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Course Outline
Week 1
Introduction to Python and R
? Python: Installing Anaconda, Jupyter Notebooks, Python syntax, variables, data types. ? R: Installing R & RStudio, R syntax, variables, data types.
? Data types (vectors, lists, matrices, data frames).
Week 2
Data Structures
? Python: Lists, tuples, dictionaries, sets. ? R: Vectors, lists, matrices, data frames. ? Vectors and Lists
o Creating and manipulating vectors
o Lists: what they are and how to use them ? Matrices and Data Frames
o Creating and modifying matrices
o Data frames: importing, exporting, and manipulating data ? Hands-on Exercises
o Create and modify different data structures Week 3
Control Flow
? Python: if, else, elif, for, while, comprehensions.
? R: if, else, for, while, apply, lapply.
? Data Manipulation
• Introduction to dplyr
o Selecting, filtering, and arranging data o Mutating and summarizing data
• Data Cleaning Techniques
o Handling missing values
o Data type conversions • Hands-on Exercises
o Use dplyr to manipulate sample datasets Week 4
Functions and Packages
? Python: Defining functions, *args, **kwargs, importing modules, pip.
? R: Creating functions, default arguments, installing and loading packages
(install.packages(), library()).
? Data Visualization
• Introduction to ggplot2
o Basics of creating plots
o Customizing plots (colors, labels, themes) • Advanced Visualization Techniques
o Faceting, geom layers, and saving plots • Hands-on Exercises
o Create various plots based on a dataset Week 5
Working with Data
? Python: Reading/writing CSV/Excel/JSON using pandas.
? R: Reading/writing CSV/Excel/JSON using readr, readxl, jsonlite.
• Importing Data
o Reading CSV, Excel, and other formats
• Web Scraping Basics
o Using rvest to scrape data from websites
• Hands-on Exercises
o Import and clean datasets from various sources
Week 6
Data Manipulation and Programming Concepts
? Python: pandas basics: DataFrame, indexing, filtering, grouping.
? R: dplyr: select(), filter(), mutate(), group_by(), summarize().
• Control Structures
o Conditional statements and loops
• Functions
o Creating and using custom functions
• Hands-on Exercises
o Write functions to automate tasks and manipulate dat
Week 7
Data Cleaning
? Python: Handling missing values, data type conversion, string operations. ? R: tidyr, handling NA, type coercion, string manipulation with stringr.
Week 8
Data Visualization
? Python: matplotlib, seaborn, basic plots, customization. ? R: ggplot2, basic grammar of graphics, customization.
Week 9
Working with Dates & Times
? Python: datetime, pandas time series, resampling. ? R: lubridate, date operations, time series indexing.
Week 10
Exploratory Data Analysis (EDA)
? Python: Using pandas_profiling, seaborn for correlation heatmaps, boxplots. ? R: Using skimr, DataExplorer, corrplot, boxplots with ggplot2.
Week 11
Intro to Statistics
? Python: Descriptive statistics with scipy, probability distributions.
? R: Base R + stats package for descriptive stats, distribution functions.
Week 12
Linear Regression
? Python: scikit-learn regression models.
? R: lm() function, interpreting coefficients, residual plots.
Week 13 Classification Models
? Python: Logistic regression, decision trees with scikit-learn. ? R: glm() for logistic regression, rpart for decision trees.
Week 14 Capstone Project
? Use either Python or R (or both) to complete a mini project involving: o Data collection
o Cleaning & manipulation o Visualization
o Modeling
? Week 15: Student Presentations and Course Synthesis |