Data Science Course

Duration: 5 Months

Suitable for: Beginners & Intermediates

Mode: Online & Classroom

Course Overview

Our comprehensive Data Science program equips you with all the skills needed to extract meaningful insights from complex data sets and build predictive models. This intensive course covers statistical analysis, machine learning algorithms, data visualization, and big data technologies, preparing you for a career in one of the most in-demand fields today.

Led by industry professionals with extensive real-world experience, this training program combines theoretical concepts with hands-on projects, ensuring you gain practical experience analyzing real-world data and building production-ready machine learning models. By the end of the course, you'll have a professional portfolio demonstrating your data science capabilities to potential employers.

Course Curriculum

Module 1: Foundations of Data Science
- Introduction to Data Science Lifecycle
- Python Programming Fundamentals
- Data Types and Structures
- NumPy for Numerical Computing
- Pandas for Data Manipulation
- Jupyter Notebooks & Development Environment
- Version Control with Git
Module 2: Data Wrangling & Preprocessing
- Data Collection Techniques
- Data Cleaning and Preprocessing
- Handling Missing Data
- Feature Engineering
- Data Transformation & Normalization
- Exploratory Data Analysis (EDA)
- Data Quality Assessment
Module 3: Statistics & Probability
- Descriptive Statistics
- Probability Distributions
- Statistical Inference
- Hypothesis Testing
- Confidence Intervals
- Correlation Analysis
- ANOVA and Chi-Square Tests
Module 4: Data Visualization
- Visualization Principles & Best Practices
- Matplotlib & Seaborn
- Interactive Visualizations with Plotly
- Dashboard Creation with Dash
- Geospatial Data Visualization
- Storytelling with Data
- Creating Effective Presentations
Module 5: Machine Learning Fundamentals
- Supervised vs Unsupervised Learning
- Regression Algorithms
- Classification Algorithms
- Model Evaluation Metrics
- Cross-Validation Techniques
- Feature Selection & Dimensionality Reduction
- Bias-Variance Tradeoff
Module 6: Advanced Machine Learning
- Ensemble Methods (Random Forests, Boosting)
- Clustering Algorithms
- Time Series Analysis & Forecasting
- Natural Language Processing
- Recommender Systems
- Anomaly Detection
- Hyperparameter Tuning
Module 7: Deep Learning
- Neural Network Fundamentals
- TensorFlow & Keras
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
- Transfer Learning
- Generative Models
- Deep Learning for Computer Vision & NLP
Module 8: Big Data & Cloud Computing
- Introduction to Big Data
- Spark for Large-Scale Data Processing
- SQL for Data Analysis
- Data Warehousing Concepts
- Cloud Platforms (AWS, Azure, GCP)
- Deploying ML Models to Production
- Model Monitoring & Maintenance
Module 9: Capstone Projects
- Predictive Analytics Project
- Natural Language Processing Project
- Computer Vision Application
- Time Series Forecasting
- Portfolio Development & Documentation

Learning Outcomes

Master data manipulation and preprocessing techniques
Apply statistical methods to extract insights from data
Create impactful data visualizations
Build and evaluate machine learning models
Deploy data science solutions to production environments
Develop deep learning applications
Create a professional portfolio of data science projects

Career Opportunities

Data Scientist
Machine Learning Engineer
Data Analyst
Business Intelligence Analyst
AI Research Scientist
Quantitative Analyst
Big Data Engineer

₹39,999

Inclusive of all taxes

180+ Hours of Training
6+ Real-world Projects
Industry-relevant Curriculum
Expert Instructors
Job Placement Assistance
Certificate of Completion
Access to Resources & Materials
Internship Opportunities

Enroll Now

Meet Our Instructors

Dr. Rajesh Kumar

Senior Data Scientist

10+ years of experience in developing machine learning solutions for finance, healthcare, and e-commerce sectors with PhD in Statistical Computing.

Priya Mehta

AI Research Specialist

Expert in deep learning and computer vision with experience at leading tech companies and multiple publications in top-tier conferences.

Aditya Sharma

Big Data Engineer

Specializes in large-scale data processing systems and cloud infrastructure for machine learning applications.

Student Success Stories

"The Data Science course at Shabnam Infosys transformed my career trajectory. Coming from a business background with minimal technical skills, the structured curriculum helped me build a strong foundation in statistics, programming, and machine learning. The hands-on projects were particularly valuable, giving me practical experience with real datasets. Within three months of completing the course, I secured a position as a Junior Data Analyst at a financial services company."

Ananya Patel

Data Analyst at FinServ Solutions

"As a software engineer looking to transition into machine learning, this course provided the perfect bridge between my programming knowledge and data science concepts. The deep learning modules were comprehensive and up-to-date with the latest advancements. The instructors were incredibly supportive, especially when I struggled with complex algorithms. I'm now working as a Machine Learning Engineer at a healthcare startup, building models that actually impact patient care."

Rohit Verma

Machine Learning Engineer at HealthTech Innovations

"The practical approach of this data science course was its greatest strength. Rather than just theory, we worked with messy, real-world datasets and learned how to derive meaningful insights. The capstone project allowed me to work on a predictive maintenance solution for manufacturing equipment, which I presented at job interviews. This portfolio project was instrumental in landing my current role, where I'm now leading data initiatives that have saved my company millions in operational costs."

Neha Kapoor

Senior Data Scientist at Industrial Analytics

Frequently Asked Questions

Do I need programming experience for this course?

While some basic understanding of programming concepts is helpful, our course is designed to accommodate beginners as well. We start with Python fundamentals and gradually build to more complex programming concepts. For absolute beginners, we offer pre-course materials to help you build basic programming skills before the main program begins.
Is a strong mathematics background required?

A basic understanding of mathematics (algebra and some calculus) is helpful, but not mandatory. Our course includes a refresher on the mathematical concepts essential for data science. We focus on building intuition behind the mathematical principles rather than just formulas, making the content accessible to students from various backgrounds.
How much time should I dedicate per week for this course?

We recommend dedicating at least 15-20 hours per week to get the most out of the course. This includes attending classes, completing assignments, working on projects, and self-study. The course is designed to be intensive but manageable for those who may be working or studying alongside.
What kind of support will I receive during the course?

Throughout the course, you'll have access to instructor support during scheduled classes, dedicated doubt-clearing sessions, a community forum for peer learning, and one-on-one mentorship sessions for personal projects. We also provide code reviews for assignments and projects to help you improve your analytical and programming skills.
How does the job placement assistance work?

Our placement assistance includes resume building workshops, LinkedIn profile optimization, technical interview preparation with focus on data science questions, mock interviews, portfolio development guidance, and connections with our hiring partners. We also organize placement drives and networking events with companies seeking data science talent. Our placement team works with you until you secure a position, even after course completion.

Data Science