Published 5/2024
Created by Olalekan Agbolade
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 22 Lectures ( 11h 28m ) | Size: 5.64 GB
Your Complete Guide to Data Analysis, Data Manipulation, Statistical Analysis, Machine Learning, and Visualization in R
What you’ll learn:
1. Fundamentals of data science
2. R programming language and its applications in data analysis
3. Practical experience through hands-on exercises and real-world datasets
4. Skills to perform data manipulation, visualization, and modeling using R
5. Handling Hypothesis questions and sample size selection
6. Handling normal distribution assumptions (Normality test, Homogeneity of variance and outliers)
7. Performing statistical analysis with Parametric and Non-Parametric Tests
8. Machine learning covering Supervised and Unsupervised learning
9. Interpretation of results
Requirements:
• Computer Access with download privileges
• Basic Math Skills
Description:
Data science is a multidisciplinary field which has emerged as a critical skillset in today’s digital age with applications across industries and sectors, making it relevant and beneficial for a wide range of individuals, from students and professionals to researchers, entrepreneurs, policymakers, and beyond; enabling them to extract actionable insights from vast amounts of data.Data Science is ranked the number one job on Glassdoor with average salary of over $120,000 in the United States and across the world.To equip our learners with the essential knowledge and skills in data science. This course presents a comprehensive data science teaching class using the R programming language. These lectures aim to provide students with a strong foundation in data science concepts, tools, and techniques through hands-on learning sessions.Here a just a few of the topics we will be learning:R programming languageVariables, data structures and functionsImporting data from CSV, Excel, SPSS, STATA, SAS, databases and SQLData manipulation techniques: subsetting, filtering, merging, and summarisingHandling missing values, outliers and data transformation.Visualizing data using ggplot2 and other R packages.Descriptive statistics and data summarization.Understanding Hypothesis testing, p-values, and confidence intervalsParametric Testing (Chi-Square, Simple T-test, Unpaired sample T-test, Paired sample T-test, One-way ANOVA, One-way Repeated ANOVA and Pearson’s CorrelationNon-Parametric Testing (Wilcoxon Test for one sample, Mann-Whitney U Test, Wilcoxon signed Ranked Test, Kruskal wallis Test, Friedman’s ANOVA, and Spearman’s CorrelationSupervised learning algorithms (Simple and Multiple Linear Regression, Logistic Regression, SVM, Decision Tree, and Artificial Neural Network)Unsupervised learning algorithms (PCA, K-means clustering, and Hierarchical clustering)Model evaluation metrics: accuracy, precision, recall, F1-score.And many more…
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