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AI for Data Analytics

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Overview

"AI for Data Analytics" explores the transformative role of artificial intelligence in revolutionizing how data is collected, processed, and analyzed. As data continues to grow exponentially, traditional analytics methods often fall short in extracting timely, accurate, and actionable insights. This book delves into how AI technologies—such as machine learning, natural language processing, and neural networks—enhance data analytics by enabling automation, predictive modeling, anomaly detection, and real-time decision-making. The content is structured to guide readers through the fundamentals of AI, progressing toward...

Syllabus

Introduction to AI in Data Analytics Overview of AI & Data Analytics

  • Understanding AI and its applications in data analytics
  • Benefits of using AI for data analysis

Introduction to AI Tools

  • Overview of popular AI tools and platforms (e.g. IBM Watson, Google AI, Tableau, Microsoft Azure AI)

Data Collection & Preparation Data Sources & Collection Methods

  • Identifying various data sources
  • Using AI tools to collect data from different platforms

Data Cleaning & Preprocessing

  • Automated data cleaning techniques
  • Handling missing data and outliers using AI tools

Exploratory Data Analysis (EDA) Understanding Your Data

  • Using AI tools to generate summary statistics
  • Visualizing data distributions and relationships

Advanced EDA Techniques

  • Automated pattern and trend detection
  • AI-driven feature selection and engineering

Data Visualization Creating Visualizations

  • Using AI tools to create charts, graphs, and dashboards
  • Best practices for data visualization

Interactive Dashboards

  • Building interactive dashboards with AI tools
  • Customizing dashboards to meet specific needs

Predictive Analytics & Modeling Introduction to Predictive Modeling

  • Understanding regression, classification, and clustering
  • Using AI tools to build predictive models

Model Evaluation & Validation

  • Automated model evaluation techniques
  • Understanding metrics and performance evaluation

Application of AI in Various Domains Financial Data Analysis

  • Case studies and applications in financial forecasting

Marketing Data Analysis

  • Analyzing customer behavior and market trends

Healthcare Data Analysis

  • Applications in patient data analysis and medical research

Advanced AI Techniques Natural Language Processing (NLP)

  • Using AI for text analysis and sentiment analysis

Time Series Analysis

  • Automated time series forecasting with AI tools

Capstone Project Project Planning & Execution

  • Defining a project scope and objectives

Applying Learned Skills

  • Using AI tools to complete a comprehensive data analysis project

Presentation & Reporting

  • Presenting findings using AI-generated reports and visualizations
AI for Data Analytics
Go to Class

via Noble Desktop

12 hours

Certificate Available

English

On-Demand

Intermediate

Instructor

Noble desktop

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