Data Analyst in R

Establish core R programming skills to analyze data by writing basic code, working with vectors, and performing calculations.

Manipulate core R data structures to store, index, and transform analysis-ready data using vectors, lists, matrices, and DataFrames.

Apply control flow, iteration, and functions in R to structure reusable workflows, reduce repetition, and handle complex data logic.

Transform text, dates, and times in R by applying string operations, date-time tools, and functional mapping to support real analysis workflows.

Create clear, insightful data visualizations in R using ggplot2 to explore trends, compare groups, and communicate findings effectively.

Develop practical data cleaning skills in R by reshaping tables, fixing missing values, and preparing relational data for analysis.

Work with regular expressions in R to precisely match, clean, and transform text data as part of advanced, real-world data cleaning workflows.

Develop core SQL skills by writing queries to access, explore, and manipulate data stored in relational databases for common data analysis tasks.

Summarize large datasets by computing statistics, grouping records, and applying SQL aggregate functions to extract meaningful insights.

Combine and analyze data across multiple tables by applying SQL joins and set operators to produce comprehensive, query-ready datasets.

Query SQLite databases from R by executing SQL statements to retrieve, filter, and analyze subsets of data for practical analysis tasks.

Write scalable, advanced SQL queries by nesting subqueries and using common table expressions to solve complex analysis problems.

Analyze data more effectively by using SQL window functions to compute running metrics, rankings, distributions, and offsets within queries.

Acquire data from external APIs in R, handling JSON responses, authentication, and status codes to support real-world analysis workflows.

Collect structured data from websites by scraping and parsing web pages in R to support downstream analysis and insights.

Apply core statistical sampling techniques in R—including random, stratified, and cluster sampling—using hands-on analysis scenarios.

Apply measures of central tendency and variability in R, using means, medians, standard deviation, and z-scores to compare data.

Compare theoretical and experimental probability in R while calculating event likelihoods using permutations, combinations, and real examples.

Apply conditional probability and Bayes’ theorem in R to model dependent events, reason under uncertainty, and build practical Naive Bayes classifiers.

Use hypothesis testing in R to assess real-world data with chi-square tests, probability distributions, and statistical significance.

Apply linear regression in R to build, interpret, and evaluate predictive models, understanding when linear assumptions hold and fail.

Implement core machine learning workflows in R using k-nearest neighbors, error metrics, and cross-validation to build reliable models.

Transform notebooks into interactive Shiny dashboards that let non-technical users explore data through clean interfaces.