Data Analytics & AI Certificate Online (Self-Paced)

Master Excel’s most powerful features to streamline data analysis, improve reporting accuracy, and extract meaningful insights from large datasets in this hands-on training.

• Learn Excel functions and formulas for organizing, calculating, and summarizing data efficiently
• Create and customize visual charts, including line, column, and pie charts to present data clearly
• Use logical statements, database functions, and data validation to manage and filter large datasets
• Build and manipulate Pivot Tables to quickly summarize, sort, and group information
• Explore advanced tools like named ranges, date calculations, and macro recording for custom reporting
• Apply auditing techniques, cell locking, and Excel hot keys to optimize your spreadsheet workflow 

Build a strong analytical foundation through hands-on training in statistical concepts, forecasting techniques, and data modeling methods used across industries for smarter decision-making.

• Understand core statistical concepts including measures of central tendency, data dispersion, and the normal curve
• Explore descriptive and inferential statistics, including probability distributions like binomial and Poisson
• Learn to analyze and forecast data using correlation, linear regression, and multiple regression techniques
• Apply predictive analytics with tools like trendlines, moving averages, and scenario modeling
• Create clear data visualizations using charts, histograms, icon sets, color scales, sparklines, and pivot tables
• Discover prescriptive analytics techniques such as Solver and linear programming to optimize decision-making

Develop the skills to turn raw data into compelling visual stories with Tableau, the industry-leading data visualization platform. This hands-on bootcamp teaches you to explore, analyze, and publish dashboards that communicate insights clearly and effectively.

• Connect to datasets in various formats, then clean, filter, and structure data for effective visual storytelling
• Create a range of visualizations including bar charts, line charts, treemaps, heat maps, dual-axis charts, and advanced types like spider maps and alluvial diagrams
• Use Tableau's calculation tools to create custom fields, apply aggregates, and deepen your data analysis
• Format charts with labels, tooltips, color, and axis adjustments, and build interactive map visualizations such as choropleths and proportional symbol maps
• Customize dashboards and stories for various audiences and screen sizes using Tableau's interactivity tools
• Publish work to Tableau Cloud and export dashboards for professional sharing and collaboration

Learn how to extract, filter, and manipulate data using SQL. This course covers PostgreSQL fundamentals, database querying, table joins, and advanced techniques for handling large datasets.

• Write SQL queries to retrieve, filter, and sort data efficiently
• Use joins to combine information from multiple tables and establish relationships
• Apply aggregate functions like SUM, COUNT, AVG, and GROUP BY to summarize data
• Work with subqueries, conditional logic (CASE statements), and advanced string functions
• Optimize queries using indexes, data type conversions, and best practices
• Explore views and user-defined functions to streamline database management

Unlock the power of Python for data-driven decision-making as you master Python programming fundamentals and dive into data analysis. Acquire essential skills to clean and manipulate data, create insightful visualizations, and perform statistical analysis, all through hands-on projects with real-world datasets.

• Handle different types of data such as integers, floats, and strings
• Control the flow of your programs with conditional statements, loops, and functions
• Reuse and simplify code with object-oriented programming
• Analyze tabular data with NumPy and pandas
• Create graphs and visualizations with Matplotlib
• Make predictions with linear regression, using scikit-learn

Learn the fundamentals of machine learning, including regression analysis and classification algorithms, in this practical, hands-on course. Gain the skills needed to solve real-world problems using machine learning, with a focus on Python programming and data science libraries.

• How to clean and balance your data using the pandas library
• Applying machine learning algorithms such as logistic regression and random forest using the scikit-learn library
• Choosing good features to use as input for your algorithms
• Properly splitting data into training, test and cross-validation sets
• Important theoretical concepts like overfitting, variance and bias
• Evaluating the performance of your machine learning models

Learn to gather, manipulate, and analyze real-life data in this course, where you'll gain hands-on experience with Python's NumPy and pandas libraries. Develop your data visualization skills using Matplotlib, Seaborn, Plotly, and Dash Enterprise, and complete real-life projects that can be deployed online.

• Plan, gather, and manipulate data from different sources to present a data story
• Find data stories through exploratory data analysis 
• Manipulate data with NumPy and pandas.
• Use advanced Python visualization libraries Plotly and Dash
• Build a dashboard and apply the rules of effective dashboard design to create professional data science solutions
• Go live with your project and deploy the dashboard on a live server

Learn how to use Python to extract data from websites and write loops for processing large numbers of pages. This course covers HTML and CSS, Python fundamentals, web scraping techniques, data storage, and scheduling.

• Learn Python syntax and how to construct programs and scripts
• Write scripts that automate manual tasks and update Excel files automatically
• Identify and correct common errors in your code
• Schedule your programs to run automatically on a regular basis
• Apply web scraping techniques through real-life exercises and examples

Advanced machine learning techniques turn into hands-on experience through real-world projects that you build and deploy across natural language processing, recommendation systems, forecasting, deep learning, and computer vision.

• Develop full-stack Flask applications and get them deployed
• Build recommendation engines that are collaborative as well as content-based
• Forecast trends through advanced time series modeling with Facebook Prophet
• Train convolutional neural networks in PyTorch and evaluate the results
• Detect objects in real time across images and video streams using YOLO
• Build sentiment analysis models that perform well by applying NLP techniques

Learn how to build AI-powered web applications using Flask and the OpenAI API. This course covers web development fundamentals, API integration, and AI-driven features for interactive applications.

• Set up Flask projects and create routes for handling web requests and rendering templates
• Design and style web applications using HTML, CSS, and Flask’s templating system
• Integrate the OpenAI API to implement AI-powered features like sentiment analysis
• Handle user input with dynamic forms and process data for real-time interactions
• Implement error handling and debugging techniques to ensure smooth application performance
• Deploy and test Flask applications for real-world use and AI-enhanced functionality

Three capstone projects run through this program, each one meant for your portfolio: 

Machine Learning & AI Capstone

• Select a structured dataset, clean it, and engineer features from it in order to train machine learning models such as logistic regression and random forest, then evaluate how they perform and present the results in clear visualizations.
• Present your work professionally, laying out the data processing workflow you followed, the modeling techniques you chose, and the insights you uncovered with Python libraries like pandas, scikit-learn, and Matplotlib.

Python for AI Capstone (Choose One of Two)

• AI Chat Assistant: Create an interactive chat assistant that lives on a webpage, with Flask and JavaScript handling the OpenAI API integration behind context-aware user interactions.
• Collectibles Identification App: Build a Flask web app that accepts uploaded images of collectible items, calls on OpenAI to identify them and produce descriptive metadata, and displays the logged session history dynamically.

Python Data Visualization Capstone

• Take global CO₂ emissions data together with GDP and population figures, clean and analyze it, and bring out trends and correlations in visualizations made with Matplotlib, seaborn, and plotly.
• Construct a responsive Dash dashboard for exploring the emissions data interactively, one that conveys regional trends, GDP-emission correlations, and emission anomalies clearly.

You work on the capstone projects on your own schedule, returning to the course materials as often as you need in order to check your progress and resolve questions as they come up.

Student work is available to view: examples of data science capstone projects.