Data Analyst

Run the whole investigation, then make people act on it

Walks the full investigative loop on real, untidy datasets — pose a question, gather and clean the data, explore it, then turn the result into a visual story an audience can act on. Each project lets you choose the dataset, so the work doubles as portfolio material.

What you will learn

→Wrangle messy, real-world data into analysis-ready shape with pandas and NumPy
→Run an exploratory analysis to surface patterns and pressure-test questions
→Handle missing values with imputation and choose appropriate encodings
→Communicate findings as clear visual stories with Matplotlib and Seaborn

Curriculum

1Introduction to data analysis with pandas and NumPy
2Advanced data wrangling
3Data visualization with Matplotlib and Seaborn

Analysis is only half the job; the other half is making people believe and act on it. This program drills both — data wrangling and exploratory analysis on datasets you pick yourself, then visualization that communicates rather than decorates. It is grounded in working Python, not point-and-click tools, so the skills carry forward if you later move toward machine learning.

Prerequisites

Basic Python
Basic SQL
Elementary algebra
Basic descriptive and inferential statistics

Concepts this course teaches

Data Analysis
The full analysis loop is the program's spine.
Data Wrangling
A dedicated advanced-wrangling module.
Data Visualization
Visualization with Matplotlib and Seaborn.
Python
All work is done in Python (pandas, NumPy).

FAQ

Is this a data-analyst or a data-scientist track?: Analyst. The focus is asking questions of existing data and communicating the answers, not building predictive models — though the Python and wrangling skills transfer directly if you move on.
Do I bring my own data?: Each project has you choose your own datasets and questions. That is deliberate: you finish with portfolio pieces on topics you actually care about rather than canned exercises.