Data Science

Module 1: Introduction to Data Science

• Definition and Scope of Data Science
• Importance and Applications in Various Industries
• Data Science vs. Data Analytics vs. Machine Learning vs. AI
• The Data Science Lifecycle
• Tools and Technologies in Data Science (Python, R, SQL, Hadoop, Spark)

Module 2: Mathematics and Statistics for Data Science

• Linear Algebra Essentials (Vectors, Matrices, Eigenvalues)
• Probability Theory (Bayes’ Theorem, Random Variables, Distributions)
• Descriptive Statistics (Mean, Median, Mode, Variance, Standard Deviation)
• Inferential Statistics (Hypothesis Testing, Confidence Intervals)
• Correlation vs. Causation

Module 3: Programming for Data Science

• Python for Data Science:
  • Python Basics (Data Types, Loops, Functions)
  • Libraries: NumPy, Pandas, Matplotlib, Seaborn
  • Data Manipulation and Cleaning
• R for Data Science:
  • R Basics and Data Handling
  • Visualization using ggplot2
• SQL for Data Science:
  • Database Basics and Querying
  • Joins, Subqueries, and Window Functions

Module 4: Data Collection and Preprocessing

• Types of Data: Structured vs. Unstructured Data
• Data Sources: APIs, Databases, Web Scraping
• Handling Missing Data, Outliers, and Duplicate Data
• Data Transformation and Feature Engineering
• Data Normalization and Standardization

Module 5: Exploratory Data Analysis (EDA)

• Data Visualization Techniques (Histograms, Boxplots, Scatterplots)
• Identifying Patterns and Trends
• Correlation Analysis and Feature Selection
• Dimensionality Reduction Techniques (PCA, t-SNE)

Module 6: Machine Learning Fundamentals

• Supervised vs. Unsupervised Learning
• Training, Testing, and Validation Datasets
• Overfitting vs. Underfitting
• Model Evaluation Metrics (Accuracy, Precision, Recall, F1 Score, ROC Curve)

Module 7: Supervised Learning Algorithms

• Regression Models:
  • Linear Regression, Multiple Regression
  • Ridge, Lasso, and Polynomial Regression
• Classification Models:
  • Logistic Regression, Decision Trees, Random Forest
  • Support Vector Machines (SVM), K-Nearest Neighbors (KNN)

Module 8: Unsupervised Learning Algorithms

• Clustering Techniques (K-Means, DBSCAN, Hierarchical Clustering)
• Association Rule Learning (Apriori, FP-Growth)
• Anomaly Detection and Outlier Detection

Module 9: Deep Learning and Neural Networks

• Introduction to Neural Networks
• Activation Functions (ReLU, Sigmoid, Softmax)
• Backpropagation and Gradient Descent
• Convolutional Neural Networks (CNN) for Image Processing
• Recurrent Neural Networks (RNN) and LSTMs for Sequential Data

Module 10: Natural Language Processing (NLP)

• Text Preprocessing (Tokenization, Lemmatization, Stopword Removal)
• Sentiment Analysis and Named Entity Recognition (NER)
• Word Embeddings (Word2Vec, GloVe, BERT)
• Text Classification and Topic Modeling

Module 11: Big Data and Cloud Computing in Data Science

• Introduction to Big Data and Hadoop
• Apache Spark for Distributed Computing
• Cloud Platforms for Data Science (AWS, Google Cloud, Azure)
• Deploying Machine Learning Models on Cloud

Module 12: Data Science Project and Case Studies

• End-to-End Data Science Project Lifecycle
• Business Problem Formulation and Data Analysis
• Model Building, Evaluation, and Optimization
• Presentation of Insights and Data Storytelling
• Real-World Applications in Finance, Healthcare, Marketing, etc.