Learning Outcomes

• Create dynamic visualizations using a variety of Power BI tools and charts
• Apply conditional formatting to enhance data clarity and highlight key insights
• Use different types of visualizations, such as scatter plots, gauges, and more, to represent data effectively
• Present complex information clearly, ensuring visualizations are informative and not overwhelming for the audience

Learning Outcomes

• Connect and integrate various data sources into Power BI and Tableau
• Manipulate and transform data using Power Query, including merging and pivoting
• Design interactive dashboards that effectively communicate data insights
• Perform in-depth data analysis using DAX functions and calculated fields
• Publish and share professional-quality reports and dashboards

Learning Outcomes

• Perform exploratory data analysis using descriptive statistics to summarize and interpret data
• Understand key principles of sampling and design randomized controlled experiments to gather data effectively
• Apply probability concepts and recognize the significance of sampling distributions and the Central Limit Theorem
• Conduct regression analysis and identify relationships between variables
• Select and apply appropriate tests of significance in various contexts, including resampling and multiple comparisons

Learning Outcomes

• Understand the fundamentals of Linux operating systems.
• Execute basic and advanced Linux commands.
• Navigate and manage files in Linux.
• Learn SQL for querying and managing databases.
• Apply SQL for data retrieval and manipulation.

Learning Content

• Introduction to Linux and Shell Commands
• File System Navigation and Permissions
• Process and User Management
• SQL Basics and Database Operations
• Advanced SQL Queries and Data Manipulation
• Hands-on Projects and Exercises

Learning Outcomes

• Describe key capabilities and components of SQL Server on Linux.
• Deploy SQL Server on Linux.
• Install additional optional components to SQL Server.
• Configure SQL Server on Linux.
• Connect and manage SQL Server images on Linux.
• Migrate databases from other database systems to SQL Server on Linux and provide disaster recovery.
• Understand high availability features of SQL Server on Linux.

Learning Content

• Introduction to SQL Server on Linux
• Installation and Configuration
• Database Administration
• Security and Authentication
• Performance Tuning and Monitoring
• High Availability and Disaster Recovery
• Hands-on Projects and Exercises

Learning Outcomes

• Understand the end-to-end machine learning pipeline.
• Preprocess and clean datasets for machine learning models.
• Train and evaluate different ML models.
• Optimize models for better performance.
• Deploy machine learning models in real-world applications.

Learning Content

• Introduction to Machine Learning Pipelines
• Data Preprocessing & Feature Engineering
• Supervised and Unsupervised Learning
• Model Evaluation & Optimization
• Machine Learning Deployment
• Hands-on Projects with Python

Learning Outcomes

• Understand fundamental statistical modeling concepts.
• Apply various statistical models using R.
• Analyze and interpret model outputs.
• Use R for regression analysis and hypothesis testing.
• Develop hands-on experience with real-world datasets.

Learning Content

• Introduction to Statistical Modeling
• Regression Analysis (Linear & Logistic)
• Hypothesis Testing & Statistical Inference
• Model Evaluation & Validation
• Data Visualization in R
• Hands-on Projects and Case Studies

Learning Outcomes

• Understand the fundamentals of effective feedback.
• Learn strategies for providing constructive feedback.
• Handle difficult conversations with confidence.
• Foster a feedback-driven work environment.
• Develop active listening and response techniques.

Learning Content

• Introduction to Feedback Principles
• The Psychology of Feedback
• Constructive vs. Destructive Feedback
• Effective Feedback Models
• Handling Difficult Conversations
• Hands-on Exercises and Case Studies

Learning Outcomes

• Understand the fundamentals of reinforcement learning.
• Learn how human feedback improves AI training.
• Implement policy optimization techniques.
• Apply RL concepts to real-world applications.
• Explore ethical considerations in RL from human feedback.

Learning Content

• Introduction to Reinforcement Learning
• Role of Human Feedback in RL
• Policy Optimization Techniques
• Practical Applications of RLHF
• Ethical Considerations in AI Training
• Hands-on Projects and Case Studies

Learning Outcomes

• Understand statistical methods for data analysis.
• Apply machine learning techniques for predictive modeling.
• Use Python and R for data science workflows.
• Perform hypothesis testing and regression analysis.
• Build and evaluate machine learning models.

Learning Content

• Introduction to Data Science and Statistics
• Exploratory Data Analysis
• Hypothesis Testing & Statistical Inference
• Regression & Classification Models
• Machine Learning Algorithms & Applications
• Hands-on Projects in Python and R