Accenture-Data-Analytics-and-Visualization-

The Accenture Data Analysis and Visualization Virtual Program represents a cutting-edge initiative at the intersection of data analytics, visualization, and virtual collaboration. In this program, we leverage the expertise of Accenture's vast realm of data, extracting actionable insights and presenting them through immersive visualizations.

Key Features:

• Advanced Analytics: Employ cutting-edge analytical techniques to unveil hidden patterns and trends within complex datasets.
• Interactive Visualizations: Transform raw data into compelling visual narratives, providing stakeholders with a comprehensive understanding of information.
• Virtual Collaboration: Break geographical boundaries through virtual platforms, fostering collaborative data exploration and decision-making.
• Industry-Relevant Insights: Tailor analyses to specific industries, ensuring directly applicable insights for diverse sectors.
• Continuous Learning: Cultivate a culture of continuous learning with workshops, webinars, and hands-on projects to enhance data analysis and visualization skills.

Benefits:

• Informed Decision-Making: Empower decision-makers with timely, data-driven insights for strategic planning.
• Global Accessibility: Promote inclusivity and diverse perspectives by facilitating collaboration among teams globally.
• Enhanced Efficiency: Streamline data analysis processes, reducing time-to-insight and improving project efficiency.
• Skill Development: Equip participants with the skills needed to excel in dynamic fields of data analysis and visualization.

The Accenture Data Analysis and Visualization Virtual Program is a commitment to turning data into a strategic asset, utilizing visualization to drive innovation across industries. Join us in this virtual journey of exploration, analysis, and discovery.

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 : Data_Analytics Client Brief

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 :

In the Accenture Data Analysis and Visualization Virtual Program, the data analyst plays a pivotal role by conducting exploratory data analysis, implementing advanced analytics techniques, and creating visually compelling dashboards. Engaging in collaborative virtual exploration, they tailor analyses to specific industries, ensuring relevance and applicability. Continuous learning is emphasized through participation in workshops, supporting the development of skills. Quality assurance measures are implemented, and the analyst contributes strategically by providing data-driven insights to support decision-making. The role involves documentation of methodologies and findings, gathering feedback for iterative improvements, and maintaining adaptability to evolving project requirements. “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 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 which are missing,
• changing the data type of some values within a column, and
• removing columns which 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 set :

• Reaction Types
• Reactions
• Content

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 data set after data modelling & data cleaning : Cleaned Dataset