ºÃÉ«ÏÈÉú

• Understanding the role of data science in healthcare and public health system (30 min)
• Overview of the data science process: data collection, cleaning, exploration, modeling, evaluation, and deployment (1 hour and 30 minutes)
• Introduction to data science tools and programming languages (30 min) 

Module 1: Introduction to Python (Lecture time: 1 hour) 

• What is Python? (10 min)
• Why Python? (5 min)
• Installing Python (15 min)
• Python IDEs (15 min)
• Jupyter Notebook Overview (15 min)

Module 2: Python Basics (Lecture time: 9 hours)

• Python Basic Data types (30 min)
• Expression (30 min)
• Flow control (1 hour)
• Lists (1 hour)
• Slicing (1 hour)
• IF statements (1 hour)
• Loops (1 hour)
• Dictionaries (1 hour)
• Tuples (1 hour)
• Functions (1 hour) 

• Data ETL (Extract, Transform, Load) (15 min)
• Data Cleaning (15 min)
• Pandas (15 min)
• Numpy (15 min)
• Matplotlib (15 min)
• Seaborn (15 min)
• Univariate Non-graphical (15 min)
  • Central tendency
  • Spread
  • Skewness and kurtosis
• Multivariate Non-graphical (15 min)
• Univariate graphical (15 min)
  • Histogram
  • Stem-and-leaf-plots
  • Boxplots
• Multivariate graphical (15 min)
  • line chart
  • Scatterplot
  • Heat map
  • Bubble chart

Module 1: Introduction to Database (Lecture time: 1 hour)

• List the features of Oracle Database 11 g
• Discuss the basic design, theoretical, and physical aspects of a relational database 
• Categorize the different types of SQL statements 
• Describe the data set used by the course
• Log on to the database using SQL Developer environment
• Save queries to files and use script files in SQL Developer 

Module 2: Retrieve Data using the SQL SELECT Statement (Lecture time: 1 hour) 

• List the capabilities of SQL SELECT statements
• Generate a report of data from the output of a basic SELECT statement
• Select All Columns
• Select Specific Columns 
• Use Column Heading Defaults 
• Use Arithmetic Operators 
• Understand Operator Precedence
• Learn the DESCRIBE command to display the table structure 

Module 3: Learn to Restrict and Sort Data (1 hour)

• Write queries that contain a WHERE clause to limit the output retrieved
• List the comparison operators and logical operators that are used in a WHERE clause
• Describe the rules of precedence for comparison and logical operators
• Use character string literals in the WHERE clause
• Write queries that contain an ORDER BY clause to sort the output of a SELECT statement
• Sort output in descending and ascending order

Module 4: Usage of Single-Row Functions to Customize Output (1 hour)

• Manipulate strings with character function in the SELECT and WHERE clauses
• Perform arithmetic with date data
• Manipulate dates with the DATE Functions

Module 5: Aggregate Data using the Group Functions (30 min)

• Use the aggregation functions in SELECT statements to produce meaningful reports
• Divide the data into groups by using the GROUP BY clause 
• Exclude groups of date by using the HAVING clause 

Module 6: Display Data from Multiple Tables Using Joins (15 min)

• Write SELECT statements to access data from more than one table 
• View data that generally does not meet a join condition by using outer joins

Module 7: Use Subqueries to Solve Queries (15 min)

• Describe the types of problems that subqueries can solve
• Define sub-queries 

• Tableau basic (20 min)
• Learn Tableau Dashboards (20 min)
• Learn Tableau Basic Reports (20 min)
• Learn Tableau Charts (20 min)
• Learn Tableau Advanced Reports (20 min)
• Learn Tableau Calculations & Filters (20 min)

Module 1: Descriptive statistics (Lecture time: 3 hours)

• Central Tendency (25 min)
  • Mean
  • Median
  • Mode
  • Normal Distribution 
  • Standard normal distribution 
  • Population
  • Sample
  • Skewness
• Standard Deviation (25 min)
• Data deviation & distribution (25 min)
• Variance (25 min)
• Distance metrics (25 min)
  • Euclidean Distance
  • Manhattan Distance
• Correlation (25 min)
  • Pearson correlation 
• Variable analysis (25 min)
  • Univariate
  • Bivariate
  • Multivariate
• Outlier analysis (25 min)
  • What is an Outlier?
  • Interquartile Range
  • Box & whisker plot
  • Upper Whisker
  • Lower Whisker
  • Scatter plot
  • Cook's Distance

Module 2: Inferential statistics (Lecture time: 1 hour) 

• Probability Basics 
  • What does it mean by probability? (5 min)
  • Rules of probability (5 min)
  • Types of probability (5 min) 
  • ODDS Ratio (5 min)
  • Central limit theorem (5 min)
  • Probability of multiple events (5 min) 
  • Binomial distribution
  • Continuous Probability distribution (5 min) 
  • Bernoulli distribution (5 min) 
  • Normal distribution (5 min) 
  • Standard Normal distribution (5 min) 
  • Uniform distribution (5 min) 
  • Baye's theorem (5 min) 

Module 3: Hypothesis Testing (Lecture time: 1 hour) 

• Null and Alternate Hypothesis (5 min) 
• Critical value Method (5 min) 
• P-value Method (5 min) 
• Pearson's Correlation Coefficient (5 min) 
• Spearman's Rank Correlation (5 min) 
• Chi-Squared Test (5 min) 
• Student's t-test (5 min) 
• Analysis of Variance Test (ANOVA) (5 min) 
• A/B testing (5 min) 

Module 1: Feature Engineering (Lecture time: 2 hours) 

• Missing data imputation (10 min) 
• Categorical encoding (10 min) 
• Variable transformation (10 min) 
• Outlier engineering (10 min) 
• Date and time engineering (10 min) 

Module 2: Supervised Learning (Lecture time: 5 hours) 

• Regression (2 hours) 
  • Linear Regression (40 min) 
  • Multiple Linear Regression (40 min) 
  • Polynomial Linear Regression (40 min) 
• Classification (3 hours) 
  • Logistic regression (30 min) 
  • Naive Bayes Classifier (30 min) 
  • K -- Nearest Neighbour (30 min) 
  • SVM algorithm (30 min) 
  • Decision Trees (30 min) 
  • Random Forest (30 min) 
  • Boosting algorithm (30 min) 

Module 3: Model Evaluation and Validation (Lecture time: 2 hours) 

• Classification (1 hour) 
  • Confusion Matrix (10 min) 
  • Precision (5 min) 
  • Recall (5 min) 
  • Specificity (5 min) 
  • F1 Score (5 min) 
  • Cross Validation (5 min) 
  • Overfitting vs Underfitting (5 min) 
  • Ridge and Lasso (5 min) 
  • ROC curve (5 min) 
  • Gini (5 min) 
  • Entropy (5 min) 
• Regression (1 hour) 
  • MSE (15 min) 
  • R squared (15 min) 
  • RMSE (15 min) 
  • MAPE (15 min) 

Module 4: Dimensionality Reduction Methods (Lecture time: 2 hours) 

• Principal Component Analysis (30 min) 
• Singular Value Decomposition (30 min) 
• Linear Discriminant Analysis (30 min) 
• Isomap Embedding (30 min) 

Module 5: Unsupervised Learning (Lecture time: 1 hours) 

• K-means Clustering (1 hour) 

• Introduction (40 min) 
• Shallow Neural Networks (40 min) 
• Deep Neural Networks (40 min) 
• Loss Functions (40 min) 
• Training Models (40 min) 
• Gradients and Initialization (40 min) 
• Measuring Performance (40 min) 
• Regularization (40 min) 
• Convolutional Networks (40 min) 
• Transformers (40 min) 
• Generative Adversarial Networks (40 min) 
• Diffusion models (40 min) 

• Image Resizing (20 min) 
• Image Rotation (20 min) 
• Image Translation (20 min) 
• Image Shearing (20 min) 
• Image Normalization (20 min) 
• Morphological Image Processing (20 min) 
• Gaussian Image Processing (20 min) 
• Edge Detection in image processing (20 min) 

Module 1: Introduction to NLP (Lecture time: 1 hour) 

• Text Pre-processing (10 min) 
• Noise Removal (10 min) 
• Lexicon Normalization (10 min) 
• Lemmatization (10 min) 
• Stemming (10 min) 
• Object Standardization (10 min) 

Module 2: Text to Features (Feature Engineering) (Lecture time: 1 hour)

• Syntactic Parsing (5 min)  
• Dependency Grammar (5 min) 
• Part of Speech Tagging (5 min) 
• Entity Parsing (5 min)  
• Named Entity Recognition (5 min) 
• Topic Modeling (5 min) 
• N-Grams (5 min) 
• TF -- IDF (5 min) 
• Frequency / Density Features (5 min) 
• Word Embedding (5 min) 

Module 3: Tasks of NLP (Lecture time: 1 hour) 

• Text Classification (15 min)
• Text Matching (15 min) 
• Levenshtein Distance (15 min) 
• Phonetic Matching (15 min) 
• Flexible String Matching (15 min) 

Module 4: Large Language Model (Lecture time: 2.5 hours) 

• What is LLM (30 min)
• Structure of LLM(1 hour) 
• Fine Tuning of LLM (1 hour)