Data Analysis
Turn raw data into insights. Data wrangling, exploratory analysis, SQL, and visualization techniques.
Most data work involves cleaning, transforming, and exploring data before any modeling begins. This section covers data manipulation techniques in both Python and R, exploratory data analysis approaches, and visualization tools. You'll also learn SQL basics for querying databases.
The Reality of Data Work
Data scientists spend most of their time on data preparation, not modeling. Real datasets have missing values, inconsistent formats, duplicates, and errors. This section teaches you to handle messy data systematically and efficiently.
What You'll Learn
You'll learn to clean and reshape data, handle missing values intelligently, merge datasets from different sources, and create visualizations that reveal patterns. The courses use both Python (Pandas) and R (tidyverse) so you can work in either environment.
Exploratory Analysis
Before building models, you need to understand your data. Exploratory data analysis (EDA) is a systematic approach: examine distributions, check for outliers, look at relationships between variables, and form hypotheses. Good EDA prevents costly mistakes later.
