Data Science Course Syllabus

Data Science Course Syllabus

Course Overview

This course is designed to provide a comprehensive introduction to the field of Data Science. Students will learn the theoretical foundations and practical applications of data science techniques, including data analysis, machine learning, data visualization, and statistical modeling. The course will cover both Python and R programming languages.

Course Objectives

• Understand the data science process and lifecycle.
• Develop skills in data wrangling, exploration, and visualization.
• Gain proficiency in statistical analysis and machine learning algorithms.
• Implement and evaluate predictive models.
• Communicate insights effectively using data visualization tools.

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Week 1: Introduction to Data Science

• Overview of Data Science
• The Data Science Process
• Tools and Technologies in Data Science
  • Python vs. R
  • Jupyter Notebooks
  • Integrated Development Environments (IDEs)
• Introduction to Python/R for Data Science

Week 2: Data Collection and Preprocessing

• Data Types and Sources
• Data Collection Techniques
• Data Cleaning and Preparation
  • Handling Missing Data
  • Data Transformation and Normalization
  • Outliers Detection and Treatment
• Exploratory Data Analysis (EDA)

Week 3: Data Wrangling

• Introduction to Pandas (Python) or dplyr (R)
• Data Manipulation Techniques
  • Merging and Joining Data
  • Grouping and Aggregating Data
  • Reshaping Data
• Working with Time-Series Data

Week 4: Data Visualization

• Importance of Data Visualization
• Tools for Data Visualization
  • Matplotlib, Seaborn (Python)
  • ggplot2 (R)
• Creating Visualizations
  • Line Plots, Bar Charts, Histograms
  • Scatter Plots, Box Plots, Heatmaps
• Interactive Visualizations with Plotly

Week 5: Introduction to Statistics

• Descriptive Statistics
  • Mean, Median, Mode
  • Variance and Standard Deviation
  • Correlation and Covariance
• Inferential Statistics
  • Hypothesis Testing
  • Confidence Intervals
  • p-values

Week 6: Probability Theory

• Basics of Probability
• Probability Distributions
  • Normal Distribution
  • Binomial Distribution
  • Poisson Distribution
• Bayes’ Theorem and Applications

Week 7: Introduction to Machine Learning

• Overview of Machine Learning
• Types of Machine Learning
  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning
• The Machine Learning Pipeline

Week 8: Supervised Learning – Regression

• Linear Regression
  • Simple Linear Regression
  • Multiple Linear Regression
• Evaluation Metrics for Regression
  • R-squared
  • Mean Squared Error (MSE)
  • Root Mean Squared Error (RMSE)

Week 9: Supervised Learning – Classification

• Logistic Regression
• k-Nearest Neighbors (k-NN)
• Decision Trees and Random Forests
• Support Vector Machines (SVM)
• Evaluation Metrics for Classification
  • Accuracy, Precision, Recall
  • Confusion Matrix
  • ROC Curve and AUC

Week 10: Unsupervised Learning

• Clustering Techniques
  • k-Means Clustering
  • Hierarchical Clustering
• Dimensionality Reduction
  • Principal Component Analysis (PCA)
  • t-SNE
• Association Rule Learning
  • Apriori Algorithm

Week 11: Advanced Machine Learning

• Ensemble Methods
  • Bagging, Boosting, and Stacking
  • XGBoost, LightGBM
• Neural Networks and Deep Learning
  • Introduction to Neural Networks
  • Convolutional Neural Networks (CNNs)
  • Recurrent Neural Networks (RNNs)

Week 12: Model Evaluation and Optimization

• Model Validation Techniques
  • Cross-Validation
  • Bootstrapping
• Hyperparameter Tuning
  • Grid Search
  • Random Search
• Model Overfitting and Underfitting
• Feature Selection and Engineering

Week 13: Big Data and Cloud Computing

• Introduction to Big Data
  • Hadoop and Spark
• Cloud Computing for Data Science
  • AWS, Google Cloud, Azure
• Distributed Computing

Week 14: Natural Language Processing (NLP)

• Text Preprocessing
  • Tokenization, Stemming, Lemmatization
  • Stopword Removal
• Sentiment Analysis
• Topic Modeling
  • Latent Dirichlet Allocation (LDA)
• Word Embeddings
  • Word2Vec, GloVe

Week 15: Data Ethics and Privacy

• Ethical Issues in Data Science
• Bias and Fairness in Algorithms
• Data Privacy and Security
• Case Studies

Week 16: Capstone Project

• Project Overview and Guidelines
• Data Collection and Preparation
• Model Selection and Implementation
• Final Report and Presentation

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Recommended Textbooks & Resources

• “Python for Data Analysis” by Wes McKinney
• “R for Data Science” by Hadley Wickham & Garrett Grolemund
• “Introduction to Statistical Learning” by Gareth James et al.
• “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” by Aurélien Géron

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Assessment Methods

• Weekly Quizzes
• Midterm Exam
• Final Exam
• Assignments and Case Studies
• Capstone Project

Software Requirements

• Python 3.x
• R and RStudio
• Jupyter Notebook
• Anaconda Distribution
• Relevant libraries: NumPy, Pandas, Scikit-learn, Matplotlib, Seaborn, TensorFlow, etc.