The Course in Applied Mathematics and Statistics for Data Analysis (using R) It offers you the opportunity to enter a booming sector where the ability to interpret and analyse data is essential. In a world driven by information, companies are looking for skilled professionals who can turn data into effective strategies. On this course, you will develop skills in basic statistics and data analysis, gain a deeper understanding of regression and classification models, and explore cluster analysis and time series analysis. Learning to use R, a powerful statistical software programme, will enable you to stand out in a highly competitive job market. Online learning gives you the flexibility to learn at your own pace, ensuring that you develop valuable skills that will set you apart in the professional world. Join our programme and become an expert in data interpretation.
Applied Mathematics and Statistics for Data Analysis (using R)
Big Data
40 hours
Introduction
Objectives
- To understand the basic concepts of statistics as applied to data analysis.
- Use R to carry out descriptive analysis and data visualisation.
- Apply regression models to predict and analyse variables.
- Implement classification models to categorise data efficiently.
- Carry out cluster analysis to identify patterns in the data.
- To explore and analyse time series in order to predict future trends.
- Interpret statistical results to support data-driven decision-making.
Table of Contents
TEACHING UNIT 1. BASIC STATISTICS AND DATA ANALYSIS
Probability distributions
Hypothesis testing and confidence intervals
Data preparation and descriptive analysis
Analysis of missing values and outliers
Case study: Data pre-processing and statistical inference
TEACHING UNIT 2. REGRESSION MODELS
Simple linear regression
Multiple linear regression
Generalised linear models (GLM)
Regression trees
Case study: Building and evaluating regression models
TEACHING UNIT 3. CLASSIFICATION MODELS
Binary logistic regression
Multinomial logistic regression
Classification trees
Random forest
Case study: Building and evaluating classification models
TEACHING UNIT 4. ANALYSIS OF CLUSTERS AND TIME SERIES
Principal component analysis (PCA)
Classification: Discriminant analysis
Classification: K-means
Time series: smoothing methods and time series decomposition
Time series: forecasting methods
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