Data Buzz Analysis Project

Hello and welcome, my name is Agunwa Glory and today I will be presenting to you the results of the Data Analytics task. 

Today's agenda 

1. I will recap the overall project to give a high-level understanding of the business problem we're tackling and the specific requirements. 

2. I will dive into the specific problem that I, the Data Analyst, have been focusing on and will give some background as to why this is such a big problem. 

3. After introducing the problem, I will introduce myself, who’s the data analyst responsible for tackling this task.

 4. I will then go over the high-level process that I followed to complete this task, so that you have complete clarity in how I tackle these kinds of tasks. 

5. Finally, I will go over all important results and I will present them as a series of insights and visualizations from our analysis. 

To wrap up, I will summarize and open for any questions. 

Tools Used:

Problem

 Focusing on the last point that I mentioned there, this is what I, the  Data Analyst has been  specifically focused on. Clearly with such grand scale, this comes with a lot of data and with such vast amounts of data comes challenges. 

To give a background on how much data you've been creating:

 - You told me that your platform receives over 100000 posts per day which amounts to 36 500 000 posts every year, of which, this is all unstructured data making it very hard to make sense of. In this day and age, content is king. Just look at some of the biggest platforms in the world, for example YouTube, Facebook and Netflix... they are all content businesses... But how to capitalize on it when there is so much? It's not just all about harvesting as much content as possible... 

The real value is in understanding and crunching this content to gain a deeper understanding of your audience and to therefore provide a more personalized and enjoyable experience. 

And this is where my data analytics expertise comes in, with the insights that I've uncovered from this task, I can show you exactly how to take analytics to production at scale 

Meet the Data Analyst 

I am Agunwa Glory, who was solely responsible for taking leadership guidance and delivering high quality insights from the raw datasets and turning these into business decisions. 

Process

 So, how did we tackle this problem? Well, I approached it in 5 steps:

 1. Data understanding - the key to success on any data project is to understand the data in detail. So I took the time to understand the data model and domain of your business.

 2. Data extraction - after understanding your business, I then architected what an ideal dataset should look like for this problem and extracted it from the relevant data sources. 

3. After extracting the raw data, I needed to process and model this data into a dataset that can precisely answer the business questions and produce analytics. 

4. With my new dataset, I used my analytical expertise to uncover insights from this dataset and to produce visualizations to describe the insights. 

5. And finally I used these insights to unlock business decisions and to make recommendations on next step