Presentation: Presentation slides communicating key insights and recommendations for the client.
4. Company Details Document: Document providing background information about the client, Social Buzz.

Skills Utilized:

• Data Analysis
• Data Modeling
• Data Understanding
• Data Visualization
• Presentations
• Strategy

Client Overview:

• Client Name: Social Buzz
• Industry: Social media & content creation
• Established: 2010
• HQ Location: San Francisco
• Number of Employees: 250

Project Details:

• Conducted an audit of Social Buzz's big data practice.
• Provided recommendations for a successful IPO.
• Analyzed content categories to highlight the top 5 categories with the largest aggregate popularity.

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Task - 1

Project Understanding:

A data analyst sits between the business and the data.

• 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.
• Mae has scheduled a project kick-off call with the internal Accenture project team for tomorrow morning.

Task for Accenture :

• Client's Problem that Accenture is tasked to address: The client has reached a massive scale within recent years and does not have the resources internally to handle it.
• Three requirements that Accenture is tasked to fulfill: Audit of big data practice, recommendations for IPO, analysis of popular content

Task for Data Analyst :

Analysis of sample data sets with visualizations to understand the popularity of different content categories.

In short, the client wanted to see “An analysis of their content categories showing the top 5 categories with the largest popularity”.

Task - 2

• Often you won’t need all these datasets to find what you’re looking for.
• So, the first step is to use this data 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.
• After Analysis we got data sets needed to complete analysis:

• Reaction Score(score is used to quantified the popularity)
• Content ID
• Reaction Types
• Content type
• Category

Data Cleaning:

Clean the data 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.

Data Modelling:

Create a final data set by merging 3 tables End result will be one spreadsheet

• A cleaned dataset
• Top 5 categories
• Categories with most reactions
• Month with most posts

Task - 3

Data Visualization and Storytelling:

Make the PowerPoint presentation as per the given template

• Powerpoint Presentation : PPT

Task - 4

Present to the Client:

Present your PowerPoint presentation to the client and deliver the insights of your analysis

Certificate of completion