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Learn Basic Data Science With Python

Learn Basic & Advanced Data Science With Python From Start For Beginner

Courses
Course Overview

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.

Requirements

  • Education
  • R Programming
  • Python Coding
  • Hadoop Platform
  • SQL Database/Coding
  • Apache Spark
  • Machine Learning and AI
  • Data Visualization

Learn Basic Introduction Python Basics & Advanced Training Started
  • Features of Python
  • Environment Setup
  • Running Mode of Python
  • Getting Started
  • 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
  • Assigning Values to variables
  • Multiple Assignment
  • Standard Data Types
  • Python Numbers
  • Python String
  • Python List
  • Python Tuples
  • Python Dictionary
  • Data Type Conversion
  • 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
  • If Statement
  • If…else Statement
  • Else if Statement
  • Nested Statement
  • Single Statement Suites
  • Defining a Function
  • Calling a Function
  • Function Arguments
  • Pass By Reference Vs Value
  • The Anonymous Functions
  • The Return Statement
  • Scope of Variables
  • 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
  • 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
  • Database Connection
  • Creating Database Table
  • Insert Operation
  • Read Operation
  • Update Operation
  • Delete Operation
  • Performing Transactions
  • Commit Operation
  • Rollback Operation
  • Disconnecting Database
  • Handling Error
  • Creating Class
  • Creating Object
  • Accessing Attributes
  • Built-In-Class Attributes
  • Destroying Objects (Garbage Collection)
  • Class Inheritance
  • Overriding Methods
  • Overloading Operators
  • Data Hiding
  • 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
Introduction Python & Data Science Training Started
  • Data Science
  • Data Scientists
  • Examples of Data Science
  • Python for Data Science
  • 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
  • Introduction to Statistics
  • Statistical and Non-Statistical Analysis
  • Some Common Terms Used in Statistics
  • Histogram
  • Bell Curve
  • Hypothesis Testing
  • Chi-Square Test
  • Correlation Matrix
  • Inferential Statistics
  • 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
  • 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
  • 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
  • Introduction to Pandas
  • Data Structures
  • Series
  • DataFrame
  • Missing Values
  • Data Operations
  • Data Standardization
  • Pandas File Read and Write Support
  • SQL Operation
  • 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
  • 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
  • Importing and exporting relational information with Sqoop
  • 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
  • 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
Learn Basic SPSS Training
  • 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
  • R - CSV Files
  • R - Excel Files
  • R - Binary Files
  • R - XML Files
  • R - JSON Files
  • R - Web Data
  • R - Database
  • R - Pie Charts R - Bar Charts R - Boxplots
  • R - Histograms R - Line Graphs R - Scatterplots
  • 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
Learn Basic R-Programming Basics
  • Introduction to SPSS
  • Data analysis with SPSS: general aspects, workflow, critical issues
  • SPSS: general description, functions, menus, commands
  • SPSS file management
  • Defining variables
  • Manual input of data
  • Automated input of data and file import
  • Data Transformation
  • Syntax files and scripts
  • Output management
  • Frequencies
  • Descriptives
  • Explore
  • Crosstabs
  • Charts
  • Means
  • T- test
  • One
  • way ANOVA
  • Non parametric tests
  • Normality tests
  • Linear correlation and regression
  • Multiple regression (linear)
  • Factor analysis
  • Cluster analysis