This is my personal Python learning journal from the Masterschool Data Analytics program. It includes hands-on exercises, assessments, and practice notebooks organized by sprint. Each notebook reflects a different stage in my learning journey.
Gain a solid understanding of Python basics, such as:
- Data Types: Strings, integers, floats, and booleans.
- Variables: Storing and manipulating data.
- Arithmetic Operators: Performing calculations.
- Conditions: Using
if,elif, andelsestatements to make decisions. - Functions: Writing reusable blocks of code.
| Notebook | Type | Topic |
|---|---|---|
| Notebook 01 | Lecture | Getting Started with Python |
| Notebook 02 | Lecture | Python Basics |
| Notebook 03 | Lecture | Functions |
| Notebook 04 | Lecture | Operators & Conditional Statements |
| Notebook 05 | Exercises | Expressions |
| Notebook 06 | Exercises | Variables |
| Notebook 07 | Exercises | Arithmetic Operators |
| Notebook 08 | Exercises | Functions |
| Notebook 09 | Exercises | Conditions |
| Notebook 10 | Assessment | Sprint 1 |
| Notebook 11 | Exercises | Hello Advanced |
| Notebook 12 | Exercises | Functions In-Depth |
Learn how to handle real-world data challenges by diving deeper into Python’s powerful features:
- Strings: Manipulating and formatting text.
- Booleans: Working with
TrueandFalsevalues. - Lists: Storing and managing collections of data.
- Loops: Automating repetitive tasks with
forandwhileloops. - Dictionaries: Storing data in key-value pairs.
- Tuples and Sets: Working with immutable and unique collections.
| Notebook | Type | Topic |
|---|---|---|
| Notebook 13 | Lecture | Strings & Lists in Python |
| Notebook 14 | Lecture | Iterations with Loops |
| Notebook 15 | Lecture | Dictionaries, Tuples & Sets |
| Notebook 16 | Lecture | Recap: Python Fundamentals |
| Notebook 17 | Exercises | Intro to Strings |
| Notebook 18 | Exercises | Lists |
| Notebook 19 | Exercises | Booleans |
| Notebook 20 | Exercises | Loops |
| Notebook 21 | Exercises | While Loops |
| Notebook 22 | Exercises | Dictionaries |
| Notebook 23 | Exercises | Tuple |
| Notebook 24 | Exercises | Set |
| Notebook 25 | Assessment | Sprint 2 |
| Notebook 26 | Exercises | Nested For Loops |
| Notebook 27 | Exercises | Nested Data Structures |
| Notebook 28 | Exercises | Operations on Lists |
| Notebook 29 | Challenge | Advanced Code Challenge |
- Build a strong foundation in Python and learn Pandas for data analysis.
- Cover key topics including the fundamentals of Pandas and core data wrangling techniques, along with exploring datasets and summarizing data.
| Notebook | Type | Topic |
|---|---|---|
| Notebook 30 | Lecture | Intro to Pandas |
| Notebook 31 | Lecture | Pandas DataFrame & Data Importing |
| Notebook 32 | Lecture | Accessing and Filtering DataFrame |
| Notebook 33 | Lecture | Working With DataFrames |
| Notebook 34 | Exercises | Introduction to Pandas Series |
| Notebook 35 | Exercises | Pandas DataFrame |
| Notebook 36 | Exercises | Pandas Foundations (Building on the Basics) |
- Focus on cleaning and transforming messy datasets to make them analysis-ready.
- Learn to merge and concatenate DataFrames, perform data assessment and cleaning, and apply aggregation techniques.
| Notebook | Type | Topic |
|---|---|---|
| Notebook 37 | Lecture | Concatenating & Merging DataFrames |
| Notebook 38 | Lecture | Assessing & Cleaning Data |
| Notebook 39 | Lecture | Assessing, Cleaning & Grouping Data from a DataFrame (skipped)1 |
| Notebook 40 | Lecture | Defining Functions to Clean Data |
| Notebook 41 | Exercises | Data Integration |
| Notebook 42 | Exercises | Data Assessment |
| Notebook 43 | Exercises | Data Cleaning |
| Notebook 44 | Exercises | Aggregating Information & Applying |
1 This lecture was a revision of the previous day's concepts to solidify knowledge - see Notebook 38 for notes.
- Explore essential tools and techniques for effective data exploration.
- Understand and practice univariate, bivariate, and multivariate analysis along with other EDA methods.
| Notebook | Type | Topic |
|---|---|---|
| Notebook 45 | Lecture | Univariate Analysis |
| Notebook 46 | Lecture | Bivariate Analysis |
| Notebook 47 | Lecture | Multivariate Analysis |
| Notebook 48 | Lecture | Complete EDA on Tips Dataset |
| Notebook 49 | Exercises | Univariate Analysis within EDA |
| Notebook 50 | Exercises | Bivariate Analysis within EDA |
| Notebook 51 | Exercises | Multivariate EDA |
- Apply your knowledge in a hands-on project.
- Gain practical experience in cleaning, preparing, exploring, visualizing, and summarizing data.
| File | Description |
|---|---|
| Project Description | Project overview with tasks and deliverables |
| Raw Data | Original dataset provided for analysis |
| Metadata | Data dictionary with a description of the original dataset |
| Clean Data | Cleaned dataset after wrangling |
| Analysis | Full exploratory data analysis (EDA) and key insights |
This project applied the full data analysis workflow to uncover insights about vehicle pricing, efficiency, and performance.