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If you have any questions or need guidance regarding the Data Science course, feel free to reach out to our course counsellor. We are here to assist you every step of the way.

Contact Number: +91-9315027516

Address: F-16, Preet Vihar, Delhi, Opposite Preet Vihar Metro Station, Lift Gate No 2

Our counsellor will help with course details, registration, and any queries you may have. Don’t hesitate to get in touch!

Week 1-4: Foundations of Data Science

Week 1: Introduction to Data Science

• Topic Description: Overview of Data Science, its applications, and career pathways.
• Learning Outcomes: Understand what Data Science is, tools like Jupyter, Python, and R.
• Pedagogies: Lectures, Hands-on labs, case studies.

Week 2: Python for Data Science

• Topic Description: Fundamental programming in Python, focusing on NumPy and Pandas.
• Learning Outcomes: Write Python scripts, perform basic data cleaning, use Pandas DataFrames.
• Pedagogies: Live coding, assignments, quizzes.

Week 3: Data Manipulation with Pandas

• Topic Description: Deep dive into Pandas for efficient data handling and preparation.
• Learning Outcomes: Manipulate large datasets, handle missing data, and group operations.
• Pedagogies: Interactive coding, real-time projects.

Week 4: Data Visualization

• Topic Description: Introduction to Matplotlib and Seaborn for data visualization.
• Learning Outcomes: Create insightful visualizations for data analysis.
• Pedagogies: Lab sessions, mini-projects.

Week 5-8: Statistics and Probability for Data Science

Week 5: Descriptive Statistics

• Topic Description: Understanding central tendency, variability, and distribution of data.
• Learning Outcomes: Compute mean, median, mode, variance, and standard deviation.
• Pedagogies: Hands-on exercises, quizzes.

Week 6: Probability Theory

• Topic Description: Basic probability concepts and their role in data science.
• Learning Outcomes: Apply probability theory, conditional probability, Bayes’ Theorem.
• Pedagogies: Case studies, quizzes.

Week 7: Hypothesis Testing

• Topic Description: Inferential statistics, p-values, and confidence intervals.
• Learning Outcomes: Conduct hypothesis testing using t-tests, chi-square tests.
• Pedagogies: Group discussions, hands-on exercises.

Week 8: Probability Distributions

• Topic Description: Explore normal, binomial, and Poisson distributions.
• Learning Outcomes: Work with various probability distributions in real-world scenarios.
• Pedagogies: Lectures, hands-on exercises.

Week 9-12: Machine Learning – Supervised Learning

Week 9: Introduction to Machine Learning

• Topic Description: Overview of machine learning concepts, types, and workflow.
• Learning Outcomes: Understand the difference between supervised and unsupervised learning.
• Pedagogies: Lectures, hands-on labs.

Week 10: Linear Regression

• Topic Description: Simple and multiple linear regression techniques.
• Learning Outcomes: Build and interpret linear regression models.
• Pedagogies: Case study, hands-on labs.

Week 11: Classification Algorithms – Logistic Regression and KNN

• Topic Description: Introduction to logistic regression and KNN for classification.
• Learning Outcomes: Apply logistic regression and KNN for classification tasks.
• Pedagogies: Coding sessions, group assignments.

Week 12: Decision Trees and Random Forests

• Topic Description: Decision tree algorithms and random forests for classification.
• Learning Outcomes: Implement decision trees and random forest models.
• Pedagogies: Labs, team-based projects.

Week 13-16: Machine Learning – Unsupervised Learning & Model Evaluation

Week 13: Clustering Techniques – K-Means & Hierarchical Clustering

• Topic Description: Clustering algorithms for unsupervised learning.
• Learning Outcomes: Implement K-means and hierarchical clustering.
• Pedagogies: Lab exercises, industry case studies.

Week 14: Dimensionality Reduction – PCA

• Topic Description: Principal Component Analysis (PCA) and its applications.
• Learning Outcomes: Apply PCA to reduce dimensions of large datasets.
• Pedagogies: Hands-on labs, tutorials.

Week 15: Model Evaluation and Hyperparameter Tuning

• Topic Description: Techniques for model evaluation and performance improvement.
• Learning Outcomes: Perform cross-validation, hyperparameter tuning, and optimize models.
• Pedagogies: Coding, quizzes, projects.

Week 16: Feature Engineering and Data Preprocessing

• Topic Description: Techniques for transforming raw data into more useful features.
• Learning Outcomes: Apply data preprocessing techniques to improve model accuracy.
• Pedagogies: Coding sessions, experiments on real-world datasets.

Week 17-20: Deep Learning and Advanced Topics

Week 17: Introduction to Neural Networks

• Topic Description: Basics of neural networks and backpropagation.
• Learning Outcomes: Understand the architecture and implementation of neural networks.
• Pedagogies: Coding exercises, quizzes, labs.

Week 18: Convolutional Neural Networks (CNNs)

• Topic Description: CNNs for image processing and classification.
• Learning Outcomes: Build and train CNN models for image classification.
• Pedagogies: Labs, mini-projects.

Week 19: Natural Language Processing (NLP)

• Topic Description: Introduction to text data processing and NLP techniques.
• Learning Outcomes: Apply tokenization, stemming, and text classification models.
• Pedagogies: Labs, case studies, quizzes.

Week 20: Time Series Analysis

• Topic Description: Analyze time series data and apply forecasting models.
• Learning Outcomes: Implement ARIMA, analyze trends, seasonality in time series data.
• Pedagogies: Real-time datasets, case studies, labs.

Week 21-24: Industry Project, Capstone, and Assessments

Week 21-22: Industry Project

• Topic Description: Work on a real-world project provided by industry partners.
• Learning Outcomes: Apply data science concepts to real-world business problems.
• Pedagogies: Project-based learning, group discussions, mentorship.

Week 23: Capstone Project

• Topic Description: Final project integrating multiple data science concepts.
• Learning Outcomes: Build a comprehensive data science solution and present findings.
• Pedagogies: Capstone project presentation, peer reviews.

Week 24: Assessment & Job Readiness

• Topic Description: Final assessments, mock interviews, and job placement preparation.
• Learning Outcomes: Ace technical interviews and coding challenges.
• Pedagogies: Mock interviews, resume reviews, coding challenges.

6-Month Internship Program

• Description: Internship with top companies in India, applying data science to business problems.
• Learning Outcomes: Gain industry experience, work on live projects, develop a portfolio.