Why This Data Science Program Works

• USA job-market aligned curriculum
• Real-time projects based on actual client scenarios
• Interview-focused training approach
• Resume & LinkedIn optimization (US format)
• Mock interviews with real feedback
• Dedicated job placement assistance
• Staffing-backed training, not just an institute

Our objective is simple: make you employable and confident for client interviews.

Who This Program Is For

• IT professionals stuck in low-growth roles
• Data / Business Analysts aiming for Data Scientist positions
• Career switchers targeting analytics & data roles
• Freshers seeking US-oriented Data Science jobs
• Non-IT background candidates serious about entering tech

No prior Data Science experience required. We start from fundamentals to job-level skills.

What You Will Master

• Analyze real-world datasets with confidence
• Apply Data Science & analytics concepts in projects
• Perform Exploratory Data Analysis (EDA)
• Clean, transform, and prepare large datasets
• Use R for analytics and reporting
• Work with Big Data & Hadoop ecosystem
• Explain projects clearly in interviews

You don’t just learn — you practice, apply, and present like a professional.

Job Placement Support – Our Biggest Advantage

• US-standard resume preparation
• LinkedIn profile optimization
• Technical & scenario-based interview preparation
• Mock interviews simulating real client rounds
• Feedback-driven improvement
• Placement assistance via staffing & consulting network

We train you to clear interviews, not just complete a syllabus.

Introduction to Data Science

• What is Data Science?
• Evolution and importance of Data Science
• US job market demand for Data Scientists
• Data Science vs Analytics vs Business Intelligence
• Role of a Data Scientist in organizations
• Data Science value chain
• Types of analytics: Descriptive, Diagnostic, Predictive, Prescriptive
• Analytics vs Data Science
• Analytics project lifecycle
• Probability & statistics lifecycle basics

Data Fundamentals

• What is Data?
• Data categorization basics
• Structured, Semi-structured & Unstructured data
• Data collection methods
• Data sources & formats
• Data quality concepts
• Data quality issues & resolution techniques
• Data change & quality stories

Data Architecture & Storage

• Data Architecture fundamentals
• Components of Data Architecture
• OLTP vs OLAP
• Data warehousing concepts
• Data storage & processing models
• Modern data platforms overview

Big Data Concepts

• What is Big Data?
• 5 V’s of Big Data
• Big Data architecture & challenges
• Big Data technology ecosystem
• Distributed computing concepts
• Large-scale data processing complexity

Hadoop & Distributed Computing

• Introduction to Hadoop
• Hadoop architecture
• HDFS concepts
• MapReduce framework
• Hadoop ecosystem (Hive, Pig, HBase, Sqoop)
• Big Data processing workflows

Data Science Deep Dive

• What is a Data Product?
• Data Science skills for US job roles
• Cost vs storage in large-scale analysis
• Industry-wise Data Science use cases
• Data Science project lifecycle
• Real-world problem-solving approach

Data Acquisition & Preparation

• Data sourcing strategies
• Internal vs external data sources
• Data acquisition techniques
• Input data evaluation
• Data format, volume & quality checks
• Data resolution & transformation
• File format conversion
• Data anonymization & compliance basics

R Programming for Data Science

• R language fundamentals
• Why R for analytics & Data Science
• R ecosystem & community
• Industry usage of R
• Installing R & packages
• Working with R Studio
• Basic operations in R

Exploratory Data Analysis & Visualization

• EDA concepts & importance
• Types & goals of EDA
• EDA implementation in R
• Boxplots, correlations & summary statistics
• Advanced EDA plots
• Data visualization principles
• Storytelling with data
• Advanced visualization techniques

Career Roles You Can Target

• Data Scientist
• Data Analyst
• Big Data Analyst
• Junior Machine Learning Engineer
• Analytics Consultant