The working world has never been more complex than it is today, with cost pressures, customer demands, and competitive markets. None of these challenges can be addressed by "going it alone." Collaboration is key, bringing the organization's best minds together to address these challenges. And key to collaboration is effective listening, ensuring that each member of an organization fully expresses their viewpoints and that these viewpoints are considered by their colleagues. In this course, you'll learn practical skills for drawing out and synthesizing others' viewpoints in service of improving your individual performance. You'll also explore bringing out the best in others and creating the best possible solutions to the challenges faced by today's organizations.

Classification models are used to categorize data into a fixed number of discrete classes or categories. The KNIME Analytics Platform allows you to load, explore, pre-process, and use your data to train classification models with little to no code. In this course, explore classification models and the metrics used to evaluate their performance. Next, construct a KNIME workflow to load and view the data for a classification model. You will clean data, impute missing values, and cap and floor outlier values in a range. Then you will identify and filter correlated variables and you will convert categorical data to numeric values and express numeric variables. Finally, train several different classification models on the training data, evaluate them using the test data, and select the best model using hyperparameter tuning. Upon completing this course, you will have the skills and knowledge to train, clean, and process your data and to use that data to train classification models and perform hyperparameter tuning.

Clustering is an unsupervised learning technique that finds logical groupings or clusters in your data, for example, identifying what social network users have the same interests and background. In this course, explore how clustering models seek to find logical groupings in your data. Next, construct a KNIME workflow to load and explore data for a clustering model. Then, fill in missing values using different imputation techniques, identify highly correlated variables, and deal with outliers. Fit a k-means clustering model on your data, identify clusters, and use scatter plots to visualize the clusters in your data. Finally, perform dimensionality reduction using principal component analysis (PCA) and use the silhouette score to evaluate the number of clusters that gives you the best clustering for your data. Upon course completion, you will be able to fit and evaluate clustering models on your data and visualize clusters using 2-D and 3-D visualizations.

Regression analysis is used to predict continuous data values. The KNIME Analytics Platform allows you to load, explore, pre-process, and use data to train regression models with little to no code. Through this course, learn how to train and evaluate regression models in KNIME. Explore how regression models work and use KNIME nodes to build a workflow to load and comprehend data. Next, discover how to compute correlations and use bar charts, box plots, scatter plots, and pivot tables. Finally, learn how to pre-process flight prediction data using one-hot and label encoding, partition data, and train regression models. After course completion, you'll be able to build a complete workflow in KNIME for regression analysis.

Organizations have been collecting data for analytics and predictive modeling for decades, however, in the past, this analysis has been restricted to engineers and analysts who can write code. The KNIME Analytics Platform makes machine learning and data analytics more accessible by allowing you to build complex workflows with little to no code. Through this course, learn how the KNIME platform works. Examine the role of the KNIME Analytics Platform and the KNIME Community Hub. Next, explore machine learning basics and how supervised and unsupervised learning techniques work. Finally, discover how to set up the KNIME Analytics Platform and get familiar with the KNIME user interface. Upon completion, you'll be able to handle building machine learning workflows using KNIME.

Organizations use time series analysis and market basket analysis to understand patterns over time. Time series analysis uses data collected over regular intervals to analyze how the variable changes over time, while market basket analysis is an application of association rule learning that tries to learn what items occur together frequently in the same transaction. In this course, discover how time series analysis works and how time series models like the autoregressive integrated moving average (ARIMA) model can help you forecast future values of time-varying data using historical values. Next, visualize time series data using moving averages and time series decomposition and fit an ARIMA model on this data for forecasting future values. Finally, use association rule learning for market basket analysis to analyze transaction data from a bakery and perform association rule learning on this data to figure out what items are frequently bought together. Upon course completion, you will be able to confidently use KNIME for time series analysis and market basket analysis.