TopTech MNC Employee Instructors:

Learn directly from industry leaders! Our courses feature instructors who are

Top tech MNC employees,bringing real-world experience and practical insights into the classroom. Stay ahead of the curve with knowledge that goes beyond traditional textbooks.

Partnership withTelangana's Largest ITStartup Incubators: As trail blazers in the education space, we've established partnerships with Telangana's largest IT startup incubators, including the renowned WowWarangal Startup Incubators. Immerse yourself in a n environment that nurtures innovation and prepares you for the dynamic world of startups.

Top MNC IndustryConnections:

Connect with the best in the industry through our extensive network of top multinational companies. Benefit from exclusive interactions, workshops, and networking opportunities with professionals at the forefront of technological advancement

IndustryConnections:

Our commitment t o bridging the gap between academia and industry is evident in our strong industry connections. Engage with industry experts, attend conferences, and join forums that provide valuable insights into the latest trends a n d developments.

Internship Opportunities:

Gain hands-on experience through exclusive internship opportunities with leading

companies. Apply your knowledge in practical settings, develop a robust skill set, and enhance your employability in the competitive tech landscape.

PlacementAssistance:

Your success is our priority! Our dedicated placement assistance program is designed to guide you toward rewarding career opportunities. Leverage our connections with top companies for a smooth transition from student to

professional.

Week 1-2: Introduction to Data Science and

Python Basics

Overview of Data Science and Al

Introduction to Python programming

language

Basic data types, variables, and operators Control flow and loops

Functions and modules

Week 3-4: Data Manipulation with Python Libraries

Introduction to NumPy for numerical operations

Data manipulation and analysis with Pandas

Data cleaning and preprocessing techniques

Week 5-6: Data Visualization Data visualization principles

Matplotlib and Seaborn for creating static plots

Interactive visualizations with Plotly

Week 7-8: Statistical Analysis and Probability

Descriptive statistics and measures of central tendency

Probability distributions

Week 9-10: Machine Learning Fundamentals

Introduction to supervised learning Linear regression and logistic regression

Model evaluation metrics

Week 11-12: More Supervised Learning Algorithms

Decision trees and random forests

Support Vector Machines (SVM) k-Nearest Neighbors (k-NN)

Week 13-14: Unsupervised Learning Clustering techniques: K-means, hierarchical, DBSCAN Dimensionality reduction: Principal Component Analysis (PCA)

Week 15-16: Introduction to Deep Learning

Basics of neural networks Introduction to TensorFlow and Keras

Building and training a simple neural network

Week 17-18: Advanced Deep Learning Convolutional Neural Networks

(CNNs) for image data

Recurrent Neural Networks (RNNs)

for sequential data

Transfer learning and pre-trained models

Week 19-20: Natural Language

Processing (NLP) Introduction to NLP

Text preprocessing and tokenization

Building NLP models with spaCy and NLTK

Week 21-22: Big Data and Distributed Computing Overview of big data concepts Introduction to Apache Spark Handling large datasets and distributed computing

Week 23-24: Model Deployment and Cloud Services

Model deployment strategies Introduction to cloud platforms (AWS, Azure, Google Cloud) Deploying a model on a cloud platform

Week 25: Capstone Project

Integrating skills learned throughout the course

Working on a real-world project Presentation and demonstration of

the project

Week 26: Final Exam