# AI for Data Analytics Canonical URL: ## Overview Transform your data analysis process with artificial intelligence (AI). This course provides a deep dive into AI tools that automate data collection, preprocessing, analysis, and visualization, enabling participants to extract valuable insights with minimal coding expertise. By the end of the course, learners will be proficient in utilizing AI-driven analytics across various sectors such as finance, marketing, and healthcare, and will be adept at presenting their findings effectively through sophisticated visualizations and reports. Throughout the course, participants will explore a range of modules that cover essential aspects of AI in data analytics. The curriculum includes an introduction to AI tools for data analysis, followed by hands-on training in data collection, cleaning, and preprocessing techniques. Learners will also examine exploratory data analysis, predictive modeling, and advanced AI techniques such as natural language processing and time series forecasting. The course culminates in a capstone project where participants apply their skills to a comprehensive data analysis task, showcasing their ability to implement AI solutions in real-world scenarios. ## What you'll learn - Overview of popular AI tools and platforms like IBM Watson, Google AI, Tableau, and Microsoft Azure AI - Learn automated data cleaning methods and how to handle missing data and outliers using AI tools - Use AI tools to generate summary statistics, visualize data distributions, and detect patterns - Understand regression, classification, and clustering, and use AI tools to build and evaluate predictive models - Explore applications of NLP for text analysis and automated time series forecasting with AI tools - Work with AI tools and platforms including IBM Watson, Google AI, Tableau, and Microsoft Azure ## Curriculum #### Welcome and Orientation - Who is in the room, where everyone stands on AI, and your first question put to a real dataset inside the first hour #### What You Need to Know About AI - How AI actually works and the three consequences that make up the rest of the course - Why having no internal test for truth means AI can fail confidently - Why some data must never be entered into a system you do not control - Why verification is not optional #### What You Need to Know About Data - Structured and unstructured data and where data comes from - The four levels of analytics, from what happened to what we should do about it - The habit of asking who is missing from a dataset before trusting what it says #### Where AI Earns Its Place, and Where It Does Not - Five kinds of work worth handing over to AI - Three conditions under which you should not reach for AI at all #### How to Talk to It - What a strong prompt contains and which of six prompt patterns fits the moment - How to iterate without starting over - Why a long conversation eventually stops seeing the file you uploaded #### Data You Do Not Know - How to orient yourself before working with data you cannot check by eye - Profile a messy, unfamiliar dataset and distinguish errors from real anomalies - Clean data transparently, explore it, and audit what the cleaning process removed - Identify rows or information removed without your approval #### Communicating What You Found - Choose which chart answers which question and make AI justify the chart it selected - Apply five rules for reading a chart critically - Use three sentences to turn a finding into a decision - Complete a mini-analysis that runs the full workflow from beginning to end #### Unlocking What Was Out of Reach - Build a working prediction from a paragraph of plain English and interrogate the result - Read a forecast for its interval rather than only its line - Pull meaning from thousands of customer reviews without reading each one individually - Apply checks that determine whether predictions, forecasts, and text analysis can be believed #### The Errors That Survive a First Check - Identify fabricated figures and other errors that initially appear reasonable - Recognize trends that reverse when data is split and hidden third factors that affect results - Identify models that memorized rather than learned - Use deliberate checks to catch errors that pass a casual review - Hunt for problems within analyses that initially appear correct #### Putting It Together - Use seven prompts in one conversation to complete the full workflow - Examine what changes when nobody is reading the intermediate steps - Understand agents in practical terms, including how most people will encounter them by inheriting rather than building one - Solve and verify a real question from someone in the room #### Capstone and Close - Apply the workflow to one real task from your own job - Document the actual prompts used and the verification attached to each - Identify the first action you will take when you return to your desk ## Schedule - Oct 29, 2026 – Oct 30, 2026 — NYC - Feb 25, 2027 – Feb 26, 2027 — NYC - Apr 15, 2027 – Apr 16, 2027 — NYC - Jun 16, 2027 – Jun 17, 2027 — NYC - Sep 21, 2027 – Sep 22, 2027 — NYC ## FAQ ### Is this course open to beginners? Yes, this course is open to beginners. Students must be comfortable using a computer. No other prior knowledge is required. ### Do you offer payment plans or student financing for this course? This course does not qualify for payment plans or student financing. See our Payment Plan FAQ to find related programs that qualify. ### Can I take this course online (remote learning)? No upcoming live online dates are currently available. Please contact us about future availability. ### What happens if I need to miss a session? You have some options: You can watch a recording of the session. We will provide any course materials. Contact us to confirm whether remote attendance is available for your session. You can use your free retake to make up any missed class time. ### What’s included with my tuition? A hands-on learning experience working on projects and exercises, which is proven to boost comprehension, retention, and engagement Expert instructors who are industry professionals and experienced educators that are driven to help you succeed Top-notch curricula that have been tried and tested over many cohorts and are consistently improved for an optimal learning experience A 1-year free retake policy which allows students to retake any part of the course or program for free to refresh the materials and gain additional practice 30-day access to class recordings to review material at your own pace A certificate of completion to verify your accomplishment ### Are there any additional fees or expenses? There are no extra fees or taxes for our courses. The price you see on this page is the maximum you’ll pay us. However, if you plan to take the course live online , you may need to obtain required software. We’ll help you get set up with a free trial of paid software prior to the class. Most of our coding classes utilize freely-available open-source software. For most of our design and motion graphics courses, we will help you get set up with a free trial of Adobe Creative Cloud. If you attend the course in-person, we will have a computer already set up for you with all of the required software for no additional cost. ### Do you offer any discounts for this course? Take this course as part of a certificate program and save: Generative AI Certificate Price: $3,495 | Hours: 84 Hours ## Pricing **Tuition:** $1049