• Understanding the role of data science in decision-making
• Overview of data science tools and technologies
• Introduction to Python programming for data science

• Data collection methods and sources
• Data cleaning and preprocessing techniques
• Exploratory Data Analysis (EDA) for insights

• Descriptive statistics and data visualization
• Inferential statistics and hypothesis testing
• A/B testing for business applications

• Introduction to machine learning concepts
• Supervised, unsupervised, and reinforcement learning
• Model selection and evaluation metrics

• Data visualization libraries (e.g., Matplotlib, Seaborn)
• Effective data storytelling and communication
• Building interactive dashboards

• Feature engineering techniques
• Feature selection methods (e.g., Recursive Feature Elimination)
• Dimensionality reduction with Principal Component Analysis (PCA)

• Linear regression and logistic regression
• Decision trees, random forests, and ensemble methods
• Clustering algorithms (e.g., K-Means, DBSCAN)
• Introduction to deep learning with neural networks

• Text preprocessing and tokenization
• Sentiment analysis and text classification
• Named Entity Recognition (NER) and text generation

• Introduction to Big Data technologies (e.g., Hadoop, Spark)
• Working with large datasets and distributed computing
• Scalable machine learning on big data

• Ethical considerations in data collection and analysis
• Privacy and data protection regulations
• Bias and fairness in machine learning models

• Resume building and interview preparation
• Effective communication in a technical role
• Time management and project planning
• Building a professional online presence

• Working on real-world data science projects
• End-to-end project development from data collection to deployment
• Collaborative capstone project with guidance from mentors

• Predictive Sales Analysis

•Recommendation System

• Image Classification

• Healthcare Predictive Analytics

• Fraud Detection