Augmented Analytics

• Automated Data Analysis:

  • Uses AI and ML to automatically analyze data and generate insights.

• Natural Language Processing (NLP):

  • Allows users to query and interact with data using natural language.

• Predictive and Prescriptive Analytics:

  • Provides forecasts and recommendations based on historical data and trends.

• Automated Data Preparation:

  • Streamlines data cleaning, integration, and preparation tasks.

Before learning Augmented Analytics, you should have the following skills:

1. Basic Data Analytics:

   • Understanding of fundamental data analysis concepts and techniques.

2. Statistical Knowledge:

   • Basic knowledge of statistics and probability.

3. Data Manipulation:

   • Proficiency in handling and preparing data using tools like Excel, SQL, or Python.

4. Visualization Tools:

   • Familiarity with data visualization tools such as Tableau, Power BI, or similar.

By learning Augmented Analytics, you gain the following skills:

1. Advanced Data Analysis:

   • Proficiency in using AI and machine learning to automate and enhance data analysis.

2. Natural Language Processing (NLP):

   • Ability to interact with data using natural language queries.

3. Predictive and Prescriptive Analytics:

   • Skills in generating forecasts and recommendations based on data trends.

4. Automated Data Preparation:

   • Expertise in automating data cleaning, integration, and preparation tasks.