Path overview
In this path, you’ll gain the fundamental skills to begin cleaning data, using the powerful tools offered by Python such as identifying and removing inaccurate records from a dataset. You’ll learn how to manipulate, analyze, and visualize data using premier Python libraries such as Pandas and Numpy.
Best of all, you’ll learn by doing — you’ll write code and get feedback directly in the browser. You’ll apply your skills to several guided projects involving realistic business scenarios to build your portfolio and prepare for your next interview.
Key skills
- Cleaning and visualizing data
- Programming with Python to manipulate data
- Working with missing data
Path outline
Part 1: Introduction to Python [4 courses]
Introduction to Python Programming 3h
Objectives- Write computer programs using Python
- Save values using variables
- Process numerical data and text data
- Create lists using Python
Basic Operators and Data Structures in Python 5h
Objectives- Use for loops to repeat processes and conduct data analysis
- Implement if, else, and elif statements in programming logic
- Employ logical and comparison operators in Python
- Develop and update Python dictionaries for data manipulation
- Construct frequency tables using dictionaries for data analytics
Python Functions and Jupyter Notebook 5h
Objectives- Write Python functions
- Debug functions
- Define function arguments
- Write functions that return multiple variables
- Employ Jupyter notebook
- Build a portfolio project
Intermediate Python for Data Science 8h
Objectives- Clean and analyze text data
- Define object-oriented programming in Python
- Process dates and times
Part 2: Intermediate Python and Pandas [2 courses]
Introduction to Pandas and NumPy for Data Analysis 13h
Objectives- Improve your workflow using vectorized operations
- Select data by value using Boolean indexing
- Analyze data using pandas and NumPy
Introduction to Data Visualization in Python 8h
Objectives- Visualize time series data with line plots
- Define correlations and visualize them with scatter plots
- Visualize frequency distributions with bar plots and histograms
- Improve your exploratory data visualization workflow using pandas
- Visualize multiple variables using Seaborn's relational plots
Part 3: Data Cleaning in Python [3 courses]
Data Cleaning and Analysis in Python 11h
Objectives- Employ data aggregation techniques
- Combine datasets
- Transform and reshape data
- Clean strings and resolve missing data
Advanced Data Cleaning in Python 9h
Objectives- Clean and manipulate text data using regular expressions
- Employ lambda functions and list comprehension with pandas
- Resolve missing data
Data Cleaning Project Walkthrough 7h
Objectives- Complete a data cleaning project from start to finish
- Improve your data cleaning skills
The Dataquest guarantee
Dataquest has helped thousands of people start new careers in data. If you put in the work and follow our path, you’ll master data skills and grow your career.
We believe so strongly in our paths that we offer a full satisfaction guarantee. If you complete a career path on Dataquest and aren’t satisfied with your outcome, we’ll give you a refund.
Master skills faster with Dataquest
Go from zero to job-ready
Learn exactly what you need to achieve your goal. Don’t waste time on unrelated lessons.
Build your project portfolio
Build confidence with our in-depth projects, and show off your data skills.
Challenge yourself with exercises
Work with real data from day one with interactive lessons and hands-on exercises.
Showcase your path certification
Share the evidence of your hard work with your network and potential employers.
Projects in this path
Project: Learn and Install Jupyter Notebook
For this project, you’ll take on the role of a Jupyter Notebook beginner. You’ll learn the essentials of running code, adding explanatory text, and installing Jupyter locally to prepare for real-world data projects.
Guided Project: Profitable App Profiles for the App Store and Google Play Markets
For this project, you’ll be a data analyst at a company that builds free, ad-supported Android and iOS apps. To drive revenue, you’ll analyze real app market data to find app profiles that attract the most users.
Guided Project: Exploring Hacker News Posts
For this project, we’ll be data analysts exploring Hacker News posts. We’ll use Python string manipulation, OOP, and date handling to analyze trends driving post popularity. Check out our Jupyter Notebook Guided Project if needed.
Guided Project: Exploring eBay Car Sales Data
For this project, we’ll assume the role of data analysts for a used car classifieds service to explore and clean a dataset of car listings from eBay Kleinanzeigen, a section of the German eBay website.
Guided Project: Finding Heavy Traffic Indicators on I-94
For this project, you’ll assume the role of a data analyst exploring a dataset on westbound traffic on the I-94 Interstate highway. You’ll apply exploratory data visualization techniques to determine indicators of heavy traffic.
Plus 3 more projects
Build your project portfolio with the Data Analyst in Python path.
Learning resources
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