Nike is about to use Twitter to run a Marketing Campaign in Africa, they want to first identify who are the top influencers in Africa that can generate the kind of Visibility and Influence they want, Using this paper approach i took the following steps.

• Scraped the different websites using BeautifulSoup and Selenium to extract the top African Influencers and Leaders twitter Handles.

• After Extracting the Twitter Handles it was feed to Twitter API and the Followers Count, Number of Likes, Number of Mentions, Number of Retweets where collected for each of them.

• An analysis was done for each of the Twitter Influncers and Leaders using the collected data and they were ranked based on the following metrics

• Popularity Score(Retweet Influence): measured by the number of Retweets and Likes.

• Reach Score (Indegree Influence): measured by the size of their audience(People who follow them- People they follow)

• Relevance Score (Mentions Influence): measured by the relevancy of their content(Mentions on others tweets)

From this analysis new insights were gained and a report can be found here

There are a lot of ways to approach A/B testing which is a test done to validate the effect of different variable on a particular measure or result.

In this Project an analysis was done using the data gotten from BIO a light weight questionnaire served with Ad campaigns done by Smart Ad a mobile First Advertising Agency to determine the campaigns effectiveness.

The goal was to design a reliable hypothesis testing algorithm for the BIO service and to determine whether the recent advertising campaign resulted in a significant lift in brand awareness.

Methods like Classical and Sequential A/B Testing where used, and Machine Learning algorithms where used to determine feature importance and make predictive analysis.

User analytics on the customer overview use of the Telecom product, customer engagement, experience and satisfaction analysis .

The main goal of the project is to analyze opportunities for growth and identify opportunities to drive profitability by changing the focus of which products or services are being offered. Conclusions were made after doing the following

• Performing data Wrangling and data aggregation to understand the usage of different services among users

• Performing a Uni-variate and Bi-variate Analysis using Matplotlib and Seaborn library in python

I performed an Exploratory data Analysis to understand the bank of Portugal customers and how they make purchases , And built a dashboard using Tableau.