AI for Data Analysts
AI for Data Analysts Syllabus
This course is designed to help data analysts understand and apply AI in their workflows. You’ll learn the fundamentals of machine learning, AI project lifecycles, and practical applications in data collection, transformation, visualization, and stakeholder communication.
- What is Machine Learning
- Classification vs Regression
- Supervised vs Unsupervised Learning
- ML Algorithms Overview
- Tooling for ML
- 10 Stages of AI Project Lifecycle
- Requirements and Scope of Work (SOW)
- Data Collection
- Data Preparation & Exploratory Data Analysis
- Feature Engineering
- Model Selection & Training
- (Accuracy, Prediction, Recall & F1 Score)
- Model Evaluation Metrics: When to use which Metric?
- Model Fine Tuning
- Model Deployment
- Deployment & Monitoring Using ML Ops
- The Role of Data in AI
- Data Infrastructure in a Company
- Data Collection: Overview
- Data Storage and Transformation: Overview
- Data Distribution: Privacy, Ethics & Governance
- Data Collection
- Data Cleaning
- Data Transformation
- Data Visualization & Insights
- 4 General Use Cases
- Why Humans are Better Than AI?
- Using AI for Emails & Presentations
- Job Titles & Resume Customization
- Asking For Referral
- Mock Interviews: Resume & Job-Specific Questions
- Mock Interviews: Technical Interview
- Mock Interviews: Case Study & Behavioral
- PDF File of Useful AI Prompts
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What You’ll Learn
- The fundamentals of AI & machine learning
- How data powers AI & ML algorithms
- AI-driven data cleaning, transformation, and visualization
- Model training, evaluation, and deployment
- Using AI for job search, resumes & interview prep
- ChatGPT prompts for AI-enabled data workflows
