Official NUC CCMAS syllabus
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Start studying GET 305 free →Descriptive statistics, frequency distribution, populations and sample, central tendency, variance data sampling, mean, median, mode, mean deviation and percentiles. Probability. Binomial, Poisson hyper-geometric and normal distributions. Statistical inference intervals, test hypothesis and significance. Regression and correlation. Introduction to big data analytics and cloud computing applications. Introduction to the R language; R as a calculator; Vectors, matrices, factors, data frames and other R collections. Iteration and looping control structures. Conditionals and other controls. Designing, using and extending functions. The Apply Family. Statistical modelling and inference in R. GET 307: Introduction to Artificial Intelligence, Machine Learning and Convergent Technologies (3 Units C: LH 45) Learning Outcomes At the completion of the course, the students are expected to be able: 1. explain the meaning, purpose, scope, stages, applications and effects of artificial intelligence; 2. explain the fundamental concepts of machine learning, deep learning and convergent technologies; 3. demonstrate the difference between supervised, semi-supervised and unsupervised learning; 4. demonstrate proficiency in machine learning workflow and how to implement the steps effectively; 5. explain natural languages, knowledge representation, expert systems and pattern recognition; 6. describe
Source: the Nigerian Universities Commission (NUC) Core Curriculum and Minimum Academic Standards (CCMAS). StudyOps loads this syllabus automatically when you study GET 305.