Data Science

Data science is a high-demand subject that is valued across in all sectors. If you are looking for beginner-friendly Data Science training in Vijayawada that ensures placements, codegnan has the perfect one for you. 

This six-month-long course will ensure that you are able to make data-driven decisions that can help you land a high-paying job.

Data Science is a discipline that uses subjects like statistics, scientific computing, and other methods to extract knowledge from data. This knowledge is then put into making informed knowledge in companies, which is much more reliable than taking decisions based on gut instincts. 

In this Data Science Course by codegnan, the prime objective is to make you capable of extracting actionable insights from large and complex datasets.

Curriculum:

• Python Introduction and setting up the environment
• Python Basic Syntax and Data Types
• Operators in Python
• Strings in Python
• Lists
• Tuples
• Sets
• Dictionaries
• Python conditional Statements
• Loops in Python
• Getting Started with Hackerrank use cases and working on them
• Lists and Dictionaries comprehension
• Functions
• Anonymous Function
• Generators
• Modules
• Packages
• Exception and Error Handling
• Classes and Objects (OOPS)
• Date and Time
• Regex
• Files
• APIs the Unsung Hero of the Connected World
• Web Scraping
• Data analysis EDA using Pandas and NumPy
• Data visualization using Matplotlib, Seaborn, and Plotly
• Database Access
• MS Excel
• Excel Worksheet
• Excel Calculation
• Excel Fill Handle
• Excel Formula
• Quick Excel Functions
• Excel Charts and visualizations
• Excel Advanced
• Tableau
• Power Bl
• Descriptive Statistics
• Inferential Statistics
• Introduction to Machine Learning
• Introduction to data science and its applications
• Data Engineering and Preprocessing
• Model Evaluation and Hyperparameter Tuning
• Supervised Learning – Regression
• Supervised Learning – Classification
• SVM, KNN & Naive Bayes
• Ensemble Methods and Boosting
• Unsupervised Learning – Clustering
• Unsupervised Learning – Dimensionality Reduction
• Recommendation Systems
• Reinforcement Learning
• Developing API using Flask / Webapp with Streamlit
• Deployment of ML Models
• Project Work and Consolidation
• Natural Language Processing NLP
• RISE OF THE DEEP LEARNING
• Artificial Neural Networks
• Convolution Neural Networks
• RNN – Recurrent Neural Networks
• Generative Models and GANs
• Computer Vision
• Real Time Drowsiness Detection Alert System
• House Price Prediction using LSTM
• Customizable Chabot using OpenAI API
• Fire and Smoke Detection using CNN

Data Science Projects And Case Studies You Will Work On:
 

• 1. Real-Time Drowsiness Detection Alert System
• Learn to collect and preprocess data, extract relevant features, and label it for drowsiness detection. Select and train a model for real-time implementation with alert mechanisms. Continuously improve the system's performance and accuracy.
• 2. Real-Time Rain Prediction using ML
• Install libraries and fetch live weather data with an API key. Preprocess data, train an ML model, and integrate it with Flask for real-time rain prediction. Test, debug, deploy, and keep the application updated with the latest weather data.
• 3. House Price Prediction using LSTM
• Scrape house price data, preprocess it, and explore additional sources if needed. Split and train the data using LSTM models. Evaluate, fine-tune (optional), and deploy the model. Continuously update the dataset and retrain the model for accurate predictions.
• 4. Fire and Smoke Detection using CNN
• Collect, preprocess, and augment the dataset for fire and smoke detection. Build a CNN model with architecture, train and evaluate it. Fine-tune for optimal performance, implement real-time inference, and set up thresholding and alert mechanisms. Optimize the model.
• 5. Customizable Chatbot using OpenAI API
• Define chatbot goals, gather and preprocess training data, integrate OpenAI API, and customize the model for user input handling and response generation. Implement post-processing, filtering, error handling, and fallback mechanisms for continuous improvement.