Ai course for data analysts

Module 1: Introduction to AI for Data Analysis

1.1 AI Concepts for Data Analysts

AI Concepts for Data Analysts: A Simplified Explanation

For a data analyst, understanding basic AI concepts is becoming crucial. This isn’t about becoming a machine learning engineer, but about understanding how AI methods can augment your traditional analytical toolkit. Here’s a breakdown of key areas:

1. Supervised Learning:

• Concept: Learning from labeled data. You provide the algorithm with inputs and the desired output (the “label”). The algorithm learns the relationship between them and can then predict the output for new, unseen inputs.
• Data Analyst Application:
  • Predictive Modeling: Predicting customer churn (whether a customer will leave), based on historical customer data (age, purchase history, website activity) labeled with whether they churned or not.
  • Classification: Categorizing customer reviews as positive, negative, or neutral based on the words used in the review. Each review is labeled with its sentiment.
  • Regression: Predicting house prices based on features like square footage, number of bedrooms, and location. Each house in the dataset is labeled with its sale price.

2. Unsupervised Learning:

• Concept: Learning from unlabeled data. You provide the algorithm with inputs without any pre-defined outputs. The algorithm tries to find patterns, structures, and relationships within the data itself.
• Data Analyst Application:
  • Clustering: Grouping customers into segments based on their purchasing behavior. This helps identify distinct customer profiles without needing pre-defined segments.
  • Anomaly Detection: Identifying fraudulent transactions by detecting unusual patterns in credit card usage.
  • Dimensionality Reduction: Simplifying large datasets by reducing the number of variables while preserving important information. This can make data easier to visualize and analyze.

3. Reinforcement Learning:

• Concept: Training an agent to make decisions in an environment to maximize a reward. The agent learns through trial and error, receiving feedback (rewards or penalties) for its actions.
• Data Analyst Application (Less Common, but Emerging):
  • Optimizing Marketing Campaigns: Automatically adjusting ad spend and targeting based on real-time performance data to maximize conversion rates.
  • Recommendation Systems (partially): While often using collaborative filtering (a more traditional data analysis approach), reinforcement learning can be used to dynamically adjust recommendation strategies based on user feedback.

4. Key Metrics & Evaluation:

• Concept: Understanding how to measure the performance of AI models. This is critical for determining if a model is accurate, reliable, and useful.
• Data Analyst Application:
  • Accuracy: How often does the model correctly predict the outcome?
  • Precision & Recall: How well does the model identify positive cases, and how many positive cases does it miss? (Important for classification tasks)
  • RMSE (Root Mean Squared Error): How close are the model’s predictions to the actual values? (Important for regression tasks)
  • Model Bias: Is the model unfairly biased against certain groups of people?

5. Data Preparation & Feature Engineering:

• Concept: Cleaning, transforming, and preparing data for AI models. This is often the most time-consuming part of any AI project. Feature engineering involves creating new features from existing data to improve model performance.
• Data Analyst Application:
  • Missing Value Imputation: Filling in missing data points using statistical techniques.
  • Data Normalization/Standardization: Scaling data to a standard range to prevent variables with larger scales from dominating the model.
  • Creating Interaction Features: Combining existing features to create new features that might be more predictive (e.g., multiplying website traffic by customer age).

Why This Matters to Data Analysts:

Understanding these AI concepts allows data analysts to:

• Identify appropriate use cases: Recognize when AI techniques can solve business problems that traditional methods can’t.
• Communicate effectively with data scientists: Participate in discussions about AI projects and understand the technical details.
• Interpret model results: Analyze the output of AI models and translate the insights into actionable recommendations.
• Evaluate model performance: Determine if a model is working correctly and provide feedback for improvement.
• Automate repetitive tasks: Use AI to automate data cleaning, analysis, and reporting processes.

In essence, AI becomes another tool in the data analyst’s belt, allowing them to tackle more complex problems and generate deeper insights.

1.2 AI vs. Traditional Data Analysis

1.3 Ethical Considerations in AI

Module 2: Mastering ChatGPT for Data Analysis

2.1 Prompt Engineering for Data Insights with ChatGPT

2.2 Data Summarization and Report Generation with ChatGPT

2.3 Advanced ChatGPT Techniques for Data Exploration

Module 3: Leveraging Bard for Data-Driven Decision Making

3.1 Bard’s Capabilities for Data Interpretation

3.2 Comparative Analysis of Data using Bard

3.3 Using Bard for Predictive Modeling Assistance

Module 4: Code Interpreter for Faster Insights and Automation

4.1 Automating Data Cleaning and Preprocessing with Code Interpreter

4.2 Generating Visualizations with Code Interpreter

4.3 Advanced Data Manipulation using Code Interpreter

Module 5: AI-Powered Data Cleaning and Preprocessing

5.1 Automated Outlier Detection

5.2 Missing Value Imputation with AI

5.3 Data Transformation Techniques

Module 6: AI for Exploratory Data Analysis (EDA)

6.1 Automated Feature Selection

6.2 AI-Driven Pattern Discovery

6.3 Generating Hypotheses with AI

Module 7: Predictive Modeling with AI

7.1 Regression Models with AI

7.2 Classification Models with AI

7.3 Time Series Forecasting with AI

Module 8: AI-Driven Data Visualization and Reporting

8.1 Automated Report Generation

8.2 Interactive Dashboards with AI

8.3 Personalized Data Insights using AI