Path overview
In this path, you’ll learn the foundations of statistics such as sampling, working with variables, and understanding frequency distribution tables and the fundamentals of probability and how to use them for analysis. You’ll also learn how to create and test hypotheses with significance testing, and how to make forecasts based on patterns and trends with real-world data.
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, preparing and analyzing data with Python
- Creating insightful data visualizations
- Using statistics to perform descriptive analytics
- Using probabilities to perform predictive analysis
Path outline
Part 1: Python Introduction [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: Data Analysis and Visualization [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 with Python [1 course]
Data Cleaning and Analysis in Python 11h
Objectives- Employ data aggregation techniques
- Combine datasets
- Transform and reshape data
- Clean strings and resolve missing data
Part 4: Probability and Statistics with Python [5 courses]
Introduction to Statistics in Python 9h
Objectives- Sample data using simple random sampling, stratified sampling, and cluster sampling
- Measure variables in statistics
- Create frequency distribution tables
Intermediate Statistics in Python 8h
Objectives- Summarize a distribution using the mean, the weighted mean, the median, or the mode
- Measure the variability of a distribution using the variance and the standard deviation
- Compare values using z-scores
Introduction to Probability in Python 4h
Objectives- Estimate theoretical and empirical probabilities
- Employ the fundamental rules of probability
- Employ combinations and permutations
Introduction to Conditional Probability in Python 6h
Objectives- Assign probabilities based on conditions
- Assign probabilities based on event independence
- Assign probabilities based on prior knowledge
- Create spam filters using multinomial Naive Bayes
Hypothesis Testing in Python 4h
Objectives- Perform a permutation test
- Perform significance testing to understand an outcome's importance
- Define regular and multi-category chi-squared tests
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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.
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Go from zero to job-ready
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Build your project portfolio
Build confidence with our in-depth projects, and show off your data skills.
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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 6 more projects
Build your project portfolio with the Data Analyst in Python path.
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