Data-Science-Kurse

Step into the role of CFO and learn how to advise a board of directors on key metrics while building a financial forecast.

Learn the basics of cash flow valuation, work with human mortality data and build life insurance products in 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.

Make it easy to visualize, explore, and impute missing data with naniar, a tidyverse friendly approach to missing data.

Develop the skills you need to clean raw data and transform it into accurate insights.

Learn how to use Python to analyze customer churn and build a model to predict it.

In this course youll learn how to leverage statistical techniques for working with categorical data.

Learn how to load, transform, and transcribe speech from raw audio files in Python.

Learn to write scripts that will catch and handle errors and control for multiple operations happening at once.

In ecommerce, increasing sales and reducing costs are key. Analyze data from an online pet supply company using Power BI.

Prepare for your next statistics interview by reviewing concepts like conditional probabilities, A/B testing, the bias-variance tradeoff, and more.

From customer lifetime value, predicting churn to segmentation - learn and implement Machine Learning use cases for Marketing in Python.

In this course, you’ll learn to classify, treat and analyze time series; an absolute must, if you’re serious about stepping up as an analytics professional.

Apply statistical modeling in a real-life setting using logistic regression and decision trees to model credit risk.

Learn how bonds work and how to price them and assess some of their risks using the numpy and numpy-financial packages.

Learn how to design and implement triggers in SQL Server using real-world examples.

Leverage tidyr and purrr packages in the tidyverse to generate, explore, and evaluate machine learning models.

Gain the essential skills using Scikit-learn, SHAP, and LIME to test and build transparent, trustworthy, and accountable AI systems.

In this course youll learn how to perform inference using linear models.

Learn how to use plotly in R to create interactive data visualizations to enhance your data storytelling.

Learn how to build a model to automatically classify items in a school budget.

Learn how to create interactive data visualizations, including building and connecting widgets using Bokeh!

Learn how to pull character strings apart, put them back together and use the stringr package.

Learn to analyze financial statements using Python. Compute ratios, assess financial health, handle missing values, and present your analysis.

Work with risk-factor return series, study their empirical properties, and make estimates of value-at-risk.

Learn how to visualize time series in R, then practice with a stock-picking case study.

Learn tools and techniques to leverage your own big data to facilitate positive experiences for your users.

Dive into the world of digital transformation and equip yourself to be an agent of change in a rapidly evolving digital landscape.

Explore latent variables, such as personality, using exploratory and confirmatory factor analyses.

Learn efficient techniques in pandas to optimize your Python code.