In today’s rapidly evolving digital landscape, data has become the cornerstone of innovation, decision-making, and problem-solving across industries. The Bachelor of Engineering in Data Analytics and Engineering is designed to equip students with the technical expertise and analytical skills needed to harness the power of data in engineering contexts. This interdisciplinary program combines the principles of engineering with advanced data analytics techniques, providing students with a robust understanding of both fields. Through a rigorous curriculum, students learn to collect, process, and analyze large datasets, while applying engineering methodologies to solve real-world problems in diverse sectors such as manufacturing, healthcare, finance, and technology.
| PROGRAMME |
SEMESTRE |
DURATION |
CREDIT |
PARTNER INSTITUTION |
ACCREDITATION |
| DATA ANALYTICS ENGINEERING |
8 SEMESTERS |
4YEARS |
240 |
UNIVERSITY OF BUEA |
|
CORE OBJECTIVES
1. Mathematics and Statistical Foundations
- – Calculus I, II, III : Foundational courses in calculus, focusing on functions, derivatives, integrals, and multivariable calculus, crucial for problem-solving in data analytics and engineering.
- – Linear Algebra : Study of vectors, matrices, and systems of linear equations, essential for data transformations and machine learning algorithms.
- – Probability and Statistics : Introduction to statistical methods and probability theory, fundamental for data analysis, hypothesis testing, and predictive modeling.
- – Discrete Mathematics : A course focusing on set theory, combinatorics, and graph theory, which are integral to data structure design and analysis.
2. Basic Engineering and Computing Courses
- – Introduction to Computer Science : A course covering programming concepts, algorithms, and data structures, which are fundamental to data engineering and software development.
- – Data Structures and Algorithms : Study of algorithms for sorting, searching, and manipulating large datasets, important for optimizing data processing and analysis.
- – Database Systems : Principles of database management systems (SQL, NoSQL), covering data storage, querying, and the design of data storage solutions.
- – Software Engineering : Introduction to software development practices and tools, emphasizing the creation of robust and scalable data processing applications.
3. Data Systems and Infrastructure
- – Data Modeling : Techniques for designing data models that ensure efficient storage, retrieval, and management of data.
- – Big Data Technologies : Introduction to platforms and tools such as Hadoop and Spark for handling large-scale data processing.
- – Cloud Computing : Understanding of cloud platforms (AWS, Google Cloud, Azure) for scalable data storage and computing resources.
- – Data Pipeline Design : Design and implementation of automated pipelines to move, transform, and store data efficiently.
4. Data Analytics and Machine Learning
- – Data Analysis Techniques : Introduction to statistical methods and tools for extracting insights from raw data.
- – Machine Learning : Study of algorithms for classification, regression, clustering, and anomaly detection, essential for predictive modeling.
- – Natural Language Processing (NLP) : Techniques for analyzing and processing human language data, including text mining and sentiment analysis.
- – Predictive Analytics : Methods for building models that forecast future trends or behaviors based on historical data.
CAREER OPPORTUNITIES
1. Data Analyst
- – Role Overview : Data Analysts are responsible for collecting, processing, and performing basic analysis on data to extract useful insights that inform business decisions.
- – Key Responsibilities : Data cleaning, analysis, and reporting using tools like Excel, SQL, or Python.
- – Skills Required : SQL, Excel, Python, statistical analysis, data visualization tools (e.g., Tableau, Power BI).
- – Industries : Healthcare, e-commerce, finance, marketing.
2. Data Scientist
- – Role Overview : Data Scientists use advanced statistical and machine learning techniques to analyze complex datasets and build predictive models.
- – Key Responsibilities : Develop and implement algorithms, create data models, and use machine learning to predict trends and outcomes.
- – Skills Required : Python, R, machine learning algorithms, data wrangling, data visualization.
- – Industries : Tech, finance, healthcare, research.
3. Machine Learning Engineer
- – Role Overview : Machine Learning Engineers design and implement machine learning systems that enable machines to learn from data.
- – Key Responsibilities : Build and train machine learning models, deploy algorithms to production systems.
- – Skills Required : Python, TensorFlow, Scikit-learn, deep learning frameworks, data processing.
- – Industries : AI, tech, automotive, healthcare.
4. Data Engineer
- – Role Overview : Data Engineers build and maintain the infrastructure that allows data generation, storage, and processing.
- – Key Responsibilities : Design data pipelines, integrate data from multiple sources, ensure efficient data flow.
- – Skills Required : SQL, Python, ETL tools, cloud platforms, big data technologies.
- – Industries : Cloud computing, finance, healthcare, enterprise IT.
