Upskilling in:
Database Design & Development with MS SQL Server;
Database Administration,
Predictive Modeling with IBM SPSS
Business Intelligence Analytics
Cloud computing concepts for Analytics and Machine Learning

Tools: Microsoft Excel, Microsoft SQL Server, PostgreSQL, IBM SPSS, Oracle SQL Developer, Microsoft Azure, Google Cloud Platform, AWS.

Technical support;
Technology and Industry research & Documentation;
Product analysis, planning & implementation;
Building out & testing of API frontend & User Experience with the Angular Framework
Business Process Modeling using BPMN;
Supporting Project Managers with inventory handling, coding, and organization;

A virtual Collaborative learning effort, backed by United Nations Sustainable Development Goals(SDG);
Working to analyze Business Datasets to find growth and profitability curves;
Exploring Data for insights and Quality Assessments.

PROJECTS

1. Simplilearn Data Science Capstone Project (Retail Customer Segmentation): Exploratory Data Analysis, Cohort Analysis, RFM Predictive modelling using Python, and Dashboard Reporting using Tableau. Link: https://github.com/ovokpus/Customer-Segmentation

2. Global Internship SDG project: Exploratory Analytics and Visualization with Python 3. Delivered proof of concept for Analysis and predictive modeling aimed at reducing Employee Attrition in a Company.

Actively involved in Various Data Science and Web Development Self-directed and Instructor-Guided Study Programs, Projects, and Virtual Internships with GE and KPMG.

PROJECTS

1. Car Manufacturing Test. Developed a Prediction model using Python, geared towards reducing time spent in testing various feature combinations for cars. Link: https://github.com/ovokpus/Car-Manufacturing-Test

2. General Electric Digital Technology Analytics Program: Performed Data Engineering on Dataiku DSS to create a single dataset from 8 datasets with the goal of developing a prediction model that will optimize the production process for aircraft parts. Also created a dashboard on Dataiku DSS to present insights on the data.

3. Portfolio Project (Web Development with ComIT): Created a Quiz Game App with Node.js/Express backend functionality. Link: https://github.com/ovokpus/Quiz-app-project

4. Portfolio Project(Data Science): Predicted Employee Salaries based on Job Description using Python 3. Link: https://github.com/ovokpus/Salary-Prediction

5. Consulting Virtual Internship with KPMG: Performed Exploratory Data Analysis and Preprocessing using Excel and Python on Client Dataset. Presented Data Quality report to client and wrote a proposal to Client on creating models that will target prospective buyers.

Key knowledge areas, skills and competencies acquired include:

Data Quality Assessment, Insights and Presentation;
Data Science with R & Python;
Object-Oriented Analysis, Design, & Programming;
Web Development with HTML, CSS, JavaScript, Bootstrap4, Node.js, MongoDB;
ML Regression and Classification Algorithms;
Natural Language Processing (NLP);
SQL; Visualizations with Tableau and Power BI
AWS Machine Learning Foundations; Microsoft Azure Machine Learning Studio; Dataiku DSS, Alteryx Designer..
High-level Overview & practical experience of Big Data & Hadoop Developer tools like MapReduce, Hive, Hue, Apache Sqoop, Apache Flume, Apache Kafka, and Apache Spark.

In the retail Industry, it is a critical requirement for businesses to understand the value derived from their customers. RFM is a method used for analyzing customer value.

Customer segmentation is the practice of segregating the customer base into groups of individuals based on some common characteristics such as age, gender, interests, and spending habits.

Our objective here is to perform customer segmentation using RFM analysis. The resulting segments can be ordered from most valuable (highest recency, frequency, and value) to least valuable (lowest recency, frequency, and value).

Since the first automobile, the Benz Patent Motor Car in 1886, Mercedes-Benz has stood for important automotive innovations. These include the passenger safety cell with a crumple zone, the airbag, and intelligent assistance systems. Mercedes-Benz applies for nearly 2000 patents per year, making the brand the European leader among premium carmakers.

Mercedes-Benz is the leader in the premium car industry. With a huge selection of features and options, customers can choose the customized Mercedes-Benz of their dreams.

To ensure the safety and reliability of every unique car configuration before they hit the road, the company’s engineers have developed a robust testing system. As one of the world’s biggest manufacturers of premium cars, safety and efficiency are paramount on Mercedes-Benz’s production lines. However, optimizing the speed of their testing system for many possible feature combinations is complex and time-consuming without a powerful algorithmic approach.

Our objective in this project is to reduce the time that cars spend on the test bench using model prediction.

Our goal in this project is to examine this dataset of job postings, and predict salaries for a new set of postings. This will involve building a model to predict the salaries given in the test dataset.

A practical use of this is for a HR Department of a large company or a Consulting Outfit that needs real-time solutions in order to make effective employment offers to potential hires.

It also finds use in getting to understand current realities in the job market and how businesses can leverage this in order to secure high quality talent, whilst keeping hiring costs low.

The primary tool used for this project is Python 3, along with an extensive array of libraries and packages available for the manipulation of data,and development of predictive modeling algorithms.