Data Analytics using AI

Online Live | 10 Hours | FDP Certification
499.00 (Inc. GST)

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This course provides a comprehensive introduction to data analytics with a focus on leveraging AI techniques. It covers the fundamentals of data analytics, including descriptive, predictive, and prescriptive methods, and introduces machine learning and AI’s role in the field. Participants will explore data preprocessing, visualization, and machine learning algorithms like clustering. The course emphasizes hands-on learning through Python, Jupyter Notebook, and AI libraries such as scikit-learn and TensorFlow. Advanced AI techniques, including deep learning and AI applications for big data, are covered alongside ethical considerations in AI-driven analytics.

Batch Date: 14th Nov’2024  to 19th Nov’2024

Duration : 10 hours

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.

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