HR Analytics: Exploring Employee Data in R
Learn how to manipulate, visualize, and perform statistical tests through a series of HR analytics case studies.
Learn how to manipulate, visualize, and perform statistical tests through a series of HR analytics case studies.
Learn survey design using common design structures followed by visualizing and analyzing survey results.
Become an expert in fitting ARIMA (autoregressive integrated moving average) models to time series data using R.
Make it easy to visualize, explore, and impute missing data with naniar, a tidyverse friendly approach to missing data.
Learn how to use tree-based models and ensembles to make classification and regression predictions with tidymodels.
Develop a strong intuition for how hierarchical and k-means clustering work and learn how to apply them to extract insights from your data.
In this course you'll learn techniques for performing statistical inference on numerical data.
Learn the principles of feature engineering for machine learning models and how to implement them using the R tidymodels framework.
Learn dimensionality reduction techniques in R and master feature selection and extraction for your own data and models.
GAMs model relationships in data as nonlinear functions that are highly adaptable to different types of data science problems.
Learn to develop R packages and boost your coding skills. Discover package creation benefits, practice with dev tools, and create a unit conversion package.
Learn about how dates work in R, and explore the world of if statements, loops, and functions using financial examples.
Explore latent variables, such as personality, using exploratory and confirmatory factor analyses.
Learn how to pull character strings apart, put them back together and use the stringr package.
Learn to effectively convey your data with an overview of common charts, alternative visualization types, and perception-driven style enhancements.
This course will introduce the support vector machine (SVM) using an intuitive, visual approach.
Learn to create interactive dashboards with R using the powerful shinydashboard package. Create dynamic and engaging visualizations for your audience.
Learn to read, explore, and manipulate spatial data then use your skills to create informative maps using R.
Apply statistical modeling in a real-life setting using logistic regression and decision trees to model credit risk.
Learn to streamline your machine learning workflows with tidymodels.
Learn the bag of words technique for text mining with R.
Get ready to categorize! In this course, you will work with non-numerical data, such as job titles or survey responses, using the Tidyverse landscape.
In this course you'll learn how to perform inference using linear models.
Gain an overview of all the skills and tools needed to excel in Natural Language Processing in R.
Manage the complexity in your code using object-oriented programming with the S3 and R6 systems.
Learn to work with time-to-event data. The event may be death or finding a job after unemployment. Learn to estimate, visualize, and interpret survival models!
Learn the basics of A/B testing in R, including how to design experiments, analyze data, predict outcomes, and present results through visualizations.
Work with risk-factor return series, study their empirical properties, and make estimates of value-at-risk.
Learn the fundamentals of valuing stocks.
Learn to use the Bioconductor package limma for differential gene expression analysis.
Learn how to create and assess measurement models used to confirm the structure of a scale or questionnaire.
Practice your Shiny skills while building some fun Shiny apps for real-life scenarios!
Learn sentiment analysis by identifying positive and negative language, specific emotional intent and making compelling visualizations.
Leverage the tools in the tidyverse to generate, explore and evaluate machine learning models.
Learn how to access financial data from local files as well as from internet sources.
Specify and fit GARCH models to forecast time-varying volatility and value-at-risk.
In this course you'll learn how to leverage statistical techniques for working with categorical data.
Learn how to tune your model's hyperparameters to get the best predictive results.
Apply your finance and R skills to backtest, analyze, and optimize financial portfolios.
This course covers the basics of financial trading and how to use quantstrat to build signal-based trading strategies.
Learn how to analyse and interpret ChIP-seq data with the help of Bioconductor using a human cancer dataset.
Learn to easily summarize and manipulate lists using the purrr package.
Strengthen your knowledge of the topics covered in Manipulating Time Series in R using real case study data.
Diagnose, visualize and treat missing data with a range of imputation techniques with tips to improve your results.
Learn to analyze, plot, and model multivariate data.
Learn how to identify important drivers of demand, look at seasonal effects, and predict demand for a hierarchy of products from a real world example.
Learn to use R to develop models to evaluate and analyze bonds as well as protect them from interest rate changes.
Learn to detect fraud with analytics in R.
Learn how to visualize time series in R, then practice with a stock-picking case study.
Learn to analyze and model customer choice data in R.
Apply your data manipulation skills to time series data on water levels of the River Thames.
Import, clean, and analyze seven years worth of training data tracked on the Runkeeper app.
Use LLMs to solve diverse language tasks for a car dealership company.
Ready to analyze and visualize financial ratios? In this project, you will take on real-world challenges like evaluating the profitability and leverage of companies across industries.
Test your Data engineering skills by creating a data pipeline to analyze E-commerce business of Walmart!
Build a regression model for a DVD rental firm to predict rental duration. Evaluate models to recommend the best one.
Define functions to catch errors when new users register for an app!
Review a data analysis workflow for adherence to Python standards and best-practices.
Analyze product data for an online sports retail company to optimize revenue.
Use pandas and Bayesian statistics to see if left-handed people actually die earlier than righties.
Analyze clothing reviews on an e-commerce platform to explore different topics and similarities among them.
Use data manipulation and summary statistics to analyze test scores across New York City's public schools!
Reanalyse the data behind one of the most important discoveries of modern medicine: handwashing.
Analyze UEFA Champion's League Soccer Games using Snowflake SQL.
Analyze an A/B test from the popular mobile puzzle game, Cookie Cats.
Use joining techniques to discover the oldest businesses in the world.
Use a variety of data manipulation techniques to explore different aspects of Lego's history!
Use OpenAI's powerful API to answer questions about tourist attractions in Paris.
Rock or rap? Apply machine learning methods in Python to classify songs into genres.
Use DataFrames to read and merge employee data from different sources.
Use categorization and ranking techniques to explore 101 years of American baby name tastes.
Play bank data scientist and use regression discontinuity to see which debts are worth collecting.
Clean customer data and use logistic regression to predict whether people will make a claim on their car insurance!
Explore Disney movie data, then build a linear regression model to predict box office success.
Use your SQL skills to find out how many companies reached a valuation of over $1 billion across different industries between 2019 and 2021!
Use Natural Language Processing on NIPS papers to uncover the trendiest topics in machine learning research.
Automatically generate keywords for a search engine marketing campaign using Python to send website visitors to the right landing page.
Dive into agriculture using supervised machine learning and feature selection to aid farmers in crop cultivation and solve real-world problems.
Use skills gained in the Data Analyst in SQL career track to derive insights about motorcycle part sales over time across multiple warehouse sites!
In this project, we will use data manipulation skills to zoom in on a time when Lego explored a new direction for their toy line!
Leverage machine learning algorithms and models for marketing analytics tasks in a streaming platform.
Automate e-commerce processes with image classification.
Automate e-commerce processes with image classification.
Use OpenAI's powerful API to enrich and summarize stock market data.
Build a machine learning model to predict if a credit card application will get approved.
Find out about the evolution of the Linux operating system by exploring its version control system.
Join us at a leading insurance company, where we'll craft a model to predict customer charges and test it with new client data
Manipulate date and time using Python
Create custom Python functions to validate user input!
Manipulate and plot time series data from Google Trends to analyze changes in search interest over time.
Identify issues in a manufacturing process using SQL window functions and subqueries
Tidy a bank marketing campaign dataset by splitting it into subsets, updating values, converting data types, and storing it as multiple csv files.
Scrape news headlines for FB and TSLA then apply sentiment analysis to generate investment insight.
Use PySpark to build an e-commerce forecasting model!
Dive into sleep data and gain insights about factors that impact sleep quality
Apply the foundational Python skills you learned in Introduction to Python and Intermediate Python by manipulating and visualizing movie data.
Use coding best practices and functions to improve a script!
Sometimes, things that once worked perfectly suddenly hit a snag. Practice your knowledge of DataFrames to find the problem and fix it!
Automatically generate keywords for a search engine marketing campaign using Python.
Arctic Penguin Exploration: Unraveling Clusters in the Icy Domain with K-means Clustering