Pre-Requisite(s): Basic Probability
Course Type: Lab

Course Objectives

• Statistical foundations of data science.
• Techniques to pre-process raw data; (data wrangling, munging) with Numpy, Pandas and other Python statistical packages; visualization with Matplotlb, Plotly and Bokeh; EDA; statistical inferences
• Predictions using statistical tests
• Estimation of statistical parameters
• Introduction to Time Series.

Course Outcomes
CO1: Understand the statistical foundations of data science.
CO2: Apply pre-processing techniques over raw data, conduct exploratory data analysis, create insightful visualizations, and identify patterns to enable further analysis
CO3: Identify machine learning algorithms for prediction/classification and to derive insights
CO4: Analyze the degree of certainty of predictions using statistical test and models.
CO5: Explore the statistical foundations of time series, and employ basic ARIMA models for time series prediction.

CO-PO Mapping