Description

Introduction:
The AI & Data Analytics certification equips professionals with essential skills to harness the power of artificial intelligence and advanced data analytics. This program covers key concepts, including AI-driven data models, machine learning, and visualization techniques, empowering participants to unlock business insights and support data-driven strategies. Through practical scenarios, candidates will gain hands-on experience with predictive analytics, dashboards, and decision-support tools, preparing them to address complex challenges using AI and analytics.

The certification emphasizes best practices for integrating AI and analytics into business operations, ensuring participants can optimize processes and enhance organizational performance. By mastering these skills, professionals will be able to design effective analytics solutions that align with business goals and promote data-driven innovation.

Prerequisites:

1. Basic Knowledge of Data Analytics Tools:
   Familiarity with data visualization and AI tools is recommended.
2. Understanding of Business Intelligence Concepts:
   Knowledge of KPIs, performance metrics, and forecasting methods is beneficial.
3. Experience with Data or AI Models:
   Prior experience in data analysis or working with machine learning models will enhance learning.
4. Analytical Skills:
   Ability to interpret data, build insights, and develop AI-driven solutions.
5. Technical Skills (Optional):
   Basic knowledge of Python, SQL, or data modeling can be advantageous.

TABLE OF CONTENT

1: Introduction to AI & Data Analytics
1.1. Overview of Artificial Intelligence (AI)
1.2. Role of Data Analytics in Decision Making
1.3. Importance of AI & Data Analytics in Various Industries

2: Foundations of Data
2.1. Understanding Data Types and Structures
2.2. Data Collection and Cleaning
2.3. Exploratory Data Analysis (EDA)

3: Basics of Statistics
3.1. Descriptive Statistics(Ref: Artificial Intelligence and Machine Learning with KNIME Analytics)
3.2. Inferential Statistics
3.3. Probability Distributions

4: Machine Learning Overview
4.1. Introduction to Machine Learning
4.2. Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
4.3. Applications of Machine Learning in Real-world Scenarios

5: Data Preprocessing
5.1. Feature Scaling and Normalization
5.2. Handling Missing Data
5.3. Encoding Categorical Data

6: Model Building
6.1. Selecting and Evaluating Models
6.2. Model Training and Testing
6.3. Model Deployment

7: Introduction to Deep Learning
7.1. Neural Networks and Deep Learning
7.2. Deep Learning Applications
7.3. Overview of TensorFlow and PyTorch(Ref: Hands-On Generative AI: Building Models with TensorFlow and PyTorch)

8: Data Visualization
8.1. Importance of Data Visualization
8.2. Tools for Data Visualization (e.g., Matplotlib, Seaborn, Tableau)

9: Big Data Analytics
9.1. Introduction to Big Data
9.2. Hadoop and MapReduce
9.3. Apache Spark for Big Data Processing

10: AI Ethics and Bias
10.1. Ethical Considerations in AI & Data Analytics
10.2. Addressing Bias in Data and Algorithms

Conclusion:
This certification prepares professionals to leverage AI and analytics to drive data-driven decisions and optimize business processes. By mastering these skills, candidates can contribute to innovation, improve outcomes, and align insights with strategic goals.

Reference