Data Analytics Professional Certificate

Develop a robust foundation in data analytics, combining classical statistical techniques with cutting-edge AI-assisted workflows. Gain the skills to extract meaningful insights from complex datasets, make data-driven decisions amid uncertainty, and communicate results effectively.

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Statistics for real-world decision-making: Learn to calculate and interpret descriptive and inferential statistics in the context of business problems.
Data visualization and storytelling: Practice the art of creating compelling visualizations that effectively communicate complex data patterns.
Generative AI for analytics: Develop a critical eye for data quality issues and potential biases in data collection and analysis.

Why Enroll?

Data-informed decision-making is now essential across all levels, from everyday consumer choices to global policy-making. As reliance on data grows, so does the need for professionals who can analyze and interpret it effectively. The Data Analytics Professional Certificate, led by industry leader Sean Barnes, equips you with the skills to manage the entire data lifecycle, from defining problems to delivering insights.

Whether you’re a software engineer exploring data, a business analyst extracting insights, or a STEM graduate building a data-centric career, you’ll gain the foundation to excel in the data economy. This course series blends classical statistics with AI-assisted workflows, perfect for aspiring data professionals or experienced practitioners.

In Data Analytics Foundations, you’ll gain proficiency in the essentials of data analysis, visualization, and communication. You’ll learn to calculate and interpret descriptive statistics, create compelling visualizations, and leverage large language models to augment your analysis.

Building on this foundation, Applied Statistics for Data Analytics goes deep into the statistical underpinnings of data analytics. Practical exercises, like developing forest fire prevention strategies, will help you demonstrate impacts in the real world.

Unique to this program is its integration of new AI tools into the analytics workflow. You’ll learn to use large language models as a thought partner, accelerating tasks like simulation modeling, formula debugging, and data visualization.

The skills you’ll gain are in high demand, and with data science roles projected to grow 36% from 2023 to 2033, developing these skills puts you at the forefront of a data-centric world.

Courses 1 and 2 of this course series are available to enroll now, with more courses coming in early 2025! You’ll earn a certificate for individual courses, and certifying in all of them as they become available will grant you the Data Analytics Professional Certificate

Course Syllabus

Module 1: Data and the Data Analyst Role

Define data and its common representations, and differentiate between structured vs. unstructured data and variable types (categorical, discrete, continuous).
List common data file formats (txt, CSV, JSON, etc.) and explore data roles (analyst, engineer, scientist) and their business impact.
Identify career opportunities and the components of the data ecosystem. Use large language models (LLMs) for stakeholder analysis and evaluate their strengths and weaknesses.

Module 2: Using Spreadsheets for Data Analytics

Describe spreadsheet use cases in business and life, introducing Google Sheets and alternatives.
Perform basic spreadsheet tasks (create, save, share, organize data) and use tools to sort and filter data.
Compare different file types and data types (date, numeric, text) and connect them to structured/unstructured data.

Module 3: Data Visualization

Understand the role of data storytelling and how visualizations (bar charts, scatter plots) aid in analysis.
Create and interpret bar and line charts in Google Sheets and select the best visualization for different types of analysis.
Construct data stories using analytics best practices.

Module 4: The Data Analytics Lifecycle

Define stages in the data analysis lifecycle (problem definition, data collection, preprocessing, analysis, interpretation, evaluation).
Gather business context and stakeholder requirements, using tools like the Rumsfeld Matrix to refine questions.
Explain how domain knowledge affects the analysis process and helps identify the right data and analysis techniques.

Instructor

Sean Barnes

Instructor Sean Barnes is a Data Science & Engineering Leader at Netflix. His research interests included infectious disease modeling, healthcare and sports analytics, agent-based and discrete-event simulation, machine learning, and data visualization.

Build foundational knowledge

Advanced statistical applications: Move beyond theory to practical implementation of correlation analysis, confidence intervals, and hypothesis testing in solving real business challenges.

Effective data-driven communication: Develop the crucial skill of translating complex analytical findings into clear, actionable insights for diverse audiences.

Future-ready analytics skills: Gain experience in AI-augmented workflows, preparing you for the evolving landscape of data analytics where human expertise and AI capabilities converge.

Real-world practice with hands-on projects

Learn end-to-end data analysis workflows: Apply segmentation techniques and descriptive statistics to real hotel booking data, demonstrating how small changes in strategy can significantly boost a hotel’s revenue.

Gain actionable insights from complex datasets: Explore how streaming services analyze song similarity, using statistical techniques to uncover patterns in popular music.

Apply advanced statistical modeling to real-world problems: Analyze environmental data to assess forest fire severity and guide resource allocation, showing how data analytics can aid in disaster management.

Who this course is for

Aspiring data analysts with no prior experience, seeking to enter a growing field
Professionals from any background looking to add practical data analysis skills to their toolkit
People interested in learning industry-standard tools like spreadsheets and Python
Data enthusiasts curious about applying AI to enhance analysis and decision-making
Career changers aiming to build a portfolio of real-world data projects

Develop job-ready skills and prepare for a career in data analytics – no prior experience required.

Skills You Will Gain

Time Series Analysis
LLMs (Large Language Models)
Data Visualization
Data Analytics Fundamentals
Spreadsheets (Google Sheets)
Descriptive Statistics
AI for Analytics
Applied Statistics and Probability
Inferential Statistics
Python Programming for Data Analysis
SQL for Data Manipulation
Data Cleaning and Preprocessing
Correlation Analysis
Tableau Dashboard Creation
Data Segmentation
Data Storytelling

5 Courses
Self-paced
Introductory

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Data Analytics Professional Certificate

Why Enroll?

Course Syllabus

Course 1: Data Analytics Foundations

Course 2: Applied Statistics for Data Analytics

Course 3: Data Analytics at Scale with Python

Course 4: Data and Preprocessing with Python and SQL

Course 5: Data Storytelling