Data Analyst with AI

What is AI?

AI is the ability of machines to perform tasks that require human intelligence, like learning, reasoning, and problem-solving.

How AI works

AI uses data, algorithms, and computing power to make predictions and automate tasks.

AI in everyday life

Virtual assistants (Open AI, Deepseek), recommendation systems (Netflix, YouTube), and chatbots.

Understanding the significance and types of applications.

Key components and basic concepts of web applications.

Essential technologies and frameworks used in web application development.

Phases and methodologies for effective software development.

Principles, frameworks, and best practices for managing projects iteratively.

Information collected from different sources (text, images, videos, numbers).

Organized in tables (databases, spreadsheets).

Freeform content (videos, images, text, emails).

A mix of both (JSON, XML).

Databases (SQL, NoSQL), cloud storage (AWS, Google Drive).

Finding useful information in data to help make better decisions.

Building systems and processes to collect, store, and process large datasets using tools like Hadoop and Spark.

AI needs strong computing resources to process large amounts of data.

General-purpose processor, used for basic AI tasks.

Specialized for AI training and deep learning.

Optimized for AI workloads (used by Google).

Running AI on devices instead of the cloud (e.g., AI in mobile phones).

Google Cloud AI, AWS AI, Microsoft Azure AI.

AI automates data visualization, trend detection, and predictive analytics.

Example: Power BI and Tableau use AI-driven insights for forecasting and decision-making.

AI detects fraud, assesses risks, and predicts market trends.

Example: AI-powered credit scoring and fraud detection in banking.

AI analyzes medical data, predicts disease outbreaks, and aids diagnostics.

Example: AI in medical imaging (MRI/CT scan analysis).

AI segments customers, analyzes sentiment, and optimizes marketing campaigns.

Example: AI-driven customer behavior analysis for personalized recommendations.

AI optimizes pricing, inventory management, and product recommendations.

Example: Amazon’s AI-powered recommendation system.

Overview of Analytics and Power BI Tool Suite

Career Opportunities and Job Roles in Power BI

Power BI Data Analyst (PL 300) Certification Overview

Introduction to AI Visuals and Features in Power BI

Understanding the Power BI Ecosystem and Architecture

Data Sources and Types for Power BI Reporting

Power BI Design Tools and Installation

Exploring Power BI Desktop Interface: Data View, Report View, and Canvas

Implementing Various Chart Types and Map Visuals

Advanced Filtering Techniques and Utilizing Bookmarks

Visual Interaction Techniques in Reports

Using Slicers for Dynamic Report Filtering

Managing Report Pages and Visual Sync Limitations

Implementing Grouping and Binning in Reports

Creating and Utilizing Hierarchies for Drill-Down Reports

Introduction to Power Query M Language

Basic and Advanced Data Transformations

Query Duplication, Grouping, and Data Cleaning Techniques

Implementing Parameter Queries for Dynamic Data Loads

Creating and Managing Parameters in Power Query

Overview of Power BI Cloud Components and App Workspaces

Creating, Managing, and Sharing Reports & Dashboards

Configuring and Managing Gateways for Data Refresh

Utilizing Workbooks and Excel Online with Power BI Cloud

Creating and Managing Power BI Apps

Importance of DAX in Power BI

Learning Basic DAX Syntax, Data Types, and Contexts

Implementing Quick Measures and Advanced Calculations

Mastering Variables and Dynamic Expressions in DAX

Advanced DAX Functions for Time Intelligence

Implementing Row-Level Security (RLS) with DAX

Configuring Power BI Report Server

Understanding Power BI Administration and AI Features

Managing Security and Report Server Administration in Power BI

Utilizing DAX for Custom Analytics and Reporting

Why Python? (Simplicity, Libraries, Community Support).

Setting up Python (Anaconda, Jupyter Notebook, VS Code.

 Data Types (int, float, string, list, tuple, dict).

 Control Structures (if-else, loops).

 Functions & Modules.

 File Handling.

Classes & Objects

 Inheritance & Polymorphism.

 Encapsulation & Abstraction.

 How OOP is used in AI (e.g., Model Classes in TensorFlow/PyTorch).

NumPy – Arrays & Numerical Computation.

Pandas – Data Manipulation & Analysis.

Matplotlib & Seaborn – Data Visualization.

Reading/Writing CSV, Excel, JSON.

Handling Missing Data.

Data Cleaning & Transformation.

Topics:

Introduction to Databases and SQL: Understanding relational databases and the role of SQL.

SQL Syntax Overview: Keywords, statements, and clauses.

Basic SQL Commands: `SELECT`, `FROM`, `WHERE`, and `ORDER BY`.

Filtering Data: Using conditions to retrieve specific data (`AND`, `OR`, `NOT`).

Topics:

Understanding Table Relationships: Primary keys, foreign keys, and the importance of relationships in databases.

Join Operations: `INNER JOIN`, `LEFT JOIN`, `RIGHT JOIN`, and `FULL JOIN`.

Subqueries and Nested Queries: Using subqueries in the `SELECT`, `FROM`, and `WHERE` clauses.

Aggregating Data: Using `GROUP BY` and aggregate functions (`COUNT`, `SUM`, `AVG`, `MIN`, `MAX`).

Topics:

Data Manipulation Commands: `INSERT`, `UPDATE`, `DELETE`.

Managing Tables: Creating and altering tables (`CREATE TABLE`, `ALTER TABLE`, `DROP TABLE`).

Advanced Filtering Techniques: Using `LIKE`, `IN`, `BETWEEN`, and wildcard characters.

Working with Dates and Times: Understanding and manipulating date and time data.

Topics:

Advanced SQL Functions: String functions, mathematical functions, and date functions.

Window Functions: Overviews of `ROW_NUMBER`, `RANK`, `DENSE_RANK`, `LEAD`, `LAG`, and their applications.

Query Performance Optimization: Indexes, query planning, and execution paths.

Common Table Expressions (CTEs): Writing cleaner and more readable queries with `WITH` clause.

Topics:

Analytical SQL for Reporting: Building complex queries to answer analytical questions.

Pivoting Data: Transforming rows to columns (`PIVOT`) and columns to rows (`UNPIVOT`).

Data Warehousing Concepts: Introduction to data warehousing practices and how they apply to SQL querying.

Integrating SQL with Data Analysis Tools: Connecting SQL databases with tools like Excel, Power BI, and Python for deeper data analysis.

Why Statistics? (Understanding Data, Making Data-Driven Decisions, Business Insights)

Types of Statistics:

Descriptive (Summarizing Data)

Inferential (Making Predictions from Data)

Measures of Central Tendency: Mean, Median, Mode (Interpreting Averages in Datasets)

Measures of Dispersion: Variance, Standard Deviation, Range, IQR (Understanding Data Spread)

Skewness & Kurtosis: Identifying Distribution Shapes and Outliers in Data

Basic Probability Concepts: Probability Rules, Conditional Probability (Likelihood of Events in Data)

Probability Distributions & Their Applications:

Discrete: Binomial (Customer Conversions), Poisson (Website Traffic Predictions)

Continuous: Normal (Stock Price Trends), Exponential (Service Wait Times)

Hypothesis Testing: Confidence Intervals, p-value Interpretation (Validating Business Hypotheses)

Common Tests & Use Cases:

t-test: Comparing Two Groups (A/B Testing in Marketing)

Chi-square test: Analyzing Categorical Data (Customer Preferences)

ANOVA: Comparing Multiple Groups (Sales Performance Across Regions)

Data Visualization Techniques: Histograms, Box Plots, Scatterplots (Understanding Trends & Patterns)

Correlation & Covariance: Identifying Relationships Between Variables (Customer Behavior Analysis)

Outlier Detection: Impact of Outliers on Business Data (Fraud Detection)

Regression Analysis:

Linear Regression: Predicting Sales, Revenue, Demand Trends

Logistic Regression: Customer Churn, Lead Conversion Predictions

Bias-Variance Tradeoff & Model Evaluation: Ensuring Reliable Predictive Models

Overfitting vs. Underfitting in Data Analytics Models

What is ML in Data Analytics? (Definition, Importance, Use Cases)

Types of ML & Their Role in Analytics:

Supervised Learning: Predictive analytics for business insights (Regression, Classification)

Unsupervised Learning: Discovering patterns in data (Clustering, Anomaly Detection)

Reinforcement Learning: Optimizing decision-making processes

Regression for Forecasting & Trend Analysis:

Linear Regression (Predicting Sales, Revenue Forecasting)

Multiple Regression (Impact of Multiple Factors on Outcomes)

Regularization Techniques (L1

Lasso, L2

Ridge) for Avoiding Overfitting

Classification for Business Intelligence:

Logistic Regression (Customer Churn Prediction)

Decision Trees (Risk Assessment in Finance)

Random Forest (Fraud Detection in Transactions)

K-Nearest Neighbors (Customer Segmentation)

Naïve Bayes (Spam Detection in Emails)

Performance Metrics for Data Analytics Models:

Accuracy, Precision, Recall, Confusion Matrix

Business Impact of ML Model Performance

Clustering for Market Segmentation & Pattern Recognition:

K-Means (Customer Segmentation, Product Categorization)

Hierarchical Clustering (Grouping Users Based on Behavior)

DBSCAN (Anomaly Detection in Transaction Data)

Ensuring Reliable Predictions:

Train-Test Split & Cross-Validation

Overfitting vs. Underfitting in Business Applications

Hyperparameter Tuning (Grid Search, Random Search) for Model Optimization

Preparing Data for Machine Learning Models:

Handling Missing Data in Business Analytics

Feature Scaling (Normalization, Standardization) for Consistent Insights

Feature Selection & Extraction to Improve Model Performance

What is Generative AI?

Key Applications

Text (Chatbots, Content Generation)

Image (DALL·E, MidJourney)

  Audio (Music Generation, Voice Synthesis)

  Code (Cursor, Copilot)

Evolution of GenAI:

From Rule-Based Systems to Deep Learning

Comparison of Generative Models (GANs, VAEs, LLMs)

Challenges in GenAI (Bias, Hallucinations, Ethical Considerations)

What is Prompt Engineering?

Importance of Effective Prompt Design

Basic Prompting Techniques:

Instruction-Based Prompts

Few-Shot & Zero-Shot Learning

Advanced Prompt Engineering:

 Chain-of-Thought (CoT) Prompting

Self-Consistency & Iterative Refinement

Structured vs. Unstructured Prompts

Experimenting with LLMs (Using GPT-4, Claude, or LLaMA)

  Transformers & LLMs

  Why Transformers? (Limitations of RNNs & LSTMs)

Key Components:

Self-Attention Mechanism

Multi-Head Attention

Encoder-Decoder Architecture

Evolution of Transformers:

From BERT to GPT, T5, and Beyond

Large Language Models (LLMs)

What are LLMs?

Pre Training vs. Fine-Tuning

Popular LLM Architectures:

GPT (OpenAI GPT-4,O3)

DeepSeek

BERT (Contextual Embeddings)

T5 (Text-to-Text Models)

Challenges in LLMs:

Bias & Ethical Issues

Scalability & Cost

Model Hallucinations

GANs & VAEs (Other Generative Models)

Generative Adversarial Networks (GANs):

What are GANs?

How GANs Work: Generator & Discriminator

Applications of GANs (DeepFake, Image Generation,Super-Resolution)

◙ Autoencoders & Variational Autoencoders (VAEs):

What are Autoencoders?

Difference Between Autoencoders & VAEs

 Applications (Data Denoising, Anomaly Detection) Lightweight Models (LIMs)

What are Lightweight AI Models?

Difference Between LIMs & LLMs

Use Cases of LIMs in Edge AI

LangChain

  What is LangChain

  Building Modular LLM Workflows

Practical Applications

Hugging Face

Overview of Hugging Face Transformers & Datasets

How to Fine-Tune & Deploy Models

Vector Databases & Retrieval-Augmented Generation (RAG)

  Introduction to Vector Databases (Pinecone, Weaviate, FAISS)

  Understanding RAG and Its Role in GenAI

What are AI Agents?

Difference Between AI Agents and Traditional AI Systems

Key Characteristics:

Autonomy

  Goal-Oriented Behavior

Tool Usage & Execution

Real-World Applications of AI Agents

  CrewAI (Multi-Agent Collaboration)

Overview: How CrewAI enables multi-agent workflows

 Components: Roles, Tasks, Tools, Memory

Use Case: Automating research and content generation

N8N (Workflow Automation for AI Agents)

  What is N8N?

  Connecting AI Agents with APIs and automation

Use Case: AI-driven task execution with n8n

  Langflow (Visual Agent Orchestration)

Introduction to Langflow

Building AI Agent workflows with a drag-and-drop interface

Use Case: Rapid prototyping and deployment of AI Agents

  Creating AI Agents using CrewAI + Lang Flow

Automating tasks with CrewAI + N8N

Multi-agent collaboration for business workflows

Challenges: Hallucinations, Reliability, Ethical Issues

Future Trends: AI Agents in Business, Research, and Automation

APIs: FastAPI.

CI/CD Containerization: Docker.

Cloud Platforms: AWS, Google Cloud, Azure.

Project: Deploy a sentiment analysis model as a web app.