Learn what Data Science is and what are the mandatory skill set required to be a Data Scientist. '
• Explain in basic terms what Statistical Inference means. Identify probability distributions
commonly used as foundations for statistical modeling. Fit a model to data.
• Use R to carry out basic statistical modeling and analysis.
• Explain the significance of exploratory data analysis (EDA) in data science. Apply basic tools
(plots, graphs, summary statistics) to carry out EDA.
• Describe the Data Science Process and how its components interact.
• Use APIs and other tools to scrap the Web and collect data.
• Apply EDA and the Data Science process in a case study.
• Apply basic machine learning algorithms (Linear Regression, k-Nearest Neighbors (k-NN),
k-means, Naive Bayes) for predictive modeling. Explain why Linear Regression and k-NN are
poor choices for Filtering Spam. Explain why Naive Bayes is a better alternative.
• Identify common approaches used for Feature Generation. Identify basic Feature Selection
algorithms (Filters, Wrappers, Decision Trees, Random Forests) and use in applications.
• Identify and explain fundamental mathematical and algorithmic ingredients that constitute a
Recommendation Engine (dimensionality reduction, singular value decomposition, principal
component analysis). Build their own recommendation system using existing components.
• Create effective visualization of given data (to communicate or persuade).
• Work effectively (and synergically) in teams on data science projects.
• Reason around ethical and privacy issues in data science conduct and apply ethical practices
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