Data Science Certification Training Course

An Introduction to PYTHON

Introductory Remark about Python

• A Brief History of Python
• How Python is differ from other languages
• Python Version

Getting Help

• How To execute Python program
• Writing your first program google colab, vs code and anaconda

Python Basics

• Python keywords,variables and Identifiers
• Python statements
• Comments in python
• Command line arguments
• Getting user input
• Exercise

Decision making & Loops 

• Introduction
• Control flow and syntax
• The if statement
• Python operators
• The while Loop
• Break and continue
• The for Loop
• Pass statement

Data Stuctures

• Introduction
• List
• Tuples
• String
• Dictionary
• Set
• Exercise

Functions in Python

• Introduction
• Calling a function
• Function arguments
• Built in function
• Scope of variables
• Decorators
• Passing function to a function
• Lambda
• Closures
• Exercise

Modules and Packages

• Modules
• Importing Modules
• Standard Modules- sys
• Standard Modules- OS
• The dir Function
• Packages
• Exercise

Exception Handling

• Errors
• Run Time Errors
• Handling IO Exception
• Try….except statement
• Raise
• Assert

Files and Directories

• Introduction
• Writing Data to a file
• Reading data from a file
• Additional file methods
• Working with files
• Working with Directories
• The pickle Module

Classes & Objects

• Introduction classes and objects
• Creating classes
• Instance methods
• Special class method
• Inheritance
• Method overriding
• Data hiding
• Exercise

Web Scrapping using Beautiful Soup

• Introduction to BeautifulSoup
• Understanding how BeautifulSoup works.
• Initializing a BeautifulSoup object with HTML content.
• Navigating the HTML Tree Structure
• Accessing specific elements by tag (find, find_all).
• Working with attributes (class, id).
• Extracting Content
• Extracting text from elements.
• Extracting links, images, and other attributes.
• Dealing with Nested HTML Structures
• Extracting content from nested tags.
• Navigating through parents, children, and sibling elements.

Request Module in Python

Understanding HTTP Protocol

What is HTTP?

Key components of an HTTP request: URL, headers, body, method (GET, POST, etc.).

HTTP status codes (200 OK, 404 Not Found, etc.).

Overview of Python's requests Module

Introduction to the requests library.

Comparison with other modules (urllib vs. requests).

GET Request

Syntax: requests.get().

Passing query parameters (params).

Inspecting response objects (response.status_code, response.content, response.text).

POST Request

Syntax: requests.post().

Sending form data using data or json parameters.

Other HTTP Methods (Overview)

PUT, DELETE, HEAD, OPTIONS (with examples).
    
Inspecting Response Data

Accessing response body (response.text vs response.content vs response.json()).

Checking status codes and their meaning (response.status_code)

Extracting response headers (response.headers).

JSON Responses

Working with JSON data (response.json()).

Handling non-JSON responses.

Sending Query Parameters in a GET Request

Using the params parameter to include query strings.

Example: requests.get(url, params={'key1': 'value1', 'key2': 'value2'}).

Sending Data in a POST Request

Sending form data (data vs. json).

• Overview of Data Analysis
• Applications of Data Analysis in Real-world Scenarios
• Data Types and Data Structures in Data Analysis

• Introduction to NumPy
• Working with Arrays
• Array Operations and Broadcasting
• Mathematical and Statistical Functions in NumPy

• Introduction to Pandas Library for Data Manipulation
• Loading and Inspecting Datasets
• Filtering, Sorting, and Selecting Data , indexing , renaming
• Handling Missing Data
• Grouping and Aggregating Data

• Handling Duplicates and Invalid Data
• Data Transformation Techniques
• Data Imputation Techniques
• Data Consistency and Integrity Checks

• Introduction to EDA
• Descriptive Statistics
• Detecting and Treating Outliers
• Data Distribution Visualization
• Correlation and Covariance Analysis

• Introduction to Hypothesis Testing (30 min)
• Types of Hypotheses (Null and Alternative) (45 min)
• t-Tests and ANOVA (1 Hour)
• Chi-Square Test (45 min)
• Interpreting p-values (45 min)

• Introduction to Matplotlib for Plotting
• Creating Basic Line, Bar, and Scatter Plots
• Customizing Plot Elements
• Multi-plot Figures and Subplots

• Introduction to Seaborn Library
• Creating Heatmaps, Boxplots, and Violin Plots
• Pairplots and Jointplots for Multivariate Analysis
• Customizing Seaborn Visualizations

• Introduction to Time Series Data
• Working with DateTime in Pandas
• Plotting Time Series Data
• Rolling, Shifting, and Resampling Time Series Data

• Creating Dashboards with Matplotlib and Seaborn
• Storytelling with Data
• Case Study: Building a Dashboard for Business Insights

• What is Machine Learning?
• Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
• Applications of ML in the Real World

• Introduction to Regression
• Simple Linear Regression
• Multiple Linear Regression
• Polynomial Regression
• Regularization: Ridge, Lasso

• Introduction to Classification
• Logistic Regression
• K-Nearest Neighbors (KNN)
• Support Vector Machines (SVM)
• Naive Bayes Classifier

• Confusion Matrix, Precision, Recall, F1-Score
• ROC Curve and AUC
• Cross-Validation
• Grid Search and Random Search

• Introduction to Decision Trees
• Decision Tree Algorithm: Classification and Regression
• Random Forests and Bagging

• Introduction to Clustering
• K-Means Clustering
• Hierarchical Clustering
• DBSCAN Algorithm

• Introduction to Dimensionality Reduction
• Principal Component Analysis (PCA)

• Introduction to Ensemble Methods
• Bagging: Random Forests
• Boosting: AdaBoost, Gradient Boosting, XGBoost

• Introduction to Time Series
• Time Series Forecasting with ARIMA
• Exponential Smoothing and Moving Averages

• Introduction to Neural Networks
• Perceptron, Activation Functions

• Case Study 1: Regression
• Case Study 2: Classification
• Case Study 3: Clustering
• Final Project: Comprehensive ML Model

• What is Deep Learning?
• Neural Networks Overview
• Key Concepts: Neurons, Weights, Activation Functions (45 min)

• Architecture of Feedforward Neural Networks
• Forward and Backpropagation
• Gradient Descent and Optimization

• Introduction to TensorFlow and Keras
• Building a Basic Neural Network with Keras
• Training and Evaluating the Model

• Introduction to Convolutional Layers
• Pooling Layers and Fully Connected Layers
• Building a CNN for Image Classification

• Image Rescaling, Normalization, and Augmentation (45 min)
• Data Augmentation Techniques
• Preparing Datasets for Training

• Concept of Transfer Learning
• Using Pre-trained Models (VGG, ResNet, etc.)
• Fine-Tuning Pre-trained Models

• Introduction to Object Detection
• YOLO (You Only Look Once) and SSD (Single Shot Multibox Detector)
• Applications of Object Detection

• Case Study 1: Image Classification with CNN
• Case Study 2: Object Detection using YOLO
• Final Project: Building a Deep Learning Model for Computer Vision

• What is a Database?
• Introduction to SQL
• Creating and Managing Databases

• Describe table ,
• UNIQUE, NULLL , NOT NULL
• Check and Deault
• Primary Key , Foreign Keys
• Composite Keys ,Candidate Key ,Super Key
• Alter command

• SELECT Queries
• Filtering Data with WHERE Clause
• ORDER BY and LIMIT
• DISTINCT and COUNT Functions
• Basic Arithmetic Operations and Aliases

• Inserting Data
• Updating and Deleting Data
• Transaction Control
• Handling NULL Values

• Introduction to Relationships and Joins
• INNER JOIN
• LEFT, RIGHT, and FULL OUTER JOIN
• CROSS JOIN and SELF JOIN
• UNION and UNION ALL

• GROUP BY Clause
• HAVING Clause
• ROLLUP and CUBE
• CASE Statements

• Introduction to Subqueries
• Correlated Subqueries
• EXISTS and NOT EXISTS
• IN and NOT IN Subqueries
• Common Table Expressions (CTEs)

• Window Functions
• LEAD and LAG Functions
• PIVOT and UNPIVOT
• Recursive Queries
• Materialized Views and Indexing

• Importance of Statistics in Data Science
• Descriptive vs. Inferential Statistics
• Data Types and Measurement Scales

• Measures of Central Tendency: Mean, Median, Mode
• Measures of Spread: Range, Variance, Standard Deviation
• Data Distribution: Normal, Skewed, and Kurtosis

• Sampling Techniques
• Confidence Intervals
• Hypothesis Testing: t-tests and p-values

• Introduction to Probability
• Conditional Probability and Bayes' Theorem
• Probability Distributions: Binomial, Poisson, and Normal

• Correlation Coefficients
• Simple Linear Regression
• Multiple Linear Regression

• Vectors and Scalars
• Matrices and Matrix Operations
• Matrix Transposition and Inverses

• Introduction to Eigenvalues and Eigenvectors
• Applications of Eigenvalues in Data Science