Learning Outcomes

• Install and Configure RapidMiner: Set up and customize RapidMiner for optimal use
• Prepare and Visualize Data: Clean and visualize data within RapidMiner for effective analysis
• Validate Machine Learning Models: Ensure the accuracy and reliability of predictive models
• Create and Deploy Predictive Models: Mashup data and operationalize predictive analytics within business processes
• Troubleshoot and Optimize: Resolve issues and fine-tune RapidMiner for enhanced performance

Learning Outcomes

• Learn new concepts from industry experts
• Gain a foundational understanding of a subject or tool
• Develop job-relevant skills with hands-on projects

Learning Outcomes

• Understand the Impact of Big Data: Analyze key use cases, tools, and processing methods used in big data management
• Describe Apache Hadoop Ecosystem: Explain Hadoop architecture and work with its core components, including Hive, HDFS, HBase, Spark, and MapReduce
• Apply Spark Programming: Develop basic parallel programming skills using DataFrames, datasets, and Spark SQL
• Optimize Spark Operations: Use Spark’s RDDs and datasets, optimize Spark SQL with Catalyst and Tungsten, and explore Spark’s development and runtime options

Learning Outcomes

• Describe the R programming language and its programming environment.
• Explain fundamental concepts like functions, variables, data types, pipes, and vectors.
• Generate visualizations in R.
• Demonstrate formatting in R Markdown for structuring content.
• Understand R packages, including Tidyverse.
• Work with data frames and use R for data cleaning and organization.

Learning Content

• Introduction to R and RStudio
• Fundamental programming concepts in R
• Working with data frames
• Data visualization in R
• R Markdown for documentation
• Hands-on projects and quizzes

Learning Outcomes

• Perform data wrangling and cleaning using Python.
• Apply statistical analysis to extract insights from data.
• Visualize data using Matplotlib and Seaborn.
• Build predictive models using machine learning techniques.

Learning Content

• Python basics for data analysis
• Data manipulation with Pandas
• Data visualization techniques
• Introduction to machine learning for data analysis

Learning Outcomes

• Understand the Tableau Certified Data Analyst exam format.
• Connect to and prepare data for analysis.
• Create interactive visualizations and dashboards.
• Apply calculations, parameters, and filters effectively.
• Interpret and communicate data insights using Tableau.

Learning Content

• Introduction to Tableau Certification
• Connecting to Data Sources
• Creating Charts & Graphs
• Building Dashboards & Stories
• Applying Calculations & Functions
• Exam Strategies & Practice Questions

Learning Outcomes

• Understand core concepts of machine learning.
• Implement supervised and unsupervised learning algorithms.
• Utilize Python libraries such as Scikit-learn for ML tasks.
• Evaluate model performance and optimize models.
• Apply machine learning techniques to real-world problems.

Learning Content

• Introduction to Machine Learning
• Data Preprocessing & Feature Engineering
• Supervised Learning (Regression & Classification)
• Unsupervised Learning (Clustering & Dimensionality Reduction)
• Model Evaluation & Optimization
• Hands-on Projects with Python

Learning Outcomes

• Understand the fundamentals of predictive modeling.
• Apply Python for data preprocessing and feature engineering.
• Build and evaluate predictive models using Scikit-learn.
• Optimize models for better performance.
• Develop hands-on experience with real-world datasets.

Learning Content

• Introduction to Predictive Modeling
• Data Cleaning & Preprocessing
• Regression & Classification Models
• Model Evaluation Techniques
• Feature Engineering & Optimization
• Hands-on Projects with Python

Learning Outcomes

• Understand big data concepts and architectures.
• Learn Hadoop ecosystem components, including HDFS and MapReduce.
• Implement big data processing with Apache Spark.
• Work with Spark RDDs, DataFrames, and Spark SQL.
• Optimize and troubleshoot big data workflows.

Learning Content

• Introduction to Big Data and Hadoop
• Hadoop Distributed File System (HDFS)
• Apache Spark and its components
• Data Processing with Spark SQL
• Machine Learning with Spark MLlib
• Hands-on projects using Hadoop and Spark

Learning Outcomes

• Design a dashboard blueprint.
• Apply data discovery techniques using Tableau.
• Build an interactive dashboard using Tableau.

Learning Content

• Introduction to Tableau
• Data Discovery Techniques
• Dashboard Design Principles
• Hands-on Dashboard Creation

Learning Outcomes

• Python Proficiency: Master Python from basics to advanced data analysis tools.
• Industry 4.0 Knowledge: Understand key concepts in digitization and Industry 4.0.
• Decision-Making: Develop skills to make informed tech decisions for digital transformation.
• Practical Implementation: Learn to apply Industry 4.0 solutions in real-world scenarios.
• Current Trends Awareness: Stay updated on Industry 4.0 research and challenges.

Learning Content

• Introduction to Python
• Data Analysis with Python
• Industry 4.0 Technologies
• Digital Transformation Strategies

Learning Outcomes

• Understand machine teaching and its role in autonomous AI systems.
• Learn how subject matter experts (SMEs) contribute to AI training.
• Evaluate the pros and cons of leveraging human expertise in AI system design.
• Differentiate between automated and autonomous decision-making systems.
• Identify use cases where autonomous AI outperforms humans and automated systems.
• Develop an autonomous AI solution to a real-world problem.
• Validate AI designs against existing expertise and problem-solving techniques.

Learning Content

• Module 1: Introduction to Machine Teaching and Autonomous AI
• Module 2: Analyzing the problem
• Module 3: Learning the solution
• Module 4: Storytelling

Learning Outcomes

• Learn from well-known faculty and industry experts from around the globe.
• Flexibility of attending anytime from anywhere, even when you are working full-time.
• Interact with industry experts during the webinars and get the latest updates/announcements on the subject.
• Experience a global learning with students from various backgrounds and experience which is a great networking opportunity.
• Get practical skills and knowledge in the fundamentals of programmable logic controllers (PLCs) and SCADA systems.
• Study a wide variety of topics related to PLC & SCADA like- software configuration, installation, troubleshooting and network security issues, PLC hardware and installation selection criteria and writing PLC programs using ladder logic.
• Learn fundamentals of PLC software, Advance Control System and SCADA network security system.

Learning Content

• Introduction to PLCs and SCADA
• PLC Hardware and Installation
• SCADA Network Security
• Hands-on Projects

Learning Outcomes

• Explain the importance of data warehousing in business intelligence.
• Design and implement effective schema designs for data warehouses.
• Implement ETL processes to efficiently load and transform data into a data warehouse.
• Apply performance optimization techniques to enhance the efficiency and responsiveness of data warehouse systems.

Learning Content

• Introduction to Data Warehousing
• Schema Design
• ETL Processes
• Performance Optimization Techniques

Learning Outcomes

• Understand SCADA system architecture and components.
• Develop and configure SCADA applications.
• Integrate PLCs with SCADA systems.
• Implement real-time data monitoring and control.
• Design Human-Machine Interface (HMI) applications.

Learning Content

• Introduction to SCADA Systems
• Communication Protocols in SCADA
• PLC and SCADA Integration
• Real-time Data Acquisition and Control
• HMI Development and Visualization
• Security and Maintenance of SCADA Systems