Syllabus Foundations of Data Science - (CS3352) UNIT I INTRODUCTION Data Science : Benefits and uses - facets of data - Data Science Process: Overview - Defining research goals - Retrieving data - Data preparation - Exploratory Data analysis - build the model - presenting findings and building applications - Data Mining - Data Warehousing - Basic Statistical descriptions of Data. (Chapter - 1) UNIT II DESCRIBING DATA Types of Data - Types of Variables -Describing Data with Tables and Graphs - Describing Data with Averages - Describing Variability - Normal Distributions and Standard (z) Scores. (Chapter - 2) UNIT III DESCRIBING RELATIONSHIPS Correlation - Scatter plots - correlation coefficient for quantitative data - computational formula for correlation coefficient - Regression - regression line - least squares regression line - Standard error of estimate - interpretation of r2 - multiple regression equations - regression towards the mean. (Chapter - 3) UNIT IV PYTHON LIBRARIES FOR DATA WRANGLING Basics of Numpy arrays - aggregations - computations on arrays - comparisons, masks, boolean logic - fancy indexing - structured arrays - Data manipulation with Pandas - data indexing and selection - operating on data - missing data - Hierarchical indexing - combining datasets - aggregation and grouping - pivot tables. (Chapter - 4) UNIT V DATA VISUALIZATION Importing Matplotlib - Line plots - Scatter plots - visualizing errors - density and contour plots - Histograms - legends - colors - subplots - text and annotation - customization - three dimensional plotting - Geographic Data with Basemap - Visualization with Seaborn. (Chapter - 5)
