- 7 Sections
- 26 Lessons
- 100 Weeks
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- An Introduction to DataData science is a field of study that focuses on techniques and algorithms to extract knowledge from data. The area combines data mining and machine learning with data-specific domains. This section focuses on defining "data" before going to any complicated topic.4
- Data Dimensionality and SpaceThis section's focus is on defining the common terminology widely used in data science. The video lectures in this section focus on terms like objects, data points, features, dimensions, vectors, high-dimensional data, and mathematical space.6
- Proximity in Data Science ContextMany data mining and machine learning algorithms rely on distance or similarity between objects/data points. Video lectures in this section focus on standard proximity measures used in data science. The section also explains how to use proximity measures to examine the neighborhood of a given point.5
- Clustering algorithmsA large portion of data science focuses on exploratory analysis. Scientists and practitioners use statistical techniques to understand the data. One way to explore the data is to check if there are clusters of data points. A cluster is a group of data points that have similar features. This section explains the clustering algorithms.8
- 4.1What is clustering?
- 4.2Quiz: introduction to clustering5 Minutes2 Questions
- 4.3A few types of clustering algorithms
- 4.4k-means clustering algorithm
- 4.5Quiz: k-means clustering10 Minutes4 Questions
- 4.6Hierarchical Clustering Algorithm
- 4.7Density-based clustering algorithm: DBSCAN
- 4.8Evaluation of clustering algorithms: Measure the quality of a clustering outcome
- Classification algorithmsA large part of machine learning-driven data science focuses on classification problems. This section explains several classification algorithms. Also, it explains linear regression-based prediction.5
- PageRank algorithmThe PageRank algorithm is a sophisticated method of evaluating the importance of web pages by examining their linking structure. This section describes the history of PageRank algorithm, how it works, and the python implementation of PageRank algorithm.2
- Outlier detectionThis section explains outlier detection mechanisms, such as the use of Boxplot technique.1