Data Analytics with R & Python

Understanding the data lifecycle and setting up R and Python.

• What is Data Analytics? The Data Lifecycle.
• Python vs. R: Strengths and weaknesses.
• Setting up Anaconda (for Python) and RStudio (for R).
• Introduction to Jupyter Notebooks.

Data manipulation with Pandas.

• Python basics (Lists, Dictionaries, Functions).
• Introduction to NumPy arrays.
• Pandas Series and DataFrames.
• Reading data (CSV, Excel), selecting, and filtering.

Cleaning data with Pandas and plotting with Matplotlib/Seaborn.

• Handling missing values (dropna, fillna).
• Grouping data with `groupby`.
• Data visualization with Matplotlib (basic plots).
• Statistical plotting with Seaborn.

Data manipulation with R and Tidyverse.

• R basics (Data types, Vectors, Data Frames).
• Introduction to the Tidyverse.
• Data manipulation with `dplyr` (select, filter, mutate, arrange).
• Summarizing data with `summarise` and `group_by`.

Tidying data and plotting with ggplot2.

• Cleaning data with `tidyr` (pivot_longer, pivot_wider).
• Introduction to `ggplot2` (Grammar of Graphics).
• Building plots layer-by-layer (geoms, aesthetics, themes).
• Creating common plots (bar, scatter, boxplot).

Extracting data from relational databases.

• SQL basics (`SELECT`, `FROM`, `WHERE`, `ORDER BY`).
• Aggregate functions (`GROUP BY`, `COUNT`, `SUM`, `AVG`).
• Joining tables (`INNER JOIN`, `LEFT JOIN`).
• Connecting to databases from R and Python.

Performing statistical tests and modeling.

• Descriptive vs. Inferential Statistics.
• Hypothesis Testing (t-tests, ANOVA).
• Correlation and Regression.
• Introduction to Linear Models (`lm()`).

Building predictive models with Scikit-learn.

• Supervised vs. Unsupervised learning.
• The Scikit-learn API.
• Model: Linear Regression.
• Model: Classification (e.g., Logistic Regression, k-NN).

Building predictive models with the Caret package.

• Train/Test splits.
• Introduction to the `caret` package.
• Model: Linear Regression (revisited with `caret`).
• Model: Classification (e.g., Random Forest).

End-to-end analysis project (student's choice of R or Python).

• Project: Choose a dataset for a full analysis.
• Data cleaning and preparation.
• Exploratory Data Analysis (EDA) and visualization.
• Building a simple predictive model.
• Final presentation of findings.