Data Science syllabus
Module 1: Introduction to Data Science
- Definition and scope of data science
- Overview of the data science life cycle
- Ethical considerations in data science
Module 2: Data Exploration and Preprocessing
- Data cleaning and handling missing values
- Exploratory Data Analysis (EDA)
- Feature scaling and normalization
- Data wrangling techniques
Module 3: Statistical Analysis and Hypothesis Testing
- Descriptive statistics
- Inferential statistics
- Hypothesis testing
- Statistical modeling and regression analysis
Module 4: Machine Learning Basics
- Introduction to machine learning
- Supervised and unsupervised learning
- Model evaluation and selection
Module 5: Predictive Modeling
- Linear and logistic regression
- Decision trees and ensemble methods (Random Forests, Gradient Boosting)/li>
- Model interpretation and evaluation
Module 6: Clustering and Dimensionality Reduction
- K-Means clustering
- Hierarchical clustering
- Principal Component Analysis (PCA)
- t-Distributed Stochastic Neighbor Embedding (t-SNE)
Module 7: Natural Language Processing (NLP)
- Text preprocessing and tokenization
- Bag-of-words and word embeddings
- Sentiment analysis
- Named Entity Recognition (NER)
Module 8: Big Data Technologies
- Introduction to big data concepts
- Hadoop and MapReduce
- Apache Spark for large-scale data processing
Module 9: Data Visualization
- Principles of effective data visualization
- Tools and libraries for data visualization (e.g., Matplotlib, Seaborn, Tableau)
- Designing informative and compelling visualizations
