Data Analytics & AI Certificate Online

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

Build a strong foundation in Python programming and data analysis through real-world projects that prepare you for advanced topics like machine learning and predictive modeling.

• Learn Python fundamentals, including variables, data types, functions, loops, and control flow for building robust programs
• Work with complex data structures like dictionaries and lists to efficiently organize and access data
• Use NumPy and Pandas to import, clean, and manipulate datasets for analysis and exploration
• Generate descriptive statistics and apply filtering, grouping, and pivoting techniques for deeper insights
• Visualize data with Matplotlib and create clear, customized charts such as bar graphs, histograms, and scatter plots
• Gain the practical skills needed to transition into machine learning with a solid understanding of data science workflows

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.

Learn how to automate web tasks and extract valuable online data using Python, with practical training in web scraping, data storage, and script scheduling.

• Understand how websites are structured using HTML and CSS to identify elements for data extraction
• Learn Python fundamentals, including variables, data types, conditionals, loops, and list manipulation
• Use the Requests and Beautiful Soup libraries to perform web scraping and target specific content
• Write loops to automate scraping across multiple web pages and streamline repetitive tasks
• Store scraped data in various formats, including text files and CSVs, for analysis and reporting
• Schedule Python scripts to run regularly, enabling continuous data collection and workflow automation

Transform raw data into interactive visual insights by building dashboards with Python’s top visualization tools. This course blends analysis, design, and deployment to help you showcase data professionally.

• Work with real-life datasets using Python’s core libraries, including NumPy for numerical computing and Pandas for data manipulation
• Create static and interactive visualizations using Matplotlib, Seaborn, and Plotly to clearly communicate trends and patterns
• Build multi-component dashboards using Dash Enterprise, incorporating callbacks, sliders, date pickers, and more
• Practice hands-on development by applying new skills to personalized projects with guided instructor support
• Publish your dashboards online using GitHub and Heroku to demonstrate your work to potential employers or clients
• Explore best practices for styling and structuring visual narratives that are clear, persuasive, and engaging

Gain hands-on experience building predictive models using Python in this practical machine learning course, designed to help you understand core algorithms and apply them to real-world data.

• Explore foundational techniques like linear and logistic regression for modeling numerical and categorical data
• Understand the difference between regression and classification problems and when to apply each approach
• Build and evaluate models using k-nearest neighbors, decision trees, and ensemble methods like random forest
• Learn key concepts such as cross-validation, training vs. test sets, and performance metrics like mean squared error
• Apply feature engineering techniques to improve model accuracy while managing overfitting and bias-variance tradeoffs
• Use Python’s essential data science libraries, NumPy, Pandas, and scikit-learn, to structure data and implement algorithms
• Gain insights into how machine learning powers systems at companies like Netflix, Spotify, and Amazon
• Complete a final portfolio project that demonstrates your ability to apply machine learning to solve real problems

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 and clean, filter, and structure the data for visual storytelling
• Create a variety of visualizations, including bar charts, line charts, treemaps, heat maps, and dual-axis charts
• Use Tableau’s calculation tools to build custom fields, apply aggregates, and enhance data analysis
• Format charts with labels, tooltips, colors, and axes to improve clarity and visual impact
• Work with geographic data to build interactive map visualizations, including choropleths and proportional symbol maps
• Customize dashboards and stories for different audiences and devices using Tableau’s interactivity tools
• Integrate external map services and use advanced visualization types such as spider maps and alluvial diagrams
• Publish your work to Tableau Online and export dashboards for professional sharing and collaboration

Gain hands-on experience with advanced machine learning techniques as you build and deploy real-world projects across natural language processing, recommendation systems, forecasting, deep learning, and computer vision.

• Build and deploy full-stack applications with Flask
• Implement collaborative and content-based recommendation engines
• Forecast trends using advanced time series modeling with Facebook Prophet
• Train and evaluate convolutional neural networks using PyTorch
• Perform real-time object detection in images and video streams with YOLO
• Apply NLP techniques to build effective sentiment analysis models

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.

Throughout this program, you will complete three capstone projects to showcase in your portfolio: 

Machine Learning & AI Capstone

• Choose, clean, and engineer features from a structured dataset to train machine learning models (e.g., logistic regression, random forest), evaluate performance, and visualize results clearly.
• Deliver a professional presentation detailing your data processing workflow, modeling techniques, and insights discovered using Python libraries like pandas, scikit-learn, and Matplotlib.

Python for AI Capstone (Choose One of Two)

• AI Chat Assistant: Build an interactive chat assistant embedded on a webpage, using Flask and JavaScript to integrate with OpenAI’s API for context-aware user interactions.
• Collectibles Identification App: Develop a Flask-based web app allowing image uploads of collectible items, leveraging OpenAI to identify items, generate descriptive metadata, and dynamically display logged session history.

Python Data Visualization Capstone

• Clean, analyze, and visualize global CO₂ emissions alongside GDP and population data, highlighting trends and correlations through insightful visualizations with Matplotlib, seaborn, and plotly.
• Build a responsive Dash dashboard enabling interactive exploration of emissions data, clearly communicating insights such as regional trends, GDP-emission correlations, and emission anomalies.

You will work on your capstone projects both in and outside of class, using scheduled mentoring sessions to review your progress, ask questions, and get personalized feedback from your instructor.

See examples of data science capstone projects from students.