Syllabus

1. Programming with Python

1.1: Introduction to Python and Basic Concepts

• Introduction to Python and Data Science
• Setting up the Environment
• Basic Operators
  • Arithmetic Operators
  • Operator Precedence
  • Comparison Operators
• Variables and Data Types
  • Reassignment
  • Assignment Operators
  • Variable Naming Rules
  • Basic Data Types
  • Assigning Differet Data Types to Variables
  • Type Conversion Functions
• Strings in Detail
  • String Concatenation
  • String Replication
  • Escape Characters
  • String Indexing and Slicing
  • String Properties
  • String Functions
  • String Methods
• Basic Input and Output
  • Output
  • Formatting Options
  • Input

1.2. Data Structures and Control Flow

• Introduction to Data Structures
• Lists
  • Creating Lists
  • List Membership
  • Indexing and Slicing
  • Modifying Lists
  • List Methods
  • Nested Lists
• Tuples
  • Creating Tuples
  • When to use Tuples?
• Logical Operators
  • Chained Comparison Operators
• Conditional Statements
  • if, elif, and else
  • Nested Conditional Statements
  • Single-line Conditional Statements
  • Match Statement
• Loops
  • for Loops
  • while Loops
  • break, continue, pass
  • Nested Loops
• List Comprehensions
  • The Basic Syntax
  • Adding a Condition
  • if-else in List Comprehensions
  • Nested List Comprehensions

1.3. Advanced Data Structures and Functions

• Dictionaries
  • Creating Dictionaries
  • Accessing Values
  • Modifying Dictionaries
  • More complex Dictionaries
  • Operations on Dictionaries
• Sets
  • Creating Sets
  • Set Operations
• Introduction to Functions
  • Writing and Calling Functions
• Function Arguments
  • Positional Arguments
  • Keyword Arguments
  • Using return statement
  • Adding logic to functions
• Scope of Variables

1.4. Advanced Functions and Functional Programming for Data Processing

• What is Functional Programming?
• Lambda Functions
• map, filter, and reduce
  • map
  • filter
  • reduce
• List Comprehensions
  • Basic Syntax
  • Adding a Condition
  • if-else in List Comprehensions
• Dictionary and Set Comprehensions
  • Dictionary Comprehensions
  • Set Comprehensions
• Error Handling (try...except)
  • Basic Syntax
  • Handling Multiple Exceptions
  • else and finally clauses
• Raising Exceptions (raise)
• Assertions

1.5. Object-Oriented Programming (OOP)

• Introduction to OOP
• Creating Classes
  • Defining a Class
  • Creating an Instance (Object)
  • A different example
• Inheritance
• Polymorphism
• Encapsulation
• Special Methods
• Data Classes

1.6. Advanced Data Structures and Iterators

• Args and Kwargs
  • *args
  • **kwargs
  • Using *args and **kwargs together
• Generators
• Iterators
• Decorators
  • Examples
  • Built-in Decorators
• Reading and Writing Files
  • Pathlib Module
  • File Operations
• Introduction to Python's Standard Library
  • Collections Module
  • Itertools Module
  • Random Module

1.7. Advanced Topic (Not Covered in the Course)

• Advanced OOP and Design Patterns
  • Metaclasses
  • Descriptors
  • Abstract Base Classes (ABCs)
  • Common Design Patterns
  • Data Model Protocols
• Concurrency and Parallelism
  • Introduction to Concurrency
  • Threading Module
  • Multiprocessing Module
  • Asyncio
  • Concurrent Data Processing
• Performance and Optimization
  • Profiling Code
  • Memoization and Caching
  • Working with C/C++ extensions
  • Memory Management
  • Vectorization Principles
  • Numerical Computing Foundations
• Production-Ready Data Science Code
  • Creating Data Science Packages
  • Virtual Environments and Dependency Management
  • Testing Data Pipelines
  • Code Style and Documentation
  • Logging and Monitoring
  • Bridging to Data Science Libraries

2. Data Analysis and Statistics

2.1. Numerical Computing with NumPy

• Introduction to NumPy
• NumPy Arrays (ndarrays)
  • Creating NumPy Arrays
  • Array Attributes
  • Reshaping Arrays
  • Data Types
  • Array from Functions
• Array Indexing and Slicing
  • 1D Array Indexing and Slicing
  • 2D Array Indexing and Slicing
  • Ellipsis
  • Boolean Indexing
• Array Operations
  • Vectorized Operations
  • Broadcasting
  • Stacking Arrays
  • Splitting Arrays
  • Transposing Arrays
• Mathematical and Statistical Functions
• Linear Algebra with NumPy
  • Examples
  • Solving a system of linear scalar equations

2.2. Data Manipulation with Pandas

• Introduction to Pandas
• Pandas Data Structures
• Series
  • Creating a Series
  • Index
  • Automatic Alignment
  • Name of the Series
  • Conditional Replace
  • Counting values
  • Applying a Function
  • Series Utils
• DataFrame, IO, Multi-Index
  • Creating a DataFrame
  • Reading and Writing Data
  • Multi-Index DataFrame
  • Operations on Indexes and Columns
• Sort, Index, Ops
  • Accessing Rows and Columns
  • Sorting a DataFrame
  • Operations on DataFrames
  • Data Cleaning and Preparation
• Data Transformations
  • Merging, Joining, and Concatenating DataFrames
  • Aggregating and Grouping Data
  • Aggregate, Filter, Transform, Apply
  • Pivot Tables
• String and DateTime
  • Vectorized String Operations
  • Date/Time Manipulation

2.3. Visualization with Matplotlib and Seaborn (Vanderplas' Book)

• Simple Line Plots
  • Adjusting the Plot: Line Colors and Styles
  • Adjusting the Plot: Axes Limits
  • Labeling Plots
• Simple Scatter Plots
  • Scatter Plots with plt.plot
  • Scatter Plots with plt.scatter
  • Visualizing Uncertainties
• Density and ContourPlots
  • Visualizing a Three-Dimensional Function
  • Histograms, Binnings, and Density
  • Two-Dimensional Histograms and Binnings

• Multiple Subplots
  • plt.axes: Subplots by Hand
  • plt.subplot: Simple Grids of Subplots
  • plt.subplots: The Whole Grid in One Go
  • plt.GridSpec: More Complicated Arrangements

• Visualization with Seaborn
  • Exploring Seaborn Plots
    • Histograms, KDE, and Densities
    • Pair Plots
    • Faceted Histograms
  • Categorical Plots
    • Joint Distributions
    • Bar Plots
  • Violin Plots
  • Heatmaps

2.4. SQL for Data Analysis (from a Pandas Perspective)

• Introduction to Relational Databases and SQL
  • Comments
  • Data Types
  • Constraints
• Querying and Selecting Data (similar to df[] and df.loc)
  • Basic Queries: SELECT, FROM, WHERE
  • Aliases
  • DISTINCT for unique values
• Filtering and Sorting Data (similar to boolean indexing and df.sort_values())
  • ORDER BY
  • LIMIT
  • Logical operators: AND, OR, NOT
  • Comparison operators: IN, NOT IN, BETWEEN
  • Pattern matching with LIKE
  • Handling nulls with IS NULL, IS NOT NULL, COALESCE, NULLIF
• Data Manipulation and Cleaning (similar to df.apply, .astype, and string methods)
  • CASE statements for conditional logic
  • CAST for type conversion
  • String Functions
    • CONCAT
    • UPPER
    • LOWER
    • TRIM
    • REPLACE
    • LEN
    • LEFT
    • RIGHT
    • SUBSTRING
  • Numerical Functions
    • ABS
    • CEIL
    • FLOOR
    • ROUND
    • TRUNC
    • SQRT
    • POWER
    • LOG
  • Date Functions
    • EXTRACT / PART
    • DATE_TRUNC
    • DATEDIFF
    • DATE_ADD / DATE_SUB
• Aggregation and Grouping (similar to df.groupby() and aggregation functions)
  • Aggregate Functions: COUNT, SUM, AVG, MIN, MAX
  • GROUP BY
  • HAVING for filtering groups
  • Rollups and Cubes (briefly)
• Combining Tables (similar to pd.merge and pd.concat)
  • Keys
  • INNER, LEFT, RIGHT, FULL OUTER JOIN
  • Self Joins & Cross Join
  • UNION, UNION ALL
  • INTERSECT, EXCEPT
• Advanced Querying
  • Subqueries (Scalar, Multi-row, Correlated)
  • Common Table Expressions (CTEs) with WITH
  • Window Functions: ROW_NUMBER, RANK, DENSE_RANK, LEAD, LAG, NTILE
• Advanced Topics
  • Data Cleaning and Transformation (Pivot/Unpivot patterns)
  • DDL for Analysts: CREATE TABLE AS SELECT (CTAS), CREATE VIEW
  • Indexes, Triggers, Views, Stored Procedures, Transactions
  • Connectors and ORMs for Python (SQLAlchemy, psycopg2)

2.5. Analytics applications

• Interactive Visualization with Plotly
  • Getting Started: Plotly Express vs Graph Objects
  • Interactive Features: hover, zoom, selection
  • Styling and Layout: axes, annotations, themes
  • Subplots and Faceting
  • Advanced Charts: treemap, sunburst, polar, funnel
  • Maps: choropleth, scatter_mapbox
  • Time Series and Animation
  • Integrating with Pandas
  • Exporting: static images, HTML
• Streamlit
  • App Structure and streamlit run
  • Widgets and Interactivity
  • Forms and Validation
  • State Management with st.session_state
  • Caching with st.cache_data and st.cache_resource
  • Displaying DataFrames and Tables
  • Layout: sidebar, columns, tabs
  • Visualizations: Matplotlib, Seaborn, Plotly
  • File Upload/Download
  • Multipage Apps
  • Deployment Basics
• PowerBI
  • Introduction to PowerBI
  • Data Sources
  • Data Modeling
  • Data Visualization
  • Dashboards
  • Reports

3. Data Science and Machine Learning

• Scikit-learn

  • Statistics
  • Handling Outliers
  • Handling Nulls
  • Handling Categorical Variables
  • Feature Scaling
  • Logs, exponentials, etc.
  • Regression
  • Classification
  • Regression and Classification Models
  • Metrics
  • Ensembles
  • Tuning
  • Unsupervised learning
  • Dimensionality Reduction???

• Pytorch

  • Introduction to Pytorch
  • Tensor Operations
  • Neural Networks
  • Training Loop
  • Metrics
  • Evaluation
  • More Complex Models
  • Saving and Inference