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Accenture Data Analytics & Visualization Internship, in this project i was focusing on Analyzing sample data sets with visualizations to understand the popularity of different content categories.

excel powerpoint-presentations powerquery vlooups

-accenture-data-analytics-and-visualization-virtual-internship's Introduction

-Accenture-Data-Analytics-and-Visualization-Virtual-Internship

Accenture Data Analytics & Visualization Internship, in this project I focused on Analyzing sample data sets with visualizations to understand the popularity of different content categories.

Certificate:

Certificate

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.
  • About Client : Social Buzz

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 data practice, recommendations for IPO, analysis of popular content

Accenture Project Team :

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 model to identify which datasets will be required to answer your business question - which is to figure out the top 5 categories with the large popularity.
  • After Analysis, we got the datasets needed to complete the 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.

End result will be three cleaned data sets:

Data Modelling:

Create a final data set by merging 3 tables

End result will be one spreadsheet

  • A cleaned dataset
  • Top 5 categories

Cleaned Data set:

So, the cleaned dataset after data modeling & data cleaning : Cleaned Dataset

Task - 3

Data Visualization and Storytelling:

Make the Powerpoint presentation as per the given template

Charts Involved:

  • Pie Chart
  • Bar Chart
  • Powerpoint Presentation : PPT

Task - 4

Present to the Client:

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

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