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PYTHON · STATISTICS · MACHINE LEARNING · NLP

Data Science with Python course.

Learn Python, statistics, data analysis, visualization and machine learning through practical exercises and projects.

Course overview

Progress through Python basics, statistics and probability, data analysis, visualization, machine learning and project work. The syllabus includes 27 lessons covering NumPy, Pandas, Matplotlib, Seaborn, time series, SciPy, web scraping, Scikit-learn and natural language processing.

Detailed syllabus

Overview
  • Why do we need Python?
  • Program structure
  • Environment Setup
  • Python Installation
  • Execution Types
  • What is an interpreter?
  • Interpreters vs Compilers
  • Using the Python Interpreter
  • Interactive Mode
  • Running python files
  • Working with Python shell
  • Integrated Development Environments (IDES)
  • Interactive Mode Programming
  • Script Mode Programming
Basic Concepts
  • Basic Operators
  • Types of Operator
  • Python Arithmetic Operators
  • Python Comparison Operators
  • Python Assignment Operators
  • Python Bitwise Operators
  • Python Logical Operators
  • Python Membership Operators (in, not in)
  • Python Identity Operators (is, is not)
  • Python Operators Precedence
  • Data Types
  • Variables
  • Assigning Values to Variables
  • Multiple Assignment
  • Python Numbers
  • Python Strings
  • Accessing Values in Strings
  • String Special Operators
  • String Formatting Operator
  • Triple Quotes
  • Built-in String Operations
  • Python Lists: Accessing Values in Lists
  • Updating Lists
  • Delete List Elements
  • Basic List Operations
  • Indexing, Slicing, and Matrixes
  • Built-in List Functions & Methods
  • Python Tuples
  • Accessing Values in Tuples
  • Updating Tuples
  • Delete Tuple Elements
  • Basic Tuples Operations
  • Indexing, Slicing, and Matrixes
  • No Enclosing Delimiters
  • Built-in Tuple Functions
  • Python Dictionary
  • Accessing Values in Dictionary
  • Updating Dictionary
  • Delete Dictionary Elements
  • Properties of Dictionary Keys
  • Built-in Dictionary Functions & Methods
Loops and Decision Making
  • if statements
  • if...else statements
  • nested if statements
  • while loop
  • for loop
  • nested loops
  • Loop Control Statements
  • 1) break statement
  • 2) continue statement
  • 3) pass statement
Functions
  • Defining a Function
  • Syntax
  • Calling a Function
  • Pass by reference vs value
  • Function Arguments
  • Required arguments
  • Keyword arguments
  • Default arguments
  • Variable-length arguments
  • The return Statement
  • Scope of Variables
  • Global vs. Local variables
Basic OOPs Concept
  • Creating class in Python; Documented String
  • Private Identifier
  • Constructor
  • Inheritance
  • Polymorphism
Python Modules and Packages
  • Framework vs Packages
  • Folium Introduction
  • Why are modules used?
  • Creating modules
  • The import Statement
  • The from...import Statement
  • The from...import * Statement
  • Locating Modules
  • The PYTHONPATH Variable
  • Namespaces and Scoping
  • The dir( ) Function
  • The globals() and locals() Functions
  • The reload() Function
  • Packages in Python
Advance Python
  • Decorator, Iterator and Generator
  • Anonymous Function
  • Lambda
  • Map
  • Filter
  • Reduce
  • SQL and Python
  • Overview of SQLite
  • Integrating Python with SQLite
  • Errors and Exception Handling
  • Standard exceptions
  • Assertions in Python
  • The assert Statement
  • What is Exception?
  • Handling an exception
  • Syntax
  • The except Clause with No Exceptions
  • The except Clause with Multiple Exceptions
  • The try-finally Clause
  • Argument of an Exception
  • Example with Tkinter Application
Data Science Overview
  • Data Science Disciplines
  • Data Science and Business Buzzwords Why are there so many
  • What is the difference between Analysis and Analytics
  • An Introduction--Business Analytics, Data Analytics, and Data Science
  • Data Science Diagram
  • Introduction -- BI, ML and AI
  • Careers in Data Science Fields
  • Data Overview
  • What is Data
  • Measuring Data
  • Measurement of Central Tendency
  • Measurements Dispersion
  • Measurement Quartile
  • Bi-variate Data and Co-variance
  • Pearson Correlation Coefficient
Probability
  • What is Probability
  • Permutations
  • Combinations
  • Intersections Unions and Complements
  • Independent and Dependent Events
  • Conditional Probability
  • Addition and Multiplication Rules
  • Bayes Theorem
Distributions
  • Introduction to Distributions
  • Uniform Distribution
  • Binomial Distribution
  • Poisson Distribution
  • Normal Distribution
Statistics
  • What is Statistics
  • Sampling
  • Central Limit Theorem
  • Standard Error
  • Hypothesis Testing
  • Hypothesis Testing Example Exercise
  • Type 1 and Type 2 Errors
  • Students T Distribution
  • Practical Example Descriptive Statistics Exercise
  • What are Confidence Intervals
  • Correlation Matrix
Anova
  • Introduction to ANOVA
  • Two Way ANOVA Overview
  • F- Distribution
Chi Square Analysis
  • Chi-Square Analysis
  • Chi Squared Analysis - Exercise Example
Python: Environment Setup and Essentials
  • Introduction to Anaconda
  • Installation of Anaconda Python Distribution – For Windows, Mac OS, and Linux
  • Jupyter Notebook Installation
  • Jupyter Notebook Introduction
Data Analysis- Numpy
  • Introduction to Numpy
  • Numpy Array
  • Numpy Indexing
  • Numpy Operations
  • Broadcasting Numpy Array
Data Analysis -- Pandas
  • Introduction to Pandas
  • Series
  • Data Frames
  • Missing Data
  • Groupby
  • Operations
  • Merging, Joining and concatenating
  • Missing Data
  • Data Input and Output
Pandas Exercise
  • Salaries Exercise
  • Ecommerce Purchases Exercise
Numpy Exercise
  • Solving Linear System
Matplotlib
  • Introduction
  • Matplotlib Drawing Graph -- Histogram, Plotting, Box Plot etc
  • Exercise
Seaborn
  • Introduction
  • Distribution
  • Categorical Plots
  • Matrix Plots
  • Regression Plots
  • Grids
  • Style and Colors
  • Exercise
Data Visualization with Pandas
  • Pandas Built-in Data Visualization
  • Pandas Data Visualization Exercise
Data Visualization - Geographical Plotting
  • Introduction to Geographical Plotting
  • Choropleth Maps - Part 1 - USA
  • Choropleth Maps - Part 2 - World
  • Choropleth Exercises; Capstone Project I
  • Calls Data Capstone Project
  • Finance Project
Time Series Analysis
  • Pandas for Time Series
  • Introduction to Time Series with Pandas
  • Date time Index
  • Time Re-sampling
  • Time Shifts
  • Pandas Rolling and Expanding
  • Time Series Analysis
  • Introduction to Time Series
  • Time Series Basics
  • Introduction to Statsmodel
  • ARIMA with Statsmodel
  • Capstone Project II
  • Stock Market Analysis Project
Scientific computing with Python (Scipy)
  • SciPy and its Characteristics
  • SciPy sub-packages
  • SciPy sub-packages –Integration
  • SciPy sub-packages – Optimize
  • Linear Algebra
  • SciPy sub-packages – Statistics
  • SciPy sub-packages – Weave
  • SciPy sub-packages – I O
Data Science with Python Web Scraping
  • Web Scraping
  • Common Data/Page Formats on The Web
  • The Parser
  • Importance of Objects
  • Understanding the Tree
  • Searching the Tree
  • Navigating options
  • Modifying the Tree
  • Parsing Only Part of the Document
  • Printing and Formatting
  • Encoding
Machine Learning with Python (Scikit–Learn)
  • Introduction to Machine Learning
  • Machine Learning Approach
  • How Supervised and Unsupervised Learning Models Work
  • Scikit-Learn
  • Supervised Learning Models – Linear Regression
  • Supervised Learning Models: Logistic Regression
  • K Nearest Neighbors (K-NN) Model
  • K Means Algorithm
  • SVMs
  • Unsupervised Learning Models: Clustering
  • Unsupervised Learning Models: Dimensionality Reduction
  • Pipeline
  • Model Persistence
  • Model Evaluation – Metric Functions
Natural Language Processing with Scikit-Learn
  • NLP Overview
  • NLP Approach for Text Data
  • NLP Environment Setup
  • NLP Sentence analysis
  • NLP Applications
  • Major NLP Libraries
  • Scikit-Learn Approach
  • Scikit – Learn Approach Built – in Modules
  • Scikit – Learn Approach Feature Extraction
  • Bag of Words
  • Extraction Considerations
  • Scikit – Learn Approach Model Training
  • Scikit – Learn Grid Search and Multiple Parameters
  • Pipeline

Projects

  • Calls data and finance capstones: Analyze calls data and explore financial datasets using Python.
  • Stock market analysis: Apply time-series analysis to stock market data.
  • Board game review prediction: Use linear regression to predict average board game reviews.
  • Credit card fraud detection: Apply anomaly detection using probability densities.
  • Stock market clustering: Use K-means and correlations in market movements to find related companies.
  • Natural language processing in Python: Practice word and sentence tokenization, part-of-speech tagging and phrase chunking.

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