OTHM Level 7 Diploma in Data Science

Here are the learning outcomes for each of the study units:

Data Science Foundations

1. Understanding Data Science: Gain a foundational understanding of data science principles, concepts, and techniques, including data collection, storage, processing, and analysis.
2. Data Exploration: Learn how to explore and manipulate datasets using programming languages and tools commonly used in data science, such as Python or R.
3. Data Preprocessing: Develop skills in data preprocessing techniques, including data cleaning, transformation, and normalization, to prepare datasets for analysis and modeling.
4. Exploratory Data Analysis (EDA): Apply exploratory data analysis techniques to uncover patterns, trends, and insights within datasets and identify potential relationships between variables.

Probability and Statistics for Data Analysis

1. Probability Theory: Understand the fundamentals of probability theory and its applications in data analysis, including probability distributions, random variables, and probability density functions.
2. Statistical Inference: Learn how to make inferences and draw conclusions from data using statistical methods, such as hypothesis testing, confidence intervals, and regression analysis.
3. Descriptive Statistics: Develop proficiency in descriptive statistics techniques, including measures of central tendency, variability, and correlation, to summarize and interpret data distributions.
4. Probability Distributions: Explore common probability distributions used in data analysis, such as the normal distribution, binomial distribution, and Poisson distribution, and understand their properties and applications.

Advanced Predictive Modelling

1. Predictive Modelling Techniques: Explore advanced predictive modelling techniques, including linear regression, logistic regression, decision trees, and ensemble methods, to build predictive models from data.
2. Model Evaluation: Learn how to evaluate the performance of predictive models using metrics such as accuracy, precision, recall, and area under the curve (AUC).
3. Model Selection and Tuning: Understand the process of model selection and hyperparameter tuning to optimize the performance of predictive models and prevent overfitting.
4. Feature Engineering: Develop skills in feature engineering techniques, including feature selection, transformation, and creation, to improve the predictive power of models and enhance their interpretability.

Data Analysis and Visualization

1. Data Visualization Principles: Learn the principles of effective data visualization and how to create clear, informative visualizations using tools such as matplotlib, seaborn, or ggplot2.
2. Exploratory Data Visualization: Use exploratory data visualization techniques to gain insights into data distributions, patterns, and relationships, and identify outliers or anomalies.
3. Interactive Visualization: Explore interactive data visualization tools and techniques to create dynamic and engaging visualizations that enable users to explore and interact with data.
4. Storytelling with Data: Develop skills in storytelling with data, including how to effectively communicate insights and findings through compelling visual narratives.

Data Mining, Machine Learning and Artificial Intelligence

1. Data Mining Techniques: Explore data mining techniques, such as clustering, association rule mining, and anomaly detection, to discover hidden patterns, trends, and insights within large datasets.
2. Supervised Learning: Understand supervised learning algorithms, including classification and regression algorithms, and how to train and evaluate predictive models using labeled data.
3. Unsupervised Learning: Learn unsupervised learning algorithms, such as clustering and dimensionality reduction techniques, and how to apply them to identify hidden structures and relationships within data.
4. Deep Learning and Neural Networks: Gain an introduction to deep learning and neural network models and their applications in solving complex problems, such as image recognition, natural language processing, and time series forecasting.

Advanced Computing Research Methods

1. Research Design: Develop research designs for conducting advanced computing research, including experimental, quasi-experimental, and observational study designs.
2. Data Collection and Management: Learn how to collect, store, and manage data for advanced computing research projects, including considerations for data quality, security, and privacy.
3. Data Analysis Techniques: Explore advanced data analysis techniques, including statistical methods, machine learning algorithms, and computational simulations, to analyze and interpret research data.
4. Research Ethics: Understand the ethical considerations and guidelines for conducting research in advanced computing, including informed consent, data anonymization, and protection of human subjects.

These learning outcomes equip students with the knowledge, skills, and competencies needed to succeed in the field of data science and conduct advanced research projects in computing.