1.1 Introduction to AI

• What is Artificial Intelligence?
• AI vs. Automation vs. Analytics: Understanding the distinctions
• Types of AI: Narrow AI, General AI, and Super AI
• Real-world examples of AI in action

1.2 AI Family Tree

• AI, Machine Learning (ML), and Deep Learning (DL): Relationships and differences
• Supervised vs. Unsupervised vs. Reinforcement Learning (brief overview)
• Neural Networks: The building blocks

1.3 History & Evolution of AI

• Key milestones: From Turing Test to AlphaGo to ChatGPT
• AI winters and breakthroughs
• Current state of AI

1.4 Core AI Applications

• Natural Language Processing (NLP): chatbots, translation, sentiment analysis
• Computer Vision (CV): facial recognition, object detection, medical imaging
• Robotics & Autonomous Systems
• Predictive Analytics: forecasting, recommendation systems

1.5 AI Across Industries

• HR: Recruitment screening, employee engagement analysis
• Operations: Supply chain optimization, predictive maintenance
• Healthcare: Diagnosis assistance, drug discovery
• Retail: Personalization, inventory management
• Education: Adaptive learning, automated grading
• Finance: Fraud detection, algorithmic trading
• Manufacturing: Quality control, process automation

1.6 Future of AI

• Emerging trends: Multimodal AI, Edge AI, Quantum ML
• Opportunities: Productivity gains, innovation acceleration
• Risks: Job displacement, privacy concerns, weaponization

2.1 Introduction to Generative AI

• What is Generative AI? Definition and characteristics
• Traditional AI (predictive) vs. Generative AI (creative)
• Everyday GenAI applications: Writing assistants, image generators, code completion
• Popular GenAI tools: ChatGPT, Claude, Gemini, DALL-E, Midjourney

2.2 How GenAI Works

• Large Language Models (LLMs) explained simply
• Introduction to Transformers: Attention mechanism in plain language
• Tokens and tokenization
• Training vs. Fine-tuning vs. Inference
• Parameters and model size (7B, 70B, 405B – what do they mean?)

2.3 Key GenAI Models Overview

• OpenAI GPT family (GPT-4, GPT-4o, o1)
• Anthropic Claude (Sonnet, Opus, Haiku)
• Google Gemini
• Meta LLaMA
• Open-source vs. Closed-source models
• Strengths and limitations of each

2.4 Recent Trends & Important Concepts

• Multimodal AI (text + image + audio + video)
• Context windows and long-form understanding
• Retrieval-Augmented Generation (RAG)
• AI reasoning models (o1, o3)
• Real-time AI and streaming responses

2.5 Prompt Engineering Fundamentals

• What is a prompt?
• Anatomy of a good prompt: Role, Task, Context, Format, Constraints
• Text generation and completion
• Summarization: Short vs. detailed summaries
• Rewriting: Tone adjustment (formal, casual, persuasive, empathetic)
• Translation and localization

2.6 Prompt Structures

• Instructional prompts
• Role-based prompts (“Act as a…”)
• Template-based prompts
• Structured output requests (JSON, tables, lists)

2.7 Core Prompting Techniques

• Zero-shot prompting: Direct instructions
• Few-shot prompting: Learning from examples
• Chain of Thought (CoT): Step-by-step reasoning
• Tree of Thoughts (ToT): Exploring multiple reasoning paths
• Self-consistency: Multiple attempts for better answers

2.8 Multi-turn Interactions

• Context retention and conversation memory
• Building on previous responses
• Clarification and refinement loops

2.9 Limitations & Quality Evaluation

• Hallucinations: What they are and how to spot them
• Bias in AI outputs
• Factual accuracy verification
• Evaluating output quality: Relevance, coherence, accuracy, creativity
• When to use AI vs. human judgment

3.1 LLM Deep Dive

• How transformers process language
• Attention mechanisms visualized
• Pre-training and next-token prediction
• Temperature and sampling parameters
• Top-p, Top-k sampling explained

3.2 LLMs vs. Small Language Models (SLMs)

• When to use LLMs vs. SLMs
• Edge deployment and efficiency
• Cost considerations

3.3 Popular LLM Comparison

• GPT-4: Strengths in reasoning and creativity
• Claude: Long context and nuanced understanding
• Gemini: Multimodal capabilities
• LLaMA: Open-source flexibility
• Use case mapping: Which model for which task?

3.4 Advanced Prompting Techniques

• Self-consistency prompting
• Instruction tuning and prompt optimization
• Negative prompting (what to avoid)
• Constraint-based prompting
• Persona switching and style guides
• Prompt chaining for complex tasks

3.5 Context Management

• Working with large documents
• Context window limitations and strategies
• Summarization for context compression
• Conversation history management

3.6 Introduction to Embeddings

• What are embeddings?
• Vector representations of text
• Semantic search applications
• Use cases: Document similarity, recommendation systems

3.7 Fine-tuning Basics

• What is fine-tuning?
• When to fine-tune vs. prompt engineer
• Transfer learning concept
• No-code fine-tuning platforms (brief overview)

4.1 Introduction to Agentic AI

• What is Agentic AI?
• Key characteristics: Autonomy, goal-directedness, adaptability
• Agentic AI vs. Traditional AI workflows
• Real-world examples: Research agents, customer service agents, coding agents

4.2 AI Agents vs. Chatbots

• Chatbots: Reactive, scripted interactions
• AI Agents: Proactive, goal-oriented, tool-using
• Comparison matrix

4.3 Components of Agentic AI

• Memory: Short-term and long-term context retention
• Planning: Breaking down goals into sub-tasks
• Tool Use: API calls, web search, code execution, database queries
• Autonomy: Self-directed task completion
• Reflection: Self-evaluation and improvement

4.4 Agent Frameworks Overview

• LangChain: Modular components for agent building
• AutoGen: Multi-agent conversations
• CrewAI: Role-based agent collaboration
• OpenAI Assistants API: Built-in agent capabilities
• Comparison and use case mapping

4.5 AI Orchestration

• What is orchestration?
• Workflow design for agent tasks
• Human-in-the-loop (HITL) integration
• Error handling and fallback strategies

5.1 No-Code AI Overview

• What is no-code/low-code?
• Benefits: Speed, accessibility, cost-effectiveness
• Limitations: Customization constraints, vendor lock-in
• When to use no-code vs. custom development

5.2 Workflow Automation Fundamentals

• Triggers, actions, and conditions
• API integrations basics
• Data mapping and transformation
• Error handling and testing

5.3 Zapier

• Platform overview and interface
• Building your first Zap
• Multi-step workflows
• AI-powered Zaps with ChatGPT integration
• Filters, formatters, and utilities
• Best practices and optimization
• Hands-on: Automate email-to-task workflow

5.4 Make.com

• Visual workflow builder
• Modules, routes, and scenarios
• Advanced routing and error handling
• Data stores and aggregators
• Scheduling and webhooks
• Hands-on: Build a content aggregation workflow

5.5 n8n

• Self-hosted automation (overview)
• Node-based workflow design
• Custom code nodes
• Integrations and credentials management
• Hands-on: Create a Slack notification system

5.6 Notion AI

• AI writing and editing
• Database automation
• Template creation with AI
• Q&A over workspace knowledge
• Hands-on: Build an AI-powered knowledge base

5.7 Airtable

• AI-powered data management
• Automation with AI fields
• Integration with other tools
• Building mini-apps
• Hands-on: Create a project tracker with AI summaries

5.8 Glide

• No-code app builder
• AI-powered features
• Data binding and workflows
• Mobile app prototyping
• Hands-on: Build a simple internal tool

5.9 Canva AI

• Text-to-design generation
• AI brand kit creation
• Magic Resize and Magic Eraser
• AI-powered content suggestions
• Hands-on: Create a presentation with AI

5.10 Tome AI

• AI-powered storytelling
• Auto-generating narratives
• Interactive presentations
• Hands-on: Build a pitch deck

5.11 Gamma.app

• AI slide deck creation
• One-click formatting
• Collaborative editing
• Hands-on: Generate a training module

5.12 Integrating AI into Business Processes

• Identifying automation opportunities
• Workflow mapping and optimization
• Change management considerations
• Measuring ROI of automation
• Building a toolkit for your role

6.1 Agent Design Principles

• Defining agent goals and scope
• Task decomposition
• User experience considerations
• Edge case handling

6.2 No-Code Agent Platforms

• Relevance AI: Building AI teams
• Stack AI: Workflow-based agents
• Flowise: Visual LLM app builder
• Voiceflow: Conversational agents
• Platform comparison and selection

6.3 Building Your First Agent

• Step-by-step agent creation
• Defining instructions and personality
• Adding tools and integrations
• Knowledge base integration (RAG)
• Testing and iteration

6.4 Practical: AI Research Assistant Agent

• Goal: Automated web research and summarization
• Components:
  1. Web search capability
  2. Content extraction
  3. Summarization
  4. Report generation
  5. Email delivery
• Tools: n8n/Make + GPT API + Google Docs
• Build process:
  1. Design the workflow
  2. Set up web search module
  3. Configure summarization
  4. Create output template
  5. Add scheduling
  6. Test and refine

6.5 Advanced Agent Features

• Memory and context persistence
• Multi-tool orchestration
• Conditional logic and branching
• User input handling
• Feedback loops

6.6 Deployment & Monitoring

• Publishing your agent
• Usage analytics
• Performance monitoring
• Iterative improvement
• Cost management

7.1 AI Challenges

Bias

• What is AI bias?
• Sources: Training data, algorithm design, human feedback
• Types: Gender, racial, cultural, socioeconomic
• Real-world examples and consequences
• Detection and mitigation strategies

Hallucinations

• What are hallucinations?
• Why LLMs hallucinate
• Identifying hallucinations
• Mitigation: Verification, grounding, confidence scoring

Privacy & Security

• Data privacy concerns
• Prompt injection attacks
• Data leakage risks
• Secure AI usage practices
• Compliance considerations (GDPR, CCPA)

Misinformation & Deepfakes

• AI-generated content detection
• Deepfake risks
• Watermarking and provenance
• Media literacy in AI age

7.2 Responsible AI Principles

• Fairness: Eliminating discriminatory outcomes
• Accountability: Clear ownership and responsibility
• Transparency: Explainability and disclosure
• Explainability: Understanding AI decisions
• Privacy: Data protection and consent
• Safety & Security: Robustness and resilience
• Human Control: Human-in-the-loop systems

7.3 Global AI Ethics Frameworks

OECD AI Principles

• Inclusive growth and sustainability
• Human-centered values
• Transparency and explainability
• Robustness and safety
• Accountability

EU AI Act

• Risk-based approach
• Prohibited AI practices
• High-risk AI systems
• Transparency obligations
• Compliance requirements

NITI Aayog (India)

• #AIForAll vision
• Responsible AI strategy
• Focus on explainability and fairness
• Sector-specific guidelines

UNESCO Recommendation on AI Ethics

• Human rights and dignity
• Environmental sustainability
• Cultural diversity
• Gender equality

Microsoft Responsible AI

• Six principles framework
• Impact assessments
• Governance structure
• Practical tools

7.4 Safe & Responsible Usage

Verifying AI Outputs

• Cross-referencing with trusted sources
• Fact-checking methodologies
• Using multiple AI models for comparison
• Critical evaluation frameworks

Human-in-the-Loop (HITL) Systems

• When to require human oversight
• Designing HITL workflows
• Balancing automation and control
• Decision authority frameworks

Trust & Compliance

• Building organizational AI policies
• Training and awareness programs
• Audit trails and documentation
• Incident response plans

7.5 Responsible AI Guardrails

• Input validation and filtering
• Output moderation
• Rate limiting and abuse prevention
• Content policies and guidelines
• Monitoring and alerting systems

7.6 Case Studies of AI Misuse

• Amazon’s biased recruiting tool
• COMPAS recidivism algorithm
• Deepfake political videos
• ChatGPT jailbreaks and prompt injection
• Lessons learned and best practices

7.7 Building Ethical AI Culture

• Organizational responsibilities
• Individual accountabilities
• Ethical decision-making frameworks
• Continuous learning and adaptation

• Python Revision (NumPy, Pandas, scikit learn, matplotlib)
• What is Machine Learning?
• Statistical Learning vs Machine Learning
• Machine Learning Use-Cases
• Machine Learning Process Flow
• Machine Learning Categories

• Accuracy
• Precision
• Recall
• True Positive Rate / False Positive Rate
• F1-Score
• Confusion Matrix
• ROC Curve
• Area Under the ROC curve
• Mean Absolute Error (MAE)
• Mean Squared Error (MSE)
• R2 Score

• What is Regression and its use cases?
• Linear Regression
• Polynomial Regression
• Support Vector Regression (SVR)
• Decision Tree Regression – ID3 and CART

• What is Classification and its use cases?
• Logistic Regression
• Support Vector Machine (SVM)
• Kernel SVM
• Naïve Bayes
• k-Nearest Neighbours (k-NN)
• Decision Tree Classification – ID3 and CART Algorithms
• Ensemble Learning
• Bagging vs Boosting
• AdaBoost, Gradient Boost, XGBoost
• Random Forest

• What is model tuning?
• Overfitting vs Underfitting
• Cross Validation
• Bias and Variance
• Hyperparameter Optimization
• Grid Search vs Random Search

• What is Dimensionality Reduction?
• Feature Selection
• Principal Component Analysis (PCA)
• Linear Discriminant Analysis (LDA)
• Random Projection
• ICA

• What is Clustering & its Use Cases?
• K-Means Clustering
• K-Means++
• How to do optimal clustering – Elbow Curve?
• C-means Clustering
• Hierarchical Clustering
• DBSCAN Clustering
• Gaussian Mixture Model
• Clustering Model Evaluation Metrics – Silhouette Coefficient, Dunn’s Index, etc.

• What are Association Rules?
• Association Rule Parameters
• Calculating Association Rule Parameters
• Recommendation Engines
• How Recommendation Engines work?
• Collaborative Filtering
• Content Based Filtering

• What is Reinforcement Learning?
• Why Reinforcement Learning?
• Elements of Reinforcement Learning
• Exploration vs. Exploitation dilemma
• Epsilon Greedy Algorithm
• Markov Decision Process (MDP)
• Q values and V values
• Q – Learning
• Values

• What is Time Series Analysis?
• Importance and Components of TSA
• White Noise
• AR model
• MA model
• ARMA model
• ARIMA model
• Stationarity

• Intro to NLP
• Setting up NLTK environment
• What is NLTK Toolkit?
• Text Processing
  • Cleaning
  • Normalization
  • Tokenization
  • Stop Word Removal
  • Part of Speech Tagging
  • Named Entity Recognition
  • Stemming
  • Lemmatization
• Feature Extraction
  • Bag of Words Model
  • Count Vectorizer
  • Term Frequency (TF)
• Inverse Document Frequency (IDF)
  • Word Embeddings

• What is Deep Learning?
• Curse of Dimensionality
• Machine Learning vs. Deep Learning
• Use cases of Deep Learning
• Human Brain vs. Neural Network
• What is Perceptron?
• Learning Rate
• Epoch
• Batch Size
• Activation Function
• Single Layer Perceptron

• Intro to Descriptive Statistics
• Measures of Center: Average, Mean, Median, Mode
• Measures of Dispersion: Range, Quartiles and Percentiles, Inter Quartile Range (IQR), Variance, Standard Deviation
• Covariance and Correlation
• Simpson’s Paradox

• Normal Distribution
• Standard Normal Distribution
• T- Distribution
• Point Estimation
• Confidence Intervals
• P-Value
• Hypothesis testing
• Levels of Hypothesis Testing

• Uses of Probability
• Need of Probability
• Basics of Probability
• Binomial Distribution
• Conditional Probability
• Bayesian Inference
• Bayes Rule

• Linear Regression Analysis
• Logistic Regression Analysis
• Problem of Collinearity
• WOE and IV
• Residual Analysis
• Heteroscedasticity
• Homoscedasticity

• Interpreted vs Compiled language
• Introduction to Python
• Advantages & Disadvantages of Python
• Python Installation (Anaconda)
• Applications of Python
• “Hello World!” in Python

• Variables
• Keywords
• Multiple Assignment
• What are Datatypes?
• Numbers
• Strings
• Indexing and Slicing
• String Functions
• Lists
• List Functions
• Dictionary
• Dictionary Functions
• Tuples
• Sets
• Type Conversion (Str, Int, Float, etc.)
• Date & Time (Date, Time and DateTime classes in Python)
• Operators (Arithmetic, Comparison, Logical, Assignment, Binary, Identity, Membership Operators)

• If-Else statements
• Relational Operators
• Nested If/Else
• Elif Statements
• Logical Operators
• For Loops
• While Loops
• Nested Loops
• Pass, Break and Continue

• User-defined functions
• Unlimited parameters
• Global and Local variables
• Lambda functions
• Map function
• Sort function
• Error and Exception Handling

• List Comprehensions
• Dictionary Comprehensions
• Set Comprehensions
• Tuple Comprehensions (doesn’t exist)

• Classes and Objects
• Methods in Classes
• Class Attributes
• Instance Attributes
• Inheritance
• Overriding Methods

• Creating a file
• Reading a file
• Writing a file
• Appending to file
• Copying file

• Intro to requests
• Intro to BeautifulSoup
• Parsing HTML structure into soup
• Data extraction from soup/HTML
• Wikipedia scraping
• News website scraping
• Maps scraping

• What is a database?
• What is SQL (Structure Query Language)?
• Introduction to MySQL
• Installing MySQL
• Connection MySQL with Python
• CRUD operations

• What is multi-threading?
• Introduction to parallel processing
• Intro to threads
• Threading module
• Creating threads using threading module
• Synchronizing threads
• Threads priority queue

• Working with NumPy
• Working with Pandas
• DataFrames
• Analyzing a dataset using NumPy and pandas

• Intro to Natural Language Processing
• Installing NLTK toolkit
• Working with NLTK Toolkit
• Sentence Segmentation
• Word Tokenization
• Parts-of-Speech Tagging
• Lemmatization
• Stop-word removal
• Named Entity Recognition (NER)
• Bag of words model
• TF-IDF (Term Frequency – Inverse Document Frequency)
• ML modelling

• Power BI Installation
• Power BI Desktop
• Data Imports into Power BI
• Views in Power BI
• Building a Dashboard
• Publishing Reports
• Creating Dashboard in Power BI

• Power Query
• Power Pivot
• Power View
• Power Map
• Power BI Service
• Power BI & QA
• Data Management Gateway
• Data Catalog

• Connect to DataSources
• Power BI SQL Server Integration
• Power BI MySQL Integration
• Power BI Excel Integration
• Python Integration with Power BI Desktop
• Power BI Web Integration using APIs
• Power BI Salesforce Integration
• Power BI Microsoft Integration

• Change Data type of column
• Clean and Transform using Query Editor
• Advanced Data sources and transformations
• Cleaning irregularly formatted data
• Combine Multiple tables
• Clusters
• Format dates
• Groups
• Hierarchies
• Joins
• Pivot table
• Query Groups
• Split Columns
• Unpivot table

• How Data Model Looks Like
• Database Normalization
• Data Tables vs Lookup Tables
• Primary Key vs Foreign Key
• Relationships vs Merged Table
• Creating Table Relationships
• Creating Snowflake Schemas
• Managing & Editing Relationships
• Active vs Inactive Relationships
• Relationship Cardinality
• Many to Many
• One to One
• Connecting Multiple Data Tables
• Filter Flow

• Introduction to DAX
• DAX calculation types
• Aggregate Functions
• Date Functions
• Logical Functions
• String Functions
• Trigonometric Functions

• Area Chart
• Bar chart
• Card
• Clustered Bar chart
• Clustered Column chart
• Donut chart
• Funnel chart
• Heat Map
• Line Chart
• Clustered Column and Line chart
• Line and Stacked Column chart
• Matrix
• Multi-Row Card
• Pie chart
• Ribbon chart
• Stacked Area char
• Scatter Chart
• Stacked Bar chart
• Waterfall chart
• Map
• Filled Map

• Slicer Basic
• Filters
• Advanced Filters
• Top N Filters
• Filters on Measures
• Page Level Filters
• Report Level Filters
• Drill through Filters

• Visuals in Power BI
• Create and Customize simple visualizations
• Combination charts
• Slicers
• Map visualizations
• Matrixes and tables
• Scatter charts
• Waterfall and funnel charts
• Gauges and single-number cards
• Modify colors in charts and visuals
• Z-Order
• Duplicate a report page

• Power BI Service
• QuickSights in Power BI
• Create and configure a dashboard
• Share dashboards with organizations
• Install and configure personal gateway

• Using Excel Data in Power BI
• Upload Excel Data to Power BI
• Import Power View and Power Pivot to Power BI
• Connect OneDrive for Business to Power BI

• Introduction to content packs, security and groups
• Publish Power BI Desktop reports
• Print and export dashboards and reports
• Manually republish and refresh your data
• Introduction Power BI Mobile
• Creating groups in Power BI
• Build content packs
• Use content packs
• Update content packs
• Integrate OneDrive for Business with Power BI
• Publish to web

• File Management System and Its Drawbacks
• Overview of Database Management System (DBMS) and Data Models
• Physical Data Models
• Logical Data Models
• Entity-Relationship (E-R) Model

• Advantages and Drawbacks of SQL Server Compared to Oracle and DB2
• Connecting to Server
• Authentication Modes
• SQL Server Authentication Mode
• Windows Authentication Mode
• Login and Password
• SQL Server Management Studio and Tools in Management Studio
• Object Explorer
• Object Explorer Details
• Query Editor

• Types Of TSQL Commands
• Data Definition Language (DDL)
• Data Manipulation Language (DML)
• Data Query Language (DQL)
• Data Control Language (DCL)
• Transaction Control Language (TCL)
• Database
• Creating Database
• Altering Database
• Deleting Database
• Constrains
• Procedural Integrity Constraints
• Declarative Integrity Constraints
• Not Null, Unique, Default and Check constraints
• Primary Key and Referential Integrity or foreign key constraints
• Data Types In TSQL
• Table
• Creating Table
• Altering/ Deleting Table

• Insert
• Identity
• Creating A Table From Another Table
• Inserting Rows From One Table To Another
• Update
• Computed Columns
• Delete
• Truncate
• Difference Between Delete and Truncate

• Select Clause
• Where clause
• Top clause
• Order By Clause
• Distinct Clause
• Isnull() function
• Column aliases
• Predicates
• Between … And
• In
• Like
• Is Null

• Scalar Functions
• Numeric Functions
• Character Functions
• Conversion Functions
• Date Functions
• Aggregate Functions
• Convenient Aggregate Functions
• Statistical Aggregate Functions
• Group By and Having Clauses
• Super Aggregates
• Over (partition by …) Clause
• Ranking Functions
• Common Table Expressions (CTE)

• Union
• Intersect
• Except

• Inner Join
• Equi Join
• Non-Equi Join
• Self-Join
• Outer Join
• Left Outer Join
• Right Outer Join
• Full Outer Join
• Cross Join

• Single Row Sub Queries
• Multi Row Sub Queries
• Any or Some
• ALL
• Nested Sub Queries
• Co-Related Sub Queries
• Exists and Not Exists

• Clustered Index
• Non-Clustered Index
• Create, Alter and Drop Indexes
• Using Indexes

• Login Creation
• User Creation
• Granting Permissions
• Revoking Permissions

• Purpose Of Views
• Creating, Altering and Dropping Indexes
• Simple and Complex Views

• Introduction
• Begin Transaction
• Commit Transaction
• Rollback Transaction

• Introduction to Excel
• Password Protect a workbook/worksheet
• Hiding & Unhiding Sheets
• Protecting Workbook structure
• Hiding sheets using VBA developer mode
• Hiding Excel Functions
• Locking/ Unlocking Cells

• Numbers
• Text
• Date Time
• Currency
• Percentage
• Converting Numbers to Text
• Converting Text to Numbers
• Changing Date/Time formats

• Working with Alt Key
• Paste special
• Insert Row/Column
• Switch between worksheets
• Navigate all excel features using Keyword

• Top Bottom Rules
• Highlight Cell Rules
• Data Bars
• Color Scales
• Exploring Rules Manager
• Use formulas for cell formatting

• Text Filters
• Number Filters
• Sorting
• Multi-level sorting
• Text to Columns – Delimiters
• Text to Columns – Fixed Width
• Remove Duplicates
• Data Validations
• Grouping

• Relative Referencing (A1)
• Absolute Referencing ($A$1)
• Partial Referencing ($A1) / (A$1)

• Sum/ Sumif/ Sumifs
• Count/ Countif/ Countifs
• SumProduct
• Round/ Roundup/ Rounddown
• Min/ Max/ Average

• If
• And
• Or
• IsText
• IsNumber

• Vlookup
• Vlookup with Match Function
• Vlookup using (*) problems
• Vlookup using Running Countif
• Nested Vlookup
• Hlookup
• Index Match
• Indirect
• Offset

• Char
• Concatenate
• Exact
• Find/ Search
• Left
• Right
• Proper
• Mid
• Upper

• Weekday
• Weeknum
• Day/ Month/ Year
• Date / Time
• Hour/ Minute/ Second

• Learning functions with {}
• Minif, Maxif, etc.

• Line
• Column
• Pie
• Bar
• Combo Charts
• Waterfall Charts
• Doughnut Charts
• Dynamic Charts using Pivot Slicers

• Pivot Table Fields
• Filters/ Rows/ Columns/ Values
• Insert Slicers
• Insert Timelines
• Group Selection
• Changing Data Source
• Report Layouts
• Group Selection
• Working with Multiple Tables using Relationship

• Record a Macro
• Save macro sheet in .xlsm format