Data Scientist in Python Certificate Program

Develop foundational Python programming skills by writing code, working with variables, and processing numerical and text data.

Strengthen Python fundamentals by using loops, conditional logic, operators, and dictionaries to manipulate data and construct frequency tables.

Create reusable Python functions and run analyses in Jupyter Notebook to organize code, debug logic, and complete portfolio-ready data projects.

Strengthen your Python data science skills by cleaning text data, working with dates and times, and applying object-oriented programming concepts.

Develop practical skills with NumPy and pandas to explore, clean, and analyze data efficiently using real datasets and guided practice.

Apply statistical reasoning to visualization by combining Python plotting tools with sound design choices to communicate patterns, trends, and insights clearly.

Apply data visualization and information design principles in Python to turn raw data into clear, engaging stories for stakeholders.

Practice cleaning and preparing messy datasets in Python by aggregating, reshaping, and combining data for efficient, real-world analysis.

Go beyond basic data cleaning by working with messy real-world datasets using advanced Python techniques like regex, lambdas, and list comprehensions.

Real datasets are messy. This project-based course walks through cleaning, combining, and preparing data in Python for analysis.

Learn to navigate the filesystem, manage permissions, and run scripts from the command line to support efficient, repeatable data workflows.

Learn to inspect files, read documentation, and process text efficiently using streams, redirection, and pipelines in real data workflows.

Develop core SQL skills by writing queries to access, explore, and manipulate data stored in relational databases for common data analysis tasks.

Summarize large datasets by computing statistics, grouping records, and applying SQL aggregate functions to extract meaningful insights.

Combine and analyze data across multiple tables by applying SQL joins and set operators to produce comprehensive, query-ready datasets.

Retrieve and analyze data from SQLite databases by running SQL queries in Python and converting results into pandas DataFrames for analysis.

Write scalable, advanced SQL queries by nesting subqueries and using common table expressions to solve complex analysis problems.

Analyze data more effectively by using SQL window functions to compute running metrics, rankings, distributions, and offsets within queries.

Develop practical skills for collecting, extracting, and analyzing web data using Python APIs, web scraping, and real-world datasets.

Translate ambiguous business questions into measurable metrics and analyses, addressing churn, pricing, and customer behavior with Python.

Practice core statistical techniques in Python to sample data, analyze variables, and visualize frequency distributions for real projects.

Develop practical skills to summarize distributions, measure variability, and compare values using core statistical tools in Python.

Build a practical foundation in probability using Python, covering random experiments, core rules, and counting techniques used in data analysis.

Extend probability fundamentals to conditional reasoning, independence, and prior knowledge, culminating in a Naive Bayes spam filter.

Practice hypothesis testing in Python by running chi-square and permutation tests to evaluate real-world outcomes and statistical significance.

Develop a supervised machine learning workflow for classification by training, evaluating, and tuning models with scikit-learn on real-world datasets.

Apply unsupervised machine learning techniques by building, evaluating, and interpreting k-means models to segment and explore unlabeled data.

Explore the calculus concepts that power machine learning, from rates of change and derivatives to the mechanics behind optimization algorithms.

Build hands-on linear algebra skills for machine learning by working with vectors, matrices, and systems used in real ML models.

Model and interpret relationships between variables by constructing, evaluating, and applying linear regression for inference and prediction.

Optimize machine learning models by implementing and applying gradient descent techniques to efficiently train and improve predictive performance.

Classify and interpret categorical outcomes by constructing, evaluating, and applying logistic regression models for inference and prediction.

Apply decision trees and random forest models to solve classification and regression problems while producing interpretable, high-performing predictions.

Improve machine learning model performance by applying optimization techniques such as cross-validation, regularization, and feature engineering in Python.

Explore deep learning with PyTorch by training, regularizing, and evaluating neural networks designed to generalize well on real data.

Strengthen your data analysis workflow with intermediate command line skills like piping, redirection, and transforming data directly from the shell.

Practice version control with Git to track changes, collaborate via GitHub, and manage real projects using workflows teams rely on every day.

Work with Apache Spark to process massive datasets using RDDs, DataFrames, and Spark SQL across distributed environments.