Category: Root

Statistics for Data Analytics and Machine Learning Projects

•Null Hypothesis •[2] •Paired t-test •Unpaired t-test •Pearson Correlation •One Way: Analysis of variance •Spearman Correlation •Spearman •Kendal Tau Coef •Wilcoxon Sum test •Basic EDA •Mcnaimer’s test •Friedman test •Kruskal-Wallis Test •Two Way Analysis of variance •K-Fold Cross Validation paired t-test •Wilcoxon Signed Rank Test Data Analytics, Machine Learning, Data Science

Make Sense of your Data: For Data Analytics Project

Hypothesis-based versus data-driven analysis “Only those data analysts who are given time to explore and analyze data thoughtfully and thoroughly are consistently successful.” Data Identification and Prioritization Use Augmented data besides Data Pipeline Analytics Sandbox Characterizing the Data—Exploring a Single Variable Data: Descriptive analysis options Find: Distribution of quantitative variables Reference: [1]. Gregory S. Nelson. …

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Factors/Variables to Consider For Experimental Design for Data Analytics Projects

Design of experiments fishbone REF: [1]. Gregory S. Nelson. The Analytics Lifecycle Toolkit: A Practical Guide for an Effective  Analytics Capability,  John Wiley & Sons © 2018 . Chapter 6 – Problem Framing Data Analytics, Machine Learning Data Analytics, Machine Learning, Data Science

Data Analytics Project: Problem Framing and Project Lifecycle

REF: Internet and Gregory S. Nelson. The Analytics Lifecycle Toolkit: A Practical Guide fo an Effective  Analytics Capability,  John Wiley & Sons © 2018 . Chapter 6 – Problem Framing Data Analytics, Machine Learning Data Analytics, Machine Learning, Data Science

Model Selection

• Optimizations/Machine Learning/Data Mining/Deep Learning/Reinforcement Learning/Graph Mining/NLP/Genetic Algorithms • Regression • Linear • Non-Linear • Classifications • Logistics Regression • Sigmoid : Binary • Softmax: Multi-Class • Bayes Classifier • SVM • Bayesian: Regression/Classification • Clustering • K-NN • KNN+ • Kmeans, Hierarchical, Density •Machine Learning/Data Mining/Deep Learning/Reinforcement Learning/Graph Mining/NLP •Time Series Analysis •Decision (Regression, …

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Model Selection for your Project

Potential Models • Statistical Models • Parametric and Non-Parametric • Mathematical Model (Optimization) • Machine Learning • Data Mining • Deep Learning • Reinforcement Learning • Graph Mining • NLP • Optimization • Genetic Algorithm •Association •Basket Association •Apriori Algorithm •Supervised •Classification •Regression •Unsupervised •Clustering/Customer Segmentation •Reinforcement •Learn a policy (interactively) •Game Playing •Robot in …

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Possible Data Analytics Project Goals

• Examine relations • Test Hypothesis • Validate • Find groups/classes/rules • Learn a policy • Maximize Reward interactively • Predict (Class or Value) • Forecast (numeric, sales) • Compare • Classify • Cluster Data Analytics, Machine Learning, Data Science

Experimental Design Examples (Data Analytics Projects)

Data Analytics, Machine Learning, Data Science

Evaluating Your Data Analytics Project Outcome

Regression Projects • R Square, Goodness of fit • RMSE Classification Projects • Confusion Matrix • ROC • Accuracy, Recall, Precision RL – Reinforcement • Reward – Cumulative Data Analytics, Machine Learning, Data Science

Dimensionality Reduction

Some Approaches •Feature Selection •Feature Extraction •PCA •SVD •LDA Data Analytics, Machine Learning, Data Science