Data Science With Python

  • Duaration - 3 Months

It is a process, not an event. It is the process of using data to understand too many different things, to understand the world. Let Suppose when you have a model or proposed explanation of a problem, and you try to validate that proposed explanation or model with your data.

Requirments
  • Education (Any Field)
  • SQL Database / Coding / Advanced Excel

It is a process, not an event. It is the process of using data to understand too many different things, to understand the world. Let Suppose when you have a model or proposed explanation of a problem, and you try to validate that proposed explanation or model with your data.

Python Overview
  • Features of Python
  • Environment Setup
  • Running Mode of Python

Basic Syntax
  • Python Identifier
  • Reserve Words
  • Lines and Indentation
  • Multi-Line Statements
  • Quotation in Python
  • Comments in Python
  • Using Blank Lines
  • Waiting for the User
  • Multiple Statements on a Single Line
  • Multiple Statement Groups as Suites

Variables
  • Assigning Values to variables
  • Multiple Assignment
  • Standard Data Types
  • Python Numbers
  • Python String
  • Python List
  • Python Tuples
  • Python Dictionary
  • Data Type Conversion

Basic Oprators
  • Types of Operators
  • Python Arithmetic Operators
  • Python Comparison Operators
  • Python Assignment Operators
  • Python Bitwise Operators
  • Python Logical Operators
  • Python Membership Operators
  • Python Identity Operators
  • Python Operators Precedence

Decision Making
  • If Statement
  • If…else Statement
  • Else if Statement
  • Nested Statement
  • Single Statement Suites

Function
  • Defining a Function
  • Calling a Function
  • Function Arguments
  • Pass By Reference Vs Value
  • The Anonymous Functions
  • The Return Statement
  • Scope of Variables

What is Exception
  • Handling An Exception
  • The except Clause with No Exceptions
  • The except Clause with Multiple Exceptions
  • The try-finally Clause
  • Argument Of an Exception
  • Raising an Exception
  • User Defined Exceptions

What Is Module?
  • Import Statement
  • From... Import Statement
  • 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

What is Database?
  • Database Connection
  • Creating Database Table
  • Insert Operation
  • Read Operation
  • Update Operation
  • Delete Operation
  • Performing Transactions
  • Commit Operation
  • Rollback Operation
  • Disconnecting Database
  • Handling Error

What is OOPS Overview?
  • Creating Class
  • Creating Object
  • Accessing Attributes
  • Built-In-Class Attributes
  • Destroying Objects (Garbage Collection)
  • Class Inheritance
  • Overriding Methods
  • Overloading Operators
  • Data Hiding

It is a process, not an event. It is the process of using data to understand too many different things, to understand the world. Let Suppose when you have a model or proposed explanation of a problem, and you try to validate that proposed explanation or model with your data.

Python(Integrated with Django Web Framework)
  • Setup, Intro to Python Programming
  • Comments and Pound Characters, Numbers and Math, Variables And Names
  • More Variables and Printing
  • Strings and Text
  • More Printing Examples, Asking Questions, Prompting People
  • Parameters, Unpacking
  • File Handling
  • Functions
  • Reading some code Boolean Practice
  • What If, Else and If, Making Decisions
  • Loops and Lists
  • Branches and Functions
  • Designing and Debugging
  • Dictionaries
  • Modules, Classes and Objects (OOP)
  • Is-A, Has-A, Objects and Classes
  • Inheritance and Composition
  • Intro to Game Development using Python
  • Creating Automated Tests
  • Advanced user inputs
  • Making sentences
  • Creating your first website
  • Getting input from a browser
  • Starting your first Web Game
  • Intro to Django Web Framework
  • Installing and Configuration
  • Starting a new project
  • Models
  • Django Admin Interface
  • URLs
  • Views (Form-based and Class-based)
  • Templates (HTML)
  • Forms
  • User Authentication
  • Intro to Git and Github Using and reading third-party Django Apps
  • Designing your Django website
  • More exercises


Data Science Overview
  • Data Science
  • Data Scientists
  • Examples of Data Science
  • Python for Data Science

Data Analytics Overview
  • Introduction to Data Visualization
  • Processes in Data Science
  • Data Wrangling, Data Exploration, and Model Selection
  • Exploratory Data Analysis or EDA
  • Data Visualization
  • Plotting
  • Hypothesis Building and Testing

Statistical Analysis and Business Applications
  • Introduction to Statistics
  • Statistical and Non-Statistical Analysis
  • Some Common Terms Used in Statistics

Data Distribution: Central Tendency, Percentiles, Dispersion
  • Histogram
  • Bell Curve
  • Hypothesis Testing
  • Chi-Square Test
  • Correlation Matrix
  • Inferential Statistics

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
  • Basic Data Types: Integer, Float, String, None, and Boolean; Typecasting
  • Creating, accessing, and slicing tuples
  • Creating, accessing, and slicing lists
  • Creating, viewing, accessing, and modifying dicts
  • Creating and using operations on sets
  • Basic Operators: 'in', '+', '*'
  • Functions
  • Control Flow

Mathematical Computing with Python (NumPy)
  • NumPy Overview
  • Properties, Purpose, and Types of ndarray
  • Class and Attributes of ndarray Object
  • Basic Operations: Concept and Examples
  • Accessing Array Elements: Indexing, Slicing, Iteration, Indexing with Boolean Arrays
  • Copy and Views
  • Universal Functions (ufunc)
  • Shape Manipulation
  • Broadcasting
  • Linear Algebra

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 Manipulation with Python (Pandas)
  • Introduction to Pandas
  • Data Structures
  • Series
  • DataFrame
  • Missing Values
  • Data Operations
  • Data Standardization
  • Pandas File Read and Write Support
  • SQL Operation

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
  • 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

Performing Hadoop status checks
  • Importing and exporting relational information with Sqoop

Data Visualization in Python using Matplotlib
  • Introduction to Data Visualization
  • Python Libraries
  • Plots
  • Matplotlib Features:
  • Line Properties Plot with (x, y)
  • Controlling Line Patterns and Colors
  • Set Axis, Labels, and Legend Properties
  • Alpha and Annotation
  • Multiple Plots
  • Subplots
  • Types of Plots and Seaborn

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

R Overview (Basic - R)
  • R - Environment Setup
  • R - Basic Syntax
  • R - Data Types
  • R - Variables
  • R - Operators
  • R - Decision Making
  • R - Loops
  • R - Functions
  • R - Strings R - Vectors R - Lists
  • R - Matrices
  • R - Arrays
  • R - Factors
  • R - Data Frames
  • R - Packages
  • R - Data Reshaping

Introduction to R - Data Interfaces
  • R - CSV Files
  • R - Excel Files
  • R - Binary Files
  • R - XML Files
  • R - JSON Files
  • R - Web Data
  • R - Database

R - Charts & Graphs
  • R - Pie Charts R - Bar Charts R - Boxplots
  • R - Histograms R - Line Graphs R - Scatterplots

R - Statistics Analysis (Data Science)
  • R - Different types of data
  • R - Data summarization
  • R - Frequency table
  • R - Frequency Distributions
  • R - Histogram
  • R - Measures of central tendency and dispersion
  • R - Skewness and kurtosis
  • R - Basic Probability
  • R - Conditional Probability
  • R - Normal Distribution
  • R - Sampling methods
  • R - Point and Interval estimation
  • R - Central Limit Theorem
  • R - Nul and alternative hypothesis
  • R - Level of significance
  • R - P value
  • R - Types of errors
  • R - Hypothesis Testing
  • R - Simple and Multiple Linear Regression
  • R - ANOVA, Interpretation of coefficients
  • R - Dummy Variables
  • R - Residual Analysis
  • R - Outliers
  • R - Logistic Regression

Introduction
  • Introduction to SPSS
  • Data analysis with SPSS: general aspects, workflow, critical issues
  • SPSS: general description, functions, menus, commands
  • SPSS file management

Input and data cleaning
  • Defining variables
  • Manual input of data
  • Automated input of data and file import

Data manipulation
  • Data Transformation
  • Syntax files and scripts
  • Output management

Descriptive analysis of data
  • Frequencies
  • Descriptives
  • Explore
  • Crosstabs
  • Charts

Statistical Tests
  • Means
  • T - test
  • One
  • way ANOVA
  • Non parametric tests
  • Normality tests

Correlation and regression
  • Linear correlation and regression
  • Multiple regression (linear)

Multivariate analysis
  • Factor analysis
  • Cluster analysis

Introduction
  • Introduction to Artificial Intelligence
  • Applications of Artificial Intelligence
  • Keras
  • Tensorflow

Dimensionality Reduction
  • Linear Discriminant Analysis (LDA)
  • Principle component Analysis (PCA)
  • Practical approach in python
  • Business case study

Artificial Neural Network
  • Plan of attack
  • Activation function
  • Gradient descent
  • Stochastic Gradient Descent
  • Backpropagation
  • Connectionism
  • Practical approach with python

Convolution Neural Network
  • Introduction of Convolution Neural Network
  • Plan of attack
  • Convolution Operation
  • ReLU Layers
  • Pooling
  • Flattening
  • Different Layers

Reinforcement Learning
  • Agent environment problem
  • Reinforcement Process
  • Q-learning
  • Practical approach with python

Natural Language Processing
  • Introduction of NLP
  • Application of Natural Language Processing
  • Regular expression
    • Characters
    • Method and function
    • Sets
    • Example
  • Feature Extraction
  • Text Mining
  • NLTK: Tokanizer, CountVectorizer
  • Practical approach with python

Computer vision
  • Introduction of computer vision
  • How a computer reads an Image
  • Application of Computer Vision
  • What is OpenCV
  • Image Detection with OpenCV
  • Practical approach with python

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