Data Analyst with AI

Fundamentals of Data & AI

Power BI for Data Analysis

Python for Data Analysis

SQL for Data Analysis

Statistics for Data Analysis

Machine Learning for Data Analysis

Transformers & Gen AI

Ai Agents

Realtime ClassRoom Training

Project and Task Based

6 to 8 Hrs Every Day

Interviews, Jobs and Placement Support

Communication Skills & Personality Development

Interview Preparations

50000 +

Students Enrolled

4.7

(500)

Ratings

6 months

Duration

Why Data Analyst with AI Training With Digital Edify?

Data Analyst Engineer

Data Analyst Architect

Technology Consultant ReactJs

Systems Engineer

System Administrator

₹ 8 LPA

Avg package

44 %

Avg hike

3000 +

Tech transitions

Annual Average Salaries

Demand

87%

Managers said hiring Java Training was top priority

Our Alumni Work at Top Companies

Explore the Digital Edify way

Learn

Learn from Curated Curriculums developed by Industry Experts

Data Analyst with AI Course Curriculum

It stretches your mind, think better and create even better.

Fundamentals of Data & AI

Module 1

Topics:

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.

Module 2

Topics:

Application Overview

Understanding the significance and types of applications.

Web Application Fundamentals

Key components and basic concepts of web applications.

Web Technologies

Essential technologies and frameworks used in web application development.

Software Development Life Cycle (SDLC)

Phases and methodologies for effective software development.

Agile & Scrum

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

Module 3

Topics:

What is data?

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

Types of Data

Structured Data: Organized in tables (databases, spreadsheets).

Unstructured Data: Freeform content (videos, images, text, emails).

Semi-structured Data: A mix of both (JSON, XML).

Data Storage

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

Data Analysis

Finding useful information in data to help make better decisions.

Data Engineering

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

Module 4

Topics:

Processing Power for AI

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

Key Technologies

CPU (Central Processing Unit): General-purpose processor, used for basic AI tasks.

GPU (Graphics Processing Unit): Specialized for AI training and deep learning.

TPU (Tensor Processing Unit): Optimized for AI workloads (used by Google).

Edge Computing

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

Cloud AI Platforms

Google Cloud AI, AWS AI, Microsoft Azure AI.

Module 5

Topics:

Business Intelligence & Analytics

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

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

Financial Data Analysis

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

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

Healthcare Analytics

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

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

Marketing & Customer Insights

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

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

Retail & E-Commerce Analytics

AI optimizes pricing, inventory management, and product recommendations.

Example: Amazon’s AI-powered recommendation system.

Power BI Course

Module 1

Topics:

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

Module 2

Topics:

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

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

Implementing Various Chart Types and Map Visuals

Advanced Filtering Techniques and Utilizing Bookmarks

Module 3

Topics:

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

Module 4

Topics:

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

Module 5

Topics:

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

Module 6

Topics:

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

Module 7

Topics:

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

Python for Data Analysis

Module 1

Topics:

Why Python?

Simplicity, Libraries, Community Support.

Setting up Python

Anaconda, Jupyter Notebook, VS Code.

Module 2

Topics:

Data Types

int, float, string, list, tuple, dict.

Control Structures

if-else, loops.

Functions & Modules

Functions & Modules.

File Handling

File Handling.

Module 3

Topics:

Classes & Objects

Classes & Objects.

Inheritance & Polymorphism

Inheritance & Polymorphism.

Encapsulation & Abstraction

Encapsulation & Abstraction.

How OOP is used in AI

e.g., Model Classes in TensorFlow/PyTorch.

Module 4

Topics:

NumPy

Arrays & Numerical Computation.

Pandas

Data Manipulation & Analysis.

Matplotlib & Seaborn

Data Visualization.

Module 5

Topics:

Reading/Writing Data

CSV, Excel, JSON.

Handling Missing Data

Handling Missing Data.

Data Cleaning & Transformation

Data Cleaning & Transformation.

SQL for Data Analysis

Module 1

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`).

Module 2

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`).

Module 3

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.

Module 4

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.

Module 5

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.

Statistics for Data Analysis

Module 1

Topics:

Why Statistics?

Understanding Data, Making Data-Driven Decisions, Business Insights.

Types of Statistics

Descriptive: Summarizing Data.

Inferential: Making Predictions from Data.

Module 2

Topics:

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.

Module 3

Topics:

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).

Module 4

Topics:

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).

Module 5

Topics:

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).

Module 6

Topics:

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

Overfitting vs. Underfitting in Data Analytics Models.

Machine Learning for Data Analysis

Module 1

Topics:

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.

Module 2

Topics:

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.

Module 3

Topics:

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).

Module 4

Topics:

Ensuring Reliable Predictions

Train-Test Split & Cross-Validation.

Overfitting vs. Underfitting in Business Applications.

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

Module 5

Topics:

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.

Generative AI

Module 1

Topics:

What is Generative AI?

Overview of generative AI concepts.

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.

Module 2

Topics:

What is Prompt Engineering?

Crafting inputs for AI models.

Importance of Effective Prompt Design

Improving model outputs.

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.

Module 3

Topics:

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.

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.

Module 4

Topics:

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.

AI Agents & Ops

Module 1

Topics:

What are AI Agents?

Overview of AI agents.

Difference Between AI Agents and Traditional AI Systems

Comparing agent-based and traditional AI.

Key Characteristics

Autonomy.

Goal-Oriented Behavior.

Tool Usage & Execution.

Real-World Applications of AI Agents

Examples of AI agent use cases.

Module 2

Topics:

CrewAI

Overview: How CrewAI enables multi-agent workflows.

Components: Roles, Tasks, Tools, Memory.

Use Case: Automating research and content generation.

N8N

What is N8N?

Connecting AI Agents with APIs and automation.

Use Case: AI-driven task execution with n8n.

Langflow

Introduction to Langflow.

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

Use Case: Rapid prototyping and deployment of AI Agents.

Module 3

Topics:

Creating AI Agents

Using CrewAI + Lang Flow.

Automating tasks

With CrewAI + N8N.

Multi-agent collaboration

For business workflows.

Module 4

Topics:

Challenges

Hallucinations, Reliability, Ethical Issues.

Future Trends

AI Agents in Business, Research, and Automation.

Module 5

Topics:

APIs

FastAPI.

CI/CD Containerization

Docker.

Cloud Platforms

AWS, Google Cloud, Azure.

Project

Deploy a sentiment analysis model as a web app.

TOOlS & PLATFORMS

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HRMS Project

The HRMS project develops a digital system for managing HR functions like employee data, payroll, recruitment, and performance, aiming to streamline processes and enhance organizational efficiency.

CRM Project

A CRM project develops a system to manage company interactions with customers, incorporating tools for contact, sales, productivity, and support to enhance service, drive sales, and boost retention.

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