ARTIFICIAL INTELLIGENCE TRAINING

BASIC INFO:

Duration: 4 Months.

Days: Monday to Thursday.

Timing: 9.00 to 10.30am & 6.00 to 7,30pm.

Fee: 30,000 (Instalments).

TRAINING IN DEPTH:

Python Language

Data Analytics

Agentic AI

Generatic AI

Machine Learning

Deep Learning

Natural Language

Processing (NLP)

LLMs

INTERMEDIATE EXPLORATION:

• PYTHON

• NUMPY

• PYTROCH

• PANDAS

• TENSORFLOW

• SCIKIT-LEARN

• TABLEAU

• DOCKER

• OOPS

• FRAMEWORKS

• GITHUB

• HUGGING FACE

• LANGCHAIN

COURSE OUTLINE:

Designed to build industry-ready Data Analysts with Python, BI tools, and AI-assisted analytics.

MONTH 1:

DATA ANALYSIS FOUNDATIONS

SQL for Data Analysis (Advanced)

Database fundamentals and relational models

Complex joins and subqueries

CTEs and window functions

Aggregations and query optimization

Real-world analytical SQL use cases

Excel for Data Analysis

Data cleaning and preparation

Pivot tables and advanced formulas

XLOOKUP and business calculations

Excel dashboards and reporting.

MONTH 2:

PYTHON & BUSINESS INTELLIGENCE

Python for Data Analysis

Python basics for analysts

Pandas and NumPy for data handling

Data cleaning and transformation

Exploratory Data Analysis (EDA)

Integrating SQL and Python workflows

Apache Superset (Advanced)

Dataset creation and modeling

Chart types and dashboard design principles

Filters, cross-filters, and parameters

Mixed charts and dual-axis reporting

KPI dashboards and operational analytics

MONTH 3:

POWER BI & AI-ASSISTED ANALYTICS

Power BI

Power BI interface and data modeling

Visual design best practices

Slicers, drill-through, and interactions

KPI cards and management dashboards

AI for Data Analysts

Introduction to AI in analytics

AI tools for data exploration

AI-assisted SQL and Python usage

Automated insights and trend detection

AI-powered reporting and decision support

CAPSTONE PROJECTS

End-to-end data analysis project

SQL and Python-based data analysis

Interactive dashboards using Superset and Power BI

AI-assisted insights and final presentation

CAREER PREPARATION

Resume building and optimization

Data Analyst interview preparation

Real-world business case studies

Salary negotiation guidance

Job application strategy

LEARNING OUTCOMES

Write advanced SQL queries on real datasets

Clean and analyze data using Python

Build professional dashboards using Superset and Power BI

Use AI tools to enhance analytical workflows

Communicate insights effectively to stakeholders.

MONTH 4:

Advanced Generated & Agentic AI.