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Introduction to Data Analytics - Computer Analytics shown from Computer
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Fundamentals of Data Analytics Training Course

Formats ⟩  Live Virtual: 4 hrs./day, 2 Days  |  In-Person: 6 hrs./day, 2 Days

This intense 2-day course provides fundamental concepts of data analytics and teaches you how to apply data and analytics principles in your organization. You’ll learn about project lifecycles, the difference between data analytics, data science, and machine learning, building an analytics framework, and using analytics tools to draw business insights.

Recommended Follow-Up: Introduction to Business Analytics

Learning Objectives »

  • Learn to solve analytical problems.
  • Work with different types of data.
  • Apply data visualization.
  • Make effective business decisions and improve ROI.
  • Utilize charts, graphs, and tools for analytics.
  • Create an analytics adoption framework.
  • Identify upcoming trends in data analytics

Course Agenda

Data Analytics Overview

  1. Importance of Data Analytics
  2. Impact on Accounting
  3. Types of Data Analytics
  4. Benefits of Data Analytics

Types of Data

  1. Qualitative and Quantitative Data
  2. Data Levels of Measurement
  3. Normal Distribution of Data
  4. Statistical Parameters

Data Visualization

  1. Understanding Data Visualization
  2. Commonly Used Visualizations
  3. Frequency Distribution Plot
  4. Swarm Plot
  5. Data Visualization Tools
  6. Languages and Libraries
  7. Dashboard Based Visualization
  8. BI and Visualization Trends

Data Science and Machine Learning

  1. The Data Science Domain
  2. Machine Learning
  3. Data Science and Business Strategy
  4. Data Science Methodology
  5. Business and Analytic Approach
  6. Requirements and Collection
  7. Understanding and Preparation
  8. Modeling, Evaluation, and Deployment

Analytics Framework and Latest Trends

  1. Customer Analytics Framework
  2. Data Understanding and Preparation
  3. Modeling and Monitoring
  4. Latest Trends
  5. Graph Analytics
  6. Automated Machine Learning
  7. Open-Source Artificial Intelligence
Introduction to Data Analytics - Computer Analytics shown from Computer

Fundamentals of Data Analytics Training Course

Formats ⟩  Live Virtual: 4 hrs./day, 2 Days  |  In-Person: 6 hrs./day, 2 Days

This intense 2-day course provides fundamental concepts of data analytics and teaches you how to apply data and analytics principles in your organization. You’ll learn about project lifecycles, the difference between data analytics, data science, and machine learning, building an analytics framework, and using analytics tools to draw business insights.

Recommended Follow-Up: Introduction to Business Analytics

Learning Objectives »

  • Learn to solve analytical problems.
  • Work with different types of data.
  • Apply data visualization.
  • Make effective business decisions and improve ROI.
  • Utilize charts, graphs, and tools for analytics.
  • Create an analytics adoption framework.
  • Identify upcoming trends in data analytics

Course Agenda

Data Analytics Overview

  1. Importance of Data Analytics
  2. Impact on Accounting
  3. Types of Data Analytics
  4. Benefits of Data Analytics

Types of Data

  1. Qualitative and Quantitative Data
  2. Data Levels of Measurement
  3. Normal Distribution of Data
  4. Statistical Parameters

Data Visualization

  1. Understanding Data Visualization
  2. Commonly Used Visualizations
  3. Frequency Distribution Plot
  4. Swarm Plot
  5. Data Visualization Tools
  6. Languages and Libraries
  7. Dashboard Based Visualization
  8. BI and Visualization Trends

Data Science and Machine Learning

  1. The Data Science Domain
  2. Machine Learning
  3. Data Science and Business Strategy
  4. Data Science Methodology
  5. Business and Analytic Approach
  6. Requirements and Collection
  7. Understanding and Preparation
  8. Modeling, Evaluation, and Deployment

Analytics Framework and Latest Trends

  1. Customer Analytics Framework
  2. Data Understanding and Preparation
  3. Modeling and Monitoring
  4. Latest Trends
  5. Graph Analytics
  6. Automated Machine Learning
  7. Open-Source Artificial Intelligence
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