Subject Syllabus
AD-404 - Data Science
AIDS · Semester 4
Course Modules
Learn topic-wise with Hyper AI and revise using mapped PYQs.
Types of Data: structured and unstructured dataData Science Road Map: Frame the Problem; Understand the Data, Data Wrangling, Exploratory Analysis, Extract Features, Model and Deploy CodeGraphical Summaries of Data: Pie Chart, Bar Graph, Pareto Chart, HistogramMeasures of central tendency of Quantitative Data: Mean, Median, ModeMeasures of Variability of Quantitative Data: Range, Standard Deviation and VarianceProbability: Introduction to Probability, Conditional Probability
Importance of Unstructured DataUnstructured Data Analytics: Descriptive, diagnostic, predictive and prescriptive data Analytics based on Case studyData Visualization: boxplots, histograms, scatterplots, features map visualization, t-SNEOverview of Advance Excel- Introduction, Data validation, Introduction to charts, pivot table, Scenario manager, Protecting data, Excel minor, Introduction to macros
Multiple hypothesis testingParameter Estimation methodsConfidence intervalsCorrelation & Regression analysis, logistic regressionShrinkage Methods, Lasso Regression, Bayesian statisticsL1 and L2 regularizationsPOWERFUL DATA ANALYSIS—SUMIFS, SUMPRODUCT, VLOOKUP | XLOOKUP, INDEX + MATCHHandling Formula Errors, Dynamic Array Formulas, Circular References, Formula Auditing, Pivoting
Introduction to Pandas, understanding DataFrameMissing Values, Data operation, String Manipulation, Regular Expressions and Data learningOutlier and ErrorVisualization tool in Python: Representation of Pie Chart, Bar Chart, Histogram, Scatterplots using PythonData Analysis, performance metrics, ROC curve, types of errorsOverfitting & Under fittingEvaluating performance of learning model: Holdout, Random sampling, cross validation and Bootstrap methodBagging & boosting, Gradient Boosting, Random Forests, Committee Machines