Introduction Data Technologies

• The Evolution of the Technology
• Importance of Data in Modern World 
• What is Data ?
• Data Technologies
• Data Roles
• What is SQL?

Installation

•  Oracle XE and SQL Developer

Using DDL Statements to Create and Manage Tables

•  Categorize the main database objects
•  Review the table structure
•  List the data types that are available for columns
•  Create a simple table
•  Modifying and Dropping tables
•  Explain how constraints are created at the time of table creation (basic intro)

Manipulating Data

•  Describe each data manipulation language (DML) statement
•  Insert rows into a table
•  Update rows in a table
•  Delete rows from a table
•  Control transactions

Retrieving and Filtering Data 

•  Explain the capabilities of SQL SELECT statements
•  Execute a basic SELECT statement
•  Filtering data with SQL WHERE clause

Restricting and Sorting Data

•  Limit the rows that are retrieved by a query
•  Sort the rows that are retrieved by a query

Using SingleRow Functions to Customize Output

•  Describe various types of functions available in SQL
•  Use character, number, and date functions in SELECT statements

Using Conversion Functions and Conditional Expressions

•  Describe various types of conversion functions that are available in SQL
•  Use the TO_CHAR, TO_NUMBER, and TO_DATE conversion functions
•  Apply conditional expressions in a SELECT statement (Case When, Decode)

Reporting Aggregated Data Using the Group Functions

•  Identify the available group functions
•  Describe the use of group functions
•  Group data by using the GROUP BY clause
•  Include or exclude grouped rows by using the HAVING clause

Displaying Data From Multiple Tables

•  Explain types of relationships between tables (one-to-one, one-to-many, many to-many)
•  Explain the role of PRIMARY and FOREIGN keys
•  Write SELECT statements to access data from more than one table using equijoins
•  Join a table to itself by using a selfjoin
•  View data that generally does not meet a join condition by using OUTER joins
•  Generate a Cartesian product of all rows from two or more tables

Using Subqueries to Solve Queries

•  Define subqueries
•  Describe the types of problems that the subqueries can solve
•  List the types of subqueries
•  Write singlerow and multiplerow subqueries
•  EXISTS and NOT EXISTS clauses

Using the SET Operators

• Describe set operators
• Use a set operator to combine multiple queries into a single query
• Control the order of rows returned

Creating Other Schema Objects

•  Create simple and complex views
•  Retrieve data from views
•  Create, maintain, and use sequences
•  Create and maintain indexes
•  Create private and public synonyms
•  Using DCL to control data: Grant and Revoke the privileges

Introduction to ETL

• Overview of ETL: What is Extract, Transform, Load?
• Importance of ETL in data integration.
• Key concepts: Source, Transformation, Destination.
• Batch extraction vs. Realtime extraction.
• Exploring various data sources (e.g., databases, APIs, files).
• ETL tools for data engineering (e.g., Apache NiFi, ODI).
• Loading data incrementally vs. full loads
• Hands-on exercise: Extracting data from a sample source.
• Hands-on exercise: Basic data transformation.
• Hands-on exercise: Loading transformed data into a destination.
• Hands-on exercise: Slowly Change Dimension

Exam

Introduction

• Introduction to python
• Data types
• Operators,operator precedence
• Introduction to functions
• Print() function
• Type conversion
• Variables
• Input() function
• Modules
• Errors

Sequences, Selection/Decision & Repetition stataments

• Introduction to string,list,tuples
• Operators -index,slice,escape,formatting
• Concatenation&Repetition
• Split(),Join() functions
• Iteration - for loops
• Range() function
• Loop accumulation
• Boolean values,expressions
• Conditional control structure
• Loop accumulation with conditionals

More programming constructs and data types

• String/list methods
• Files
• Dictionaries
• Loop accumulation with string/list/dictionaries
• Sets
• Tuples,tuple unpacking,enumerate(), * operator
• While loops

Advanced programming concepts and data manipulation techniques

• Functions
• Optional parameters
• Anonymous functions
• Sorted() function
• JSON
• Pickle
• Object serialization
• Nested iteration
• Map,filter,list comprehensions,zip
• Clean code principles
• Requests module
• Regex

Object oriented design,utilities and algorithms

• Classes
• Itertools
• Collections
• Introduction to algorithms
• More on algorithms

Numerical programming and data manipulation

• Multidimensional arrays
• Elementwise operations
• Math functions
• Jupyter
• Numpy
• Pandas

Statistical analysis and data exploration

• Introduction to probability
• Monte Carlo methods
• Statistics
• EDA

Exam

Introduction to Power BI 

• Familiarization with visualization tools
• Introduction to Power BI
• Advantages of using Power BI
• Workflow in Power BI
• Installing Power BI
• Overview of the general interface
• Loading initial data sources
• Introduction to data modeling
• Reviewing data
• Using the "Power Query Editor"

Data Operations Using Power Query Editor

• Using "Filter" (text and number filters)
• Managing rows
• Managing columns
• Creating "reference" and "duplicate" queries
• Changing data types
• Replacing values
• Managing header rows
• Using "Split Column"
• Managing data sources
• Using "Group By"
• Merging queries with "Merge Queries"
• Combining queries with "Append Queries"
• Using "Pivot Column"
• Using "Unpivot Columns"
• Determining row counts
• Changing data formats
• Performing basic calculations
• Conducting initial data analysis
• Indexing data
• Adding new columns
• Refreshing data

Data Modeling

• Advantages of modeling
• Understanding model relationships
• Managing model relationships
• Star schema
• Differences between fact and dimension tables
• Defining a relationship's cardinality and cross-filter direction
• Creating a common date table

Data Analysis Expressions (DAX)

• Syntax of DAX
• Operators in DAX
• DAX functions
• Usage of "Measure"
• Date functions
• Logical functions
• Text functions
• Aggregation functions
• Filter functions
• Creating single aggregation measures
• Creating a measure using quick measures
• Creating calculated tables

Visualization in Report View

• Principles of data visualization
• Creating initial visualizations
• Using "Tooltips"
• Utilizing "Slicer"
• Understanding synchronization
• Operations on charts
• Using a custom visual
• Applying and customizing a theme
• Configuring conditional formatting
• Configuring bookmarks
• Creating custom tooltips
• Editing and configuring interactions between visuals
• Grouping and layering visuals using the Selection pane
• Designing reports for mobile devices
• Incorporating the Q&A feature in a report
• Using AI visuals

Sharing Projects in Power BI Service

• Sharing projects via Power BI Service
• Sharing links to dashboards
• Delivering the final project
• Creating and configuring a workspace
• Configuring and updating a workspace app
• Creating dashboards
• Configuring subscriptions and data alerts
• Identifying when a gateway is required
• Configuring a dataset scheduled refresh
• Configuring row-level security group membership

Introduction to Data Modelling and Conceptual Design

• Purpose and Importance of Data Modelling
• Conceptual Modelling: Entities, Relationships, Attributes
• Types of Keys: Primary, Foreign, Composite, Surrogate
• Cardinality and Participation (Mandatory/Optional) Levels
• Hands-on Practice: Deriving entity relationships from a real-life example

Logical Design and Normalization

• Logical Modelling: Data types, field constraints, logical representation of relationships
• Creating and Reading ERDs (Entity Relationship Diagrams)
• Data Modelling Perspective with UML (Unified Modeling Language)
• Exercise: Transforming a conceptual model into a logical model
• Normalization Rules: 1NF, 2NF, 3NF, BCNF
• Practice: Step-by-step transformation of an unnormalized table

Advanced Modelling, Denormalization, and Physical Design

• Denormalization: Balancing performance and complexity
• Denormalization Preferences in OLTP and OLAP Systems
• Physical Modelling: Indexes, partitions, inter-table relationship structures
• Storage Formats and Hardware Considerations
• Impact of Modelling Decisions on Database Performance
• Final Hands-on Project: Creating and analyzing a full model flow from Conceptual → Logical → Physical

Core Concepts of Generative AI

• What is Generative AI?
• Concepts of Vectors and Embeddings
• Transformer Architecture and Basic Principles of LLMs
• Difference Between Generative and Discriminative Models
• Business Applications of LLMs
• Popular Models: ChatGPT, Claude, Gemini, LLaMA, Mistral

Prompting and Interaction with LLMs

• Prompt Engineering Techniques
• How Do Generative AI APIs Work? (OpenAI, Anthropic, Hugging Face)
• Making API Calls with Prompt Engineering
• Developing API-Based Solutions (Sample Application)
• Workshop / Hands-on Practice

Advanced Use Cases and Agent-Based Systems

• What is an Agent? Examples of LLM-Powered Agents
• Demo: Task Completion Using Agent Logic
• Retrieval-Augmented Generation (RAG): Creating Knowledge-Based Responses
• What is Fine-Tuning and When is It Needed?
• Local Models: Working with LM Studio, Ollama, LLaMA
• Applied Scenario: Connecting to a Knowledge Base with LLM Support

In this module, you will prepare for and complete a comprehensive final exam that assesses your understanding of the key concepts covered in the previous modules. This module will provide guidance on exam preparation strategies, review important topics, and offer practice exercises to ensure you are well-prepared to excel in the final assessment.