Data Analysts play a crucial role in helping organizations make informed, data-driven decisions. A Data Analyst program equips learners with the skills to collect, clean, analyze, and visualize data to uncover patterns, trends, and actionable insights.
This role is ideal for graduates and professionals from IT, Engineering, Commerce, Management, and non-technical backgrounds, as it focuses more on analytical thinking and business understanding than heavy programming. Data Analysts are in high demand across IT, finance, healthcare, marketing, e-commerce, manufacturing, and consulting industries.
Learn from Curated CurriculumsĀ developed by Industry Experts
It expands your mind, sharpens your thinking, and empowers you to create better.
1. What is an Application?
2. Types of Applications
3. Web Application Fundamentals
4. Web Technologies: (List key technologies and their roles)
Frontend: HTML, CSS, JavaScript, React
Backend: Python, Java, Node.js
Databases: SQL (MySQL, PostgreSQL), NoSQL (MongoDB).
5. Software Development Life Cycle (SDLC)
Phases: Planning, Analysis, Design, Implementation (Coding), Testing, Deployment, Maintenance.
6. Application Development Methodologies
Agile: Core principles, Scrum, Kanban
Waterfall
1. What is Data
2. Types of Data
3. Data Storage
4. Data Analysis
5. Data Engineering
6. Data Science
1. The Importance of Computing Power
2. Key Computing Technologies:
CPU (Central Processing Unit)
GPU (Graphics Processing Unit)
3. Cloud Computing:
What is the Cloud?
Cloud Service Models:
IaaS (Infrastructure as a Service)
PaaS (Platform as a Service)
SaaS (Software as a Service)
1. What is Artificial Intelligence (AI)?
2. How AI Works?
3. Key Concepts:
Machine Learning (ML)
Deep Learning (DL)
4. Generative AI:
What is Generative AI?
Examples: Large Language Models (LLMs), image generation models.
5. AI in Everyday Learning
1. Customer Relationship Management (CRM)
2. Human Resource Management Systems (HRMS)
3. Retail & E-Commerce
4. Healthcare
Python Language Basics: Variables, Conditions, and Functions
Advanced Python Structures: Lists, Tuples, Dictionaries
Virtual Environments and Package Management in Python for Data Science
1. What is Data
2. Types of Data
3. Data Storage
4. Data Analysis
5. Data Engineering
6. Data Science
1. The Importance of Computing Power
2. Key Computing Technologies:
CPU (Central Processing Unit)
GPU (Graphics Processing Unit)
3. Cloud Computing:
What is the Cloud?
Cloud Service Models:
IaaS (Infrastructure as a Service)
PaaS (Platform as a Service)
SaaS (Software as a Service)
1. What is Artificial Intelligence (AI)?
2. How AI Works?
3. Key Concepts:
Machine Learning (ML)
Deep Learning (DL)
4. Generative AI:
What is Generative AI?
Examples: Large Language Models (LLMs), image generation models.
5. AI in Everyday Learning
1. Customer Relationship Management (CRM)
2. Human Resource Management Systems (HRMS)
3. Retail & E-Commerce
4. Healthcare
An LMS project develops a digital platform for online learning, featuring course creation, content management, user tracking, assessments, and reporting, aimed at enhancing educational interaction.
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.
Internships and Course certifications forĀ Enhanced Skill Validation.
Our focus on job-readinessĀ Github Profile, Linkedin Profile, Resume Prep and Help Apply
Guidance on creating and maintaining a professional GitHub profile to showcase technical projects and coding prowess.
Assistance in crafting a compelling LinkedIn profile for networking and visibility among recruiters.
Expert advice on resume writing to effectively highlight skills, experience, and achievements.
Support in identifying suitable job opportunities and navigating the application process.
AI + Data Science
Data Analytics
Data Analytics
Data Analytics
1000+ uplifted through our hybrid classroom & online training,
enriched by real-time projects and job support.
