Data Analytics 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

• 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 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.

• Scrape (extract) text and images from websites
• Schedule Python scripts to run automatically
• Automate browser interactions, reporting, and messaging

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

• 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

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

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.