In this course, learners will prepare data for, implement, and optimize three advanced clustering models in Python while comparing their different use cases. In particular, this course focuses on the suitability of different clustering methods for different kinds of data: numerical, categorical, and mixed. Learners will distinguish between K-modes, mean-shift, and K-prototypes models, developing their understanding of when each model will best meet their needs.

Learn how to apply seasonal analysis and ARIMA models and how to decompose and identify seasonal and non- seasonal factors all while learning the nuances of building sophisticated time series models.

This course covers the clustering concepts of natural language processing, equipping learners with the ability to cluster text data into groups and topics by finding similarities between different documents.

Data is often messy, requiring cleaning and restructuring before it can be reliably used in a program or project. In this course, learners will augment their understanding of Python using two of the most popular libraries for data cleaning and wrangling, NumPy and Pandas. First, learners will practice working with NumPy objects, transforming data into efficient arrays for ease of analysis. Then, learners will clean and structure arrays into readable tabular DataFrames using Pandas, allowing them to profile a dataset for key answers and values.

Decision tree models are classification algorithms that sort novel data into categories based on iterative splitting, like the branches of a tree, according to input parameters. In this course, learners will identify use cases for decision trees in Python. They will wrangle data and implement a decision tree model before attempting to evaluate its effectiveness. Finally, learners will use their knowledge of the mathematics behind decision trees to tune the model and improve its classificatory function.

This course covers an overview of ensemble learning methods like random forest and boosting. At the end of this course, students will be able to implement and compare random forest algorithm and boosting.

This course helps students to identify the most impactful features for your model. It will build upon foundational machine learning techniques to hone predictive skills and discover critical danger points in patterns. By the end of this course, students will be able to determine key features in models.

Plotly is a powerful open-source graphic library for Python that allows users to create interactive visualizations. In this course, learners will discuss the advantages of adding additional layers of data to a visualization through dynamic elements. Learners will then learn how to connect Plotly to the data transformation library Pandas using Cufflinks. Finally, learners will generate interactive bar charts, box plots, scatter plots, and other commonly used formats.

Plotly is a powerful open-source graphic library for Python that allows users to create interactive visualizations. In this course, learners will discuss the advantages of adding additional layers of data to a visualization through dynamic elements. Learners will then learn how to connect Plotly to the data transformation library Pandas using Cufflinks. Finally, learners will generate interactive bar charts, box plots, scatter plots, and other commonly used formats.

In this course, learners will encounter more sophisticated methods for generating clusters within unlabeled data using Python. The first method, hierarchical clustering, creates tree branch-based clusters in order of increasing specificity. The second, density-based clustering, creates groups based on the concentration of data points within a region. By the end of this course, learners will prepare data for, implement, and optimize these models, and compare their relative advantages.

This is an intermediate network analytics course. By the end of this course, students will be able to quantitatively measure and visualize network nodes with scores. They will also summarize their findings by testing their network resilience.

Detecting outlier data points are powerful machine learning techniques. This course covers how techniques like Local Outlier Factor and Isolation Forest play a role in anomaly and outlier detection. By the end of the course, students will learn to implement these techniques to identify anomalous data points.

This course is designed for learners who would like to learn about statistics and apply it for decision-making. This course is a comprehensive review of intermediate statistics topics like t-values, t-distributions, chi-square distributions, f-statistic, and f-distributions that enable us to compare observed and expected frequencies objectively.

Classification is a machine learning technique that can be used to sort novel data into labeled categories. In this course, learners will identify use cases for classification algorithms and become familiar with the theoretical underpinnings of supervised machine learning (working with labeled data). In particular, learners will build, evaluate, and interpret a k-nearest neighbors model in Python, based on one of the most commonly used classification algorithms.

Clustering is a machine learning technique that can be used to group unlabeled data based on shared features. In this course, learners will identify use cases for clustering algorithms and become familiar with the theoretical underpinnings of unsupervised machine learning (working with unlabeled data). In particular, learners will build, evaluate, and interpret a K-means model in Python, based on one of the most commonly used clustering algorithms.

This course introduces the concepts of networks, network graphs and network types. By the end of this course, students will be able to understand and visualize the connections between network nodes.

This course covers the basics of Support Vector machine algorithm. It helps students implement and optimize the model for a dataset.

Detecting outlier data points are powerful machine learning techniques. This class will build upon foundational machine learning techniques to discover critical danger points in patterns. By the end of this course, students will use techniques like DBSCAN and SMOTE to identify anomalous data points.

This course is designed for learners who would like to learn about statistics and apply it for decision-making. This course is a comprehensive review of statistical terms ranging from foundational (mean, median, mode, standard deviation, variance, covariance, correlation) to more complex concepts such as normality in data, confidence intervals, and p-values. Additional topics include how to calculate summary statistics and how to carry out hypothesis testing to inform decisions.

Learn how to apply time series basics and concepts to create accurate forecasts for their organizations and make better decisions when developing strategies.