Master Data Science To
Launch Your Career
Into New Horizons
Boost your career with our Master in Data Science course, crafted to provide you with the advanced skills and knowledge essential for success in today’s data-driven landscape. Whether you’re aiming to transition into a growing field or advance in your current role, this course will help you seize new opportunities and become a leader in data science.
Job Placement Connections
Skills Assessment & Estimation Training
Resume and LinkedIn Profile Development
Mastering Negotiation and Persuasion Skills
Practice Job Interviews and Placement
Digital Marketing Career Opportunities
Guaranteed Placement Assistance for All Students
9 LPA
Highest Package
4.5 LPA
Average Package
90%
Placement Rate
50%
Average Salary Hike
*Placement Statistics According to the 2024 Internal Report.
Our Alumni Partner with Leading Brands Such As
Our graduates can collaborate with prestigious brands across various industries, leveraging their skills in digital marketing strategies, SEO optimization, social media management, and more. Their expertise enhances the brands’ online presence and showcases the high level of training and knowledge they’ve acquired through our program. This hands-on experience with leading companies highlights their ability to drive impactful results and adapt to the ever-evolving digital landscape.
Connect with CXOs and CMOs from Top Digital Brands
Curriculum Developed in Collaboration with Industry Experts
Course Modules
Module 1: Introduction to Numpy (Video)
Begin your adventure with an overview of Numpy, a core Python package. Learn the fundamentals of working with arrays and carrying out effective calculations to set the stage for your data science abilities.
Module 2: Using Pandas to Wrangle Data
Discover how to use Pandas to clean, transform, and get your data ready for analysis. To make sure your datasets are prepared for modeling, this subject covers the fundamental data manipulation techniques.
Module 3: Plotting in Python
Dive into data visualization with Python, mastering techniques to create insightful and compelling charts and graphs that bring your data to life.
Module 4: Linear Models for Regression & Classification
Understand the core concepts of linear models, and learn how to apply them to both regression and classification problems, forming the basis for predictive modeling.
Module 5: Preprocessing Data
Explore various data preprocessing techniques that enhance the quality of your data, ensuring your models receive the best possible input for accurate predictions.
Module 6: Decision Trees
Delve into decision trees, a versatile tool in machine learning. Learn how to build, visualize, and interpret decision trees for both regression and classification tasks.
Module 7: Naive Bayes
Discover the simplicity and power of the Naive Bayes algorithm, particularly effective for text classification and other categorical data problems.
Module 8: Composite Estimators
Learn how to combine multiple models to improve prediction accuracy. This module introduces composite estimators and how they can be used to enhance model performance.
Module 9: Model Selection and Evaluation
Master the techniques for selecting the best model and evaluating its performance. Understand metrics, cross-validation, and the trade-offs involved in model selection.
Module 10: Feature Selection Techniques
Learn the importance of feature selection in model building, and explore various techniques to identify and select the most significant features for your models.
Module 11: Nearest Neighbors
Get hands-on with the Nearest Neighbors algorithm, a simple yet powerful method for classification and regression, particularly in high-dimensional spaces.
Module 12: Clustering Techniques
Explore clustering techniques to group similar data points together, an essential skill for unsupervised learning tasks like market segmentation and anomaly detection.
Module 13: Anomaly Detection
Learn how to identify outliers and anomalies in your data, a critical aspect of fraud detection, network security, and other domains.
Module 14: Support Vector Machines
Delve into Support Vector Machines (SVM), a powerful algorithm for classification and regression tasks, particularly in high-dimensional spaces.
Module 15: Dealing with Imbalanced Classes
Understand the challenges of working with imbalanced datasets and explore techniques to address these issues, ensuring your models are fair and accurate.
Module 16: Ensemble Methods
Learn how to boost your model’s performance using ensemble methods like bagging, boosting, and stacking, which combine the strengths of multiple models.
Case Studies
Case 1: Linear Regression
Apply linear regression techniques to real-world data, learning how to build and interpret models that predict continuous outcomes.
Case 2: Online Learning
Explore the dynamics of online learning models and how they adapt to new data, providing continuous improvement in predictions.
Case 3: Customer Churn Prediction
Use data science techniques to predict customer churn, helping businesses retain valuable customers by identifying at-risk individuals.
Case 4: Income Prediction
Build models to predict income levels based on various factors, a common task in socio-economic research and financial services.
Case 5: Predicting Employee Exit
Analyze employee data to predict who might leave the company, providing valuable insights for human resources and organizational planning.
