Data Science course

What Will You Learn?

• Course Modules:
• Module 1: Introduction to Data Science
• - What is Data Science?
• - Definition and scope
• - Importance and applications
• - Data Science Process
• - Data collection
• - Data cleaning
• - Data analysis
• - Data visualization
• - Interpretation and reporting
• - Tools and Technologies
• - Overview of Python, R, SQL, and others
• - Introduction to Jupyter Notebooks
• Module 2: Python for Data Science
• - Python Basics
• - Syntax and data types
• - Control structures (loops, conditionals)
• - Functions and modules
• - Libraries for Data Science
• - NumPy: Numerical computing
• - Pandas: Data manipulation
• - Matplotlib and Seaborn: Data visualization
• - Scikit-learn: Machine learning
• Module 3: Data Collection and Cleaning
• - Data Collection Methods
• - Web scraping
• - APIs
• - Databases
• - Data Cleaning Techniques
• - Handling missing data
• - Data normalization
• - Dealing with outliers
• - Data transformation
• Module 4: Exploratory Data Analysis (EDA)
• - Descriptive Statistics
• - Measures of central tendency (mean, median, mode)
• - Measures of dispersion (range, variance, standard deviation)
• - Data Visualization Techniques
• - Histograms, bar charts, and box plots
• - Scatter plots and correlation matrices
• - EDA Case Studies
• - Practical examples and hands-on exercises
• Module 5: Probability and Statistics
• - Probability Theory
• - Basic concepts (independence, conditional probability)
• - Probability distributions (normal, binomial, Poisson)
• - Statistical Inference
• - Sampling methods
• - Hypothesis testing
• - Confidence intervals
• Module 6: Machine Learning Basics
• - Introduction to Machine Learning
• - Supervised vs. unsupervised learning
• - Overview of algorithms
• - Supervised Learning
• - Linear regression
• - Classification (logistic regression, decision trees, random forests)
• - Model evaluation metrics (accuracy, precision, recall, F1 score)
• - Unsupervised Learning
• - Clustering (k-means, hierarchical)
• - Dimensionality reduction (PCA, t-SNE)
• Module 7: Advanced Machine Learning
• - Ensemble Methods
• - Bagging and boosting
• - Random forests and gradient boosting machines
• - Support Vector Machines (SVM)
• - Theory and applications
• - Neural Networks and Deep Learning
• - Basics of neural networks
• - Introduction to deep learning frameworks (TensorFlow, Keras)
• - Model Deployment and Serving
• - Deploying machine learning models
• - Model monitoring and maintenance
• Module 8: Natural Language Processing (NLP)
• - Text Processing Techniques
• - Tokenization, stemming, and lemmatization
• - Bag of words and TF-IDF
• - NLP Applications
• - Sentiment analysis
• - Text classification
• - Named entity recognition
• - Introduction to NLP Libraries
• - NLTK, spaCy
• Module 9: Big Data Technologies
• - Introduction to Big Data
• - Definition and characteristics (Volume, Velocity, Variety)
• - Big Data Tools and Frameworks
• - Hadoop and MapReduce
• - Apache Spark
• - Data Processing with Spark
• - RDDs and DataFrames
• - Spark SQL and MLlib
• Module 10: Capstone Project
• - Project Planning and Design
• - Problem definition and goal setting
• - Data collection and preprocessing
• - Project Execution
• - Model development and evaluation
• - Results interpretation and reporting
• - Presentation and Review
• - Project presentation
• - Peer review and feedback
• Additional Resources:
• - Books and References
• - Recommended textbooks and papers
• - Online Courses and Tutorials
• - MOOCs and video tutorials
• - Communities and Forums
• - Joining data science communities for networking and support
• Assessment and Certification:
• - Quizzes and Assignments
• - Regular quizzes to test understanding
• - Hands-on assignments for practical experience
• - Final Exam
• - Comprehensive exam covering all modules
• - Certification
• - Certificate of completion for those who pass the final exam and capstone project
• This course content is designed to provide a solid foundation in data science, equipping students with the knowledge and skills needed to pursue a career in this rapidly growing field.