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Applied Mathematics and Statistics for Data Analysis (with R)

Cross-cutting
Digital Skills

Digital Skills

Duration

40 hours

PRESENTATION

The Applied Mathematics and Statistics for Data Analysis (with R) Course It offers you the opportunity to enter a booming industry where the ability to interpret and analyze data is essential. In a data-driven world, companies are looking for skilled professionals who can transform data into effective strategies. In this course, you will develop skills in basic statistics and data analysis, delve deeper into regression and classification models, and explore cluster analysis and time series analysis. Learning how to use R, a powerful statistical software program, will help you stand out in a highly competitive job market. Online training gives you the flexibility you need to learn at your own pace, ensuring that you develop valuable skills that will set you apart in the professional world. Join our program and become an expert in data interpretation.

Objectives

– Understand the basic concepts of statistics as applied to data analysis.  

– Use R to perform descriptive analysis and data visualization.  

– Apply regression models to predict and analyze variables.  

– Implement classification models to categorize data efficiently.  

– Perform cluster analysis to identify patterns in the data.  

– Explore and analyze time series to predict future trends.  

– Interpret statistical results to support data-driven decision-making.  

Syllabus

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 Preprocessing and Statistical Inference TEACHING UNIT 2. REGRESSION MODELS Simple linear regression Multiple linear regression Generalized 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 LEARNING UNIT 4. CLUSTER ANALYSIS AND TIME SERIES Principal Component Analysis (PCA) Classification: Discriminant Analysis Classification: K-means Time Series: Smoothing and Time Series Decomposition Methods Time Series: Forecasting Methods
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Are the Educa PHAROS courses eligible for credit?

Many courses can be credited toward the master's programs at Structuralia.
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Hours

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Minutes

264

Courses

Educa PHAROS is a next-generation training model that places a company’s human capital at the forefront. Through a platform that adapts to each company’s corporate identity and offers a total of more than 900 courses, it provides tailored training for each organization. The unlimited flat-rate plan provides each company with the number of courses that best suits its needs, as well as the ability to determine which employees will have access.
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