Data Analytics using AI

Curriculum

Session 1: Introduction to Data Analytics and AI

• Objective: Understand the fundamentals of data analytics and the role of AI.

Topics:

• • Basics of Data Analytics: Types and importance (Descriptive, Predictive, Prescriptive).
  • Introduction to AI and Machine Learning: Overview and relevance to data analytics.
  • Tools and Platforms: Python, Jupyter Notebook, AI libraries (scikit-learn, TensorFlow).

Activity:

• • Quick setup of Python environment and Jupyter Notebook.

Session 2: Data Preprocessing, Visualization, and Analysis using AI

• Objective: Learn data preprocessing techniques and visualization using AI tools.

Topics:

• • Data Cleaning and Transformation: Handling missing values, feature engineering.
  • Data Visualization Techniques: Using Matplotlib, Seaborn.
  • Introduction to ChatGPT for Data Analysis: Automating EDA and generating insights.

Activity:

• • Hands-on exercise: Data cleaning, visualization, and using ChatGPT for exploratory data analysis (EDA).

Session 3: Machine Learning and Clustering in Data Analytics

• Objective: Explore key machine learning algorithms, including clustering techniques, for data analytics.

Topics:

• • Supervised Learning: Overview of algorithms (Linear Regression, Decision Trees).
  • Clustering Techniques: Understanding clustering methods like K-Means, Hierarchical Clustering, DBSCAN.
  • Model Evaluation: Understanding metrics (Accuracy, Precision, Recall for supervised learning; Silhouette Score for clustering).

Activity:

• • Hands-on: Implementing clustering algorithms and visualizing clusters using Python.

Session 4: Advanced AI Techniques in Data Analytics

Objective

: Dive into advanced AI techniques and their applications in data analytics.

Topics:

• • Introduction to Deep Learning: Basics of Neural Networks, CNNs, RNNs.
  • AI for Big Data: Introduction to Big Data tools (Hadoop, Spark).
  • Advanced Use of ChatGPT: Generating reports, querying datasets, and performing automated clustering analysis.

Activity:

• • Practical exercise: Implementing a simple neural network and using ChatGPT for advanced data querying and analysis, including automated clustering.

Session 5: Real-World Applications, Data Analysis using ChatGPT, and Ethics in AI-Driven Analytics

• Objective: Apply knowledge to real-world scenarios, utilize ChatGPT for advanced data analysis, and discuss ethical implications.

Topics:

• • AI Applications in Industry: Use cases in Healthcare, Finance, Retail.
  • Data Analysis Using ChatGPT:
    • Leveraging ChatGPT for advanced data analysis and decision-making.
    • Automating complex data tasks: Trend analysis, forecasting, hypothesis testing, and clustering.
  • Ethics in AI: Understanding biases, transparency, and deployment challenges.
  • Building End-to-End AI Solutions: From data collection to deployment.

Activity:

• • Group project: Developing a mini AI-driven data analytics solution using ChatGPT, including clustering analysis.
  • Discussion: Ethical considerations and future trends in AI and data analytics.