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DS-U5-Objective

Objective of Unit 5: Data Analytics and Model Evaluation

The objective of Unit 5: Data Analytics and Model Evaluation is to provide students with comprehensive knowledge and practical skills in advanced data analytics techniques, model evaluation methods, and various applications in different domains such as text analysis, social network analysis, and business analysis. This unit aims to equip students with the ability to:

  1. Understand and Implement Clustering Algorithms:

    • K-Means Clustering: Learn how to partition data into distinct groups based on feature similarity, optimizing the within-cluster variance.
    • Hierarchical Clustering: Explore methods to create a hierarchy of clusters using agglomerative or divisive strategies.
    • Clustering in Time-Series Analysis: Apply clustering techniques to time-series data to identify patterns or trends over time.

  2. Introduction to Text Analysis:

    • Text-Preprocessing: Learn the steps involved in preparing text data for analysis, such as tokenization, stemming, lemmatization, and removing stop words.
    • Bag of Words (BoW): Understand the BoW model to represent text data as a collection of word frequencies.
    • TF-IDF (Term Frequency-Inverse Document Frequency): Learn how to weight words in text data to reflect their importance across documents.
    • Topic Modeling: Introduce methods to discover abstract topics within a collection of documents.

  3. Need and Introduction to Social Network Analysis:

    • Understanding Social Networks: Learn the basics of analyzing social networks to uncover relationships and influence patterns within a network.
    • Applications: Explore practical applications of social network analysis in various fields such as marketing, sociology, and information dissemination.

  4. Introduction to Business Analysis:

    • Analytical Techniques: Understand the importance of data analytics in business decision-making and strategy development.
    • Use Cases: Explore real-world examples of how businesses leverage data analytics for competitive advantage.

  5. Model Evaluation and Selection:

    • Metrics for Evaluating Classifier Performance: Learn different metrics such as accuracy, precision, recall, F1-score, and their use cases.
    • Holdout Method and Random Subsampling: Understand techniques for splitting data into training and testing sets to validate model performance.
    • Parameter Tuning and Optimization: Explore methods to fine-tune model parameters to improve performance.
    • Result Interpretation: Gain skills in interpreting the results of various models to make informed decisions.

  6. Practical Implementation:

    • Using Scikit-learn for Clustering and Time-Series Analysis: Apply clustering algorithms and analyze time-series data using Scikit-learn library in Python.
    • Evaluation Tools in Sklearn.metrics: Utilize tools like confusion matrix, AUC-ROC curves, and elbow plot for model evaluation and selection.
    • Confusion Matrix: Learn to visualize and interpret the performance of classification models.
    • AUC-ROC Curves: Understand the trade-offs between true positive rate and false positive rate to evaluate classifiers.
    • Elbow Plot: Use the elbow method to determine the optimal number of clusters in K-Means clustering.

By the end of this unit, students should be proficient in applying advanced data analytics techniques, evaluating and selecting models based on various metrics, and leveraging these skills in practical scenarios across different domains.