Week 1: Advanced R programming for Data Science: Introduction and Background
Fundamentals of R: Installation and set-up, set working directory, packages, and libraries; R operators: Arithmetic, assignment, comparison, and logical operators; Working with different data types; Vector creation and manipulation; Miscellaneous functions: Sequence, repetition, sorting, generate random numbers, user-defined functions, lapply, sapply, and tapply function; Factor variables, Indexing, Data coercion, conditional statements
Week 2: Introduction to Data Visualization with R:
Basic Plotting types: Barchart, Pie Chart, Histogram, Density plot, Boxplot; Plot customization: Adding legend, Adding color in plots, Adding axis labels and chart title, Modifying axis and scales; Overlay plots in R
Week 3: Advanced Data Visualization with ggplot2:
Key components; Color, size, shape, and other aesthetic attributes; Faceting: Wrap faceting and Grid faceting;Plot geoms: Adding a smoother to a plot, Boxplots, jitterplots, histogram, frequency polygons,Time series with line and path plots; Modifying the axes; Quick plots; Correlation matrix with ggplot.
Week 4: Exploratory Data Analysis (EDA) and Data Wrangling:
Reading and writing the data, exporting, and saving a dataframe; Data handling and cleaning:
Recording the variables, dealing with NAs, adding a row and column to the dataframe, wide to long data formats, merging the dataframes.
Week 5: Handling Complex Date and Time Objects:
Getting the current date and time, POSIX classes (POSIXct and POSIXlt), Parsing dates, Date and time components, Dates not in Standard Format; Operations on dates: subtract/add, finding difference, generating a sequence, truncate; Time zones; Time intervals: Interval and overlaps;
Periods and durations; Date arithmetic; Rounding the dates
Week 6: Basic Statistics with R:
Measures of central tendency, Measures of Variability, Measures of Shape; summary statistics by group; Dealing with outliers: Truncate and Winsorize.
Week 7: Probability and Stochastics with R:
Probability Distribution, Binomial Distribution, Normal Distribution, Sampling Distribution, Types of Sampling: Probability vs non-Probability
Week 8: Advanced Inferential Statistics with R:
One-sample test, two-sample test, T statistics, Z statistics, Test with Proportion, Test with variances; ANOVA: one way and two ways
Week 9: Introduction to Model Building and Evaluation: Simple and Multiple Linear Regression Modeling (SLRM):
Linearity and normality, Fitting SLRM, Storing and printing the regression results, Interpretation of the regression results, Diagnosis of the fitted model, Tests for autocorrelation and heteroscedasticity, Computation of robust standard errors, and Visualization of regression results
Week 10: Introduction to Time-series Modelling and Panel Data Methods
Time-series modelling, issues with time-series data, basic time-series properties, Introduction to pandel data, Reading & Writing Panel Data, Panel Data Manipulation, Outlier Treatment, Panel Data Visualization, Descriptive Statistics Pooled OLS, Fixed Effect Estimation, LSDV Estimation, Random Effect Estimation, Diagnostic Tests, Residual Analysis, Robust Estimation
Week 11: Advanced Non-Linear Modelling and Evaluation: Quantile Regression Method
Reading & Writing Quantile Data, Quantile Data Manipulation, Outlier Treatment, Quantile Data Visualization, Diagnostic Tests, Residual Analysis, Robust Estimation
Week 12: Advanced Classification Methods: Logit/Probit Regression Modelling
Introduction to Classification Algorithms, Linear probability models, Introduction to Logit/Probit Modelling, Thresholding and Classification Matrix, ROC Curve, Parameter Interpretation, Maximum Likelihood Estimation, and Goodness-of-Fit measures.
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