Data Science With Python

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