DATA SCIENCE APPLICATIONS (Professional Elective – V) B.Tech. IV Year I Sem. JNTUH R-18

• Analyze a real-world problem from a specific domain (e.g., finance, healthcare, retail) and explain how data science techniques could be used to address it.

• Compare and contrast different data science tools and libraries (e.g., Python libraries like Pandas, NumPy, Scikit-learn) based on their functionalities and suitability for particular tasks.

• Explain the concept of recommender systems and discuss different recommendation algorithms (e.g., collaborative filtering, content-based filtering) and their applications.

• Identify the challenges and opportunities associated with implementing data science solutions in various domains.

Unit II: Time Series and Supply Chain Management

• Develop a time series forecasting model (e.g., ARIMA, LSTM) to predict future values of a specific time series data like stock market indices.

• Analyze a real-world case study in supply chain management and explain how data science techniques can optimize logistics and inventory management.

• Discuss the challenges and potential benefits of applying data science in time series analysis and supply chain management.

Unit III: Data Science in Education and Social Media

• Explain how data science can be used to personalize learning experiences in education and assess student performance.

• Discuss the ethical considerations and potential biases involved in using data science tools in social media analysis.

• Analyze a case study related to data science applications in education or social media and evaluate its effectiveness.

Unit IV: Data Science in Healthcare and Bioinformatics

• Explain how data science can be used for disease diagnosis, treatment prediction, and drug discovery in healthcare.

• Discuss the role of bioinformatics in analyzing biological data and its applications in personalized medicine.

• Analyze a case study related to data science applications in healthcare or bioinformatics and discuss its potential impact.

Unit V: Case Studies in Data Optimization with Python

• Choose a real-world optimization problem and implement a solution using Python libraries like scipy.optimize.

• Analyze the performance of your optimization model and compare it to alternative approaches.

• Discuss the benefits and limitations of using Python for data optimization tasks.