Datascience Curriculum

Python :

Goal – Get an overview of the python which is required to work on data science

Objectives - At the end of this Module, you should be able understand the following topics

  • Lists
  • Tuples
  • Dictionaries
  • Sets
  • Importing packages
  • If else
  • Loops
  • Comprehensions
  • Functions
  • Map
  • Filter
  • Reduce
  • Numpy
  • Pandas
  • Merging,querying,aggregating
  • Assignments for practice

R :

Goal – Get an overview of the R which is required to work on data science

Objectives - At the end of this Module, you should be able understand the following topics

  • Introduction
  • Basic operations in R
  • Vectors
  • Factors
  • Matrices
  • Data frames
  • Lists
  • Logical and Relational operators
  • Conditional Statements
  • Loops
  • Functions
  • Apply Family

Introduction

  • Applications of Machine Learning
  • Why Machine Learning is the Future
  • Installing R and R Studio (MAC & Windows)
  • Installing Python and Anaconda (MAC & Windows)

 -------------------------- Part  Data Preprocessing --------------------------

  • Welcome to Part  - Data Preprocessing
  • Get the dataset
  • Importing the Libraries
  • Importing the Dataset
  • For Python learners, summary of Object-oriented programming classes & objects
  • Missing Data
  • Categorical Data
  • Splitting the Dataset into the Training set and Test set
  • Feature Scaling
  • And here is our Data Preprocessing Template!
  • Quiz  Data Preprocessing

------------------------------ Part  Regression ------------------------------

  • Welcome to Part  - Regression

 Simple Linear Regression

  • How to get the dataset
  • Dataset + Business Problem Description
  • Simple Linear Regression Intuition -   
  • Simple Linear Regression in Python -  
  • Simple Linear Regression in R -  
  • Quiz  Simple Linear Regression

Multiple Linear Regression

  • How to get the dataset
  • Dataset + Business Problem Description
  • Multiple Linear Regression Intuition -   
  • Multiple Linear Regression in Python -   
  • Multiple Linear Regression in Python - Backward Elimination - Preparation
  • Multiple Linear Regression in Python - Backward Elimination -  !
  • Multiple Linear Regression in Python - Backward Elimination -  Solution
  • Multiple Linear Regression in R -   
  • Multiple Linear Regression in R - Backward Elimination -  !
  • Multiple Linear Regression in R - Backward Elimination -  Solution
  • Quiz  Multiple Linear Regression

Polynomial Regression

  • Polynomial Regression Intuition
  • How to get the dataset
  • Polynomial Regression in Python -  
  • Python Regression Template
  • Polynomial Regression in R -   
  • R Regression Template

Support Vector Regression (SVR)

  • How to get the dataset
  • SVR in Python
  • SVR in R

Decision Tree Regression

  • Decision Tree Regression Intuition
  • How to get the dataset
  • Decision Tree Regression in Python
  • Decision Tree Regression in R

 Random Forest Regression

  • Random Forest Regression Intuition
  • How to get the dataset
  • Random Forest Regression in Python
  • Random Forest Regression in R

 Evaluating Regression Models Performance

  • R-Squared Intuition
  • Adjusted R-Squared Intuition
  • Evaluating Regression Models Performance - 's Final Part
  • Interpreting Linear Regression Coefficients
  • Conclusion of Part  - Regression

 ---------------------------- Part  Classification ----------------------------

  • Welcome to Part  - Classification

 Logistic Regression

  • Logistic Regression Intuition
  • How to get the dataset
  • Logistic Regression in Python -  
  • Python Classification Template
  • Logistic Regression in R -   
  • R Classification Template
  • Quiz  Logistic Regression

 K-Nearest Neighbors (K-NN)

  • K-Nearest Neighbor Intuition
  • How to get the dataset
  • K-NN in Python
  • K-NN in R
  • Quiz  K-Nearest Neighbor

 Support Vector Machine (SVM)

  • SVM Intuition
  • How to get the dataset
  • SVM in Python
  • SVM in R
    • SVMzip

Kernel SVM

  • Kernel SVM Intuition
  • Mapping to a higher dimension
  • The Kernel Trick
  • Types of Kernel Functions
  • How to get the dataset
  • Kernel SVM in Python
  • Kernel SVM in R

Naive Bayes

  • Bayes Theorem
  • Naive Bayes Intuition
  • Naive Bayes Intuition (Challenge Reveal)
  • Naive Bayes Intuition (Extras)
  • How to get the dataset
  • Naive Bayes in Python
  • Naive Bayes in R

 Decision Tree Classification

  • Decision Tree Classification Intuition
  • How to get the dataset
  • Decision Tree Classification in Python
  • Decision Tree Classification in R

 Random Forest Classification

  • Random Forest Classification Intuition
  • How to get the dataset
  • Random Forest Classification in Python
  • Random Forest Classification in R

 Evaluating Classification Models Performance

  • False Positives & False Negatives
  • Confusion Matrix
  • Accuracy Paradox
  • CAP Curve
  • CAP Curve Analysis
  • Conclusion of Part  - Classification

 ---------------------------- Part  Clustering ----------------------------

  • Welcome to Part  - Clustering

 K-Means Clustering

  • K-Means Clustering Intuition
  • K-Means Random Initialization Trap
  • K-Means Selecting The Number Of Clusters
  • How to get the dataset
  • K-Means Clustering in Python
  • K-Means Clustering in R
  • Quiz  K-Means Clustering

Hierarchical Clustering

  • Hierarchical Clustering Intuition
  • Hierarchical Clustering How Dendrograms Work
  • Hierarchical Clustering Using Dendrograms
  • How to get the dataset
  • HC in Python -   
  • HC in R -   
  • Quiz  Hierarchical Clustering
  • Conclusion of Part  - Clustering

 ---------------------- Part  Association Rule Learning ----------------------

  • Welcome to Part  - Association Rule Learning

 Apriori

  • Apriori Intuition
  • How to get the dataset
  • Apriori in R -   
  • Apriori in Python -  

Eclat

  • Eclat Intuition
  • How to get the dataset
  • Eclat in R
    • Eclatzip

 ------------------------ Part  Reinforcement Learning ------------------------

  • Welcome to Part  - Reinforcement Learning

 

Upper Confidence Bound (UCB)

  • The Multi-Armed Bandit Problem
  • Upper Confidence Bound (UCB) Intuition
  • How to get the dataset
  • Upper Confidence Bound in Python -   
  • Upper Confidence Bound in R -  

Thompson Sampling

  • Thompson Sampling Intuition
  • Algorithm Comparison UCB vs Thompson Sampling
  • How to get the dataset
  • Thompson Sampling in Python -  
  • Thompson Sampling in Python -  
  • Thompson Sampling in R -  
  • Thompson Sampling in R -  

 --------------------- Part  Natural Language Processing ---------------------

  • Welcome to Part  - Natural Language Processing
  • How to get the dataset
  • Natural Language Processing in Python -   
  • Challenge
  • Natural Language Processing in R -  
  • Natural Language Processing in R -  
  • Challenge

 ---------------------------- Part  Deep Learning ----------------------------

  • Welcome to Part  - Deep Learning
  • What is Deep Learning?

 Artificial Neural Networks

  • Plan of attack
  • The Neuron
  • The Activation Function
  • How do Neural Networks work?
  • How do Neural Networks learn?
  • Gradient Descent
  • Stochastic Gradient Descent
  • Backpropagation
  • How to get the dataset
  • Business Problem Description
  • ANN in Python -   - Installing Theano, Tensorflow and Keras
  • ANN in R -   
  • ANN in R -   (Last )

 Convolutional Neural Networks

  • Plan of attack
  • What are convolutional neural networks?
  •  - Convolution Operation
  • (b) - ReLU Layer
  •  - Pooling
  •  - Flattening
  •  - Full Connection
  • Summary
  • Softmax & Cross-Entropy
  • How to get the dataset
  • CNN in Python -  
  • CNN in R

 ----------------------- Part  Dimensionality Reduction -----------------------

  • Welcome to Part  - Dimensionality Reduction

 Principal Component Analysis (PCA)

  • How to get the dataset
  • PCA in Python -   
  • PCA in R -  

Linear Discriminant Analysis (LDA)

  • How to get the dataset
  • LDA in Python
  • LDA in R

 Kernel PCA

  • How to get the dataset
  • Kernel PCA in Python
  • Kernel PCA in R

 --------------------- Part  Model Selection & Boosting ---------------------

  • Welcome to Part  - Model Selection & Boosting

Model Selection

  • How to get the dataset
  • k-Fold Cross Validation in Python
  • k-Fold Cross Validation in R
  • Grid Search in Python -   
  • Grid Search in R

XGBoost

  • How to get the dataset
  • XGBoost in Python -   
  • XGBoost in R

Introduction to Big Data, Hadoop

  • Big Data Introduction
  • Hadoop Introduction
  • What is Hadoop? Why Hadoop?
  • Hadoop History?
  • Different types of Components in Hadoop?
  • HDFS, MapReduce, PIG, Hive, SQOOP, HBASE, OOZIE, Flume, Zookeeper and so on
  • What is the scope of Hadoop?

Deep Drive in HDFS (for Storing the Data):

  • Introduction of HDFS
  • HDFS Design
  • HDFS role in Hadoop
  • Features of HDFS
  • Daemons of Hadoop and its functionality
  • Name Node
  • Secondary Name Node
  • Job Tracker
  • Data Node
  • Task Tracker
  • Anatomy of File Wright
  • Anatomy of File Read
  • Network Topology
  • Nodes
  • Racks
  • Data Center
  • Parallel Copying using DistCp
  • Basic Configuration for HDFS Data Organization Blocks and Replication Rack Awareness Heartbeat Signal How to Store the Data into HDFS How to Read the Data from HDFS Accessing HDFS (Introduction of Basic UNIX commands) CLI commands

Java 2 Enterprise Edition:

  • The Enterprise today

  • J2EE Platform

  • J2EE Architecture

  • Developing J2EE Applications

Database Programming with JDBC:

  • Java Database Connectivity

  • Database Drivers

  • JDBC Core API

  • Open database Connectivity

Servlets:

  • Servlets

  • Servlet Implementation

  • More about Servlets

  • Tomcat Documentation

JSP – Java Server Pages:

  • JSP Basics & Architecture

  • The Nuts & Bolts

  • JSP Application Design

  • Tag Libraries

Remote method Invocation:

  • Remote Objects

  • Stubs & Skeletons

  • Serialization Classes

  • Remote Interfaces

Enterprise Java Bean:

  • Components of EJB

  • Session Bean

  • Entity Bean

  • Message driven Bean

  • Java Transaction Services

  • Transaction Manager Functionality

  • Transaction Model

  • Transaction Manager Implementation

Java Transaction API:

  • Introduction

  • Distributed Transaction Process & Transaction Manager

  • Sample Program

Java Naming & Directory Services Interface:

  • Naming Services Overview

  • Directory Services Overview

  • Naming Service Provider

  • Directory Service provider

JavaMail:

  • Implementation of System

  • JavaMail with Weblogic

  • Sending Message with JavaMail

  • Reading Message with JavaMail

Struts:

  • Introduction to Frameworks

  • Frameworks vs Class Libraries

  • Struts Framework

  • Configuration

  • Packages in Struts

 XML:

  • Role of XML

  • XML Syntax & Parsing

  • Elements. Tags & Attributes

  • Roles & functions of DTD

  • XML Schema Structure

  • JAXP – Java XML API

  • XML Parsers for Java

  • Role of SAX & DOM

 

PEGA CSA COURSE CONTENT

APPLICATION DESIGN

The role of the System Architect

  • Introduction to the Role of the System Architect
  • The role of the system architect

Tour of the Designer Studio

  • Introduction to Navigating the Designer Studio
  • How to navigate the Designer Studio
  • How to manage user access to an application

The building blocks of a Pega application

  • Introduction to the Building Blocks of a Pega Application
  • Rules and rule types
  • Rules and rules ets
  • Classes and class hierarchy
  • Rule scope
  • How to create a rule
  • How to update a rule
  • How to reuse rules through inheritance

Assessing Guardrail compliance

  • Introduction to Assessing Guardrail Compliance
  • Compliance Score
  • How to assess Guardrail compliance
  • How to address guardrail violations
  • Justifying rule warnings

CASE DESIGN

Creating cases and child cases

  • Introduction to Creating Cases and Child Cases
  • Case type and case
  • Case type relationships
  • Adding a top-level case type in an application
  • Adding child case type in an application
  • Creating a case during case processing

DATA MODEL DESIGN

Data elements in Pega applications

  • Introduction to Data Elements in Pega Applications
  • Data elements in Pega applications
  • How to manage properties
  • How to reference a property
  • Defining properties

Setting property values automatically

    • Introduction to Setting Property Values Automatically
    • Data transforms

How to set values with data transforms

      • The pyDefault data transform
      • Setting property values using the pyDefault data transform
      • Data transforms and super classing
      • How to configure super classing for data transforms

Setting property values declaratively

      • Introduction to Setting property values declaratively
      • Declarative processing
      • Declare expressions
      • How to set a property value with a declare expression
      • Setting a property value with a declare expression

Passing data to another case

      • Introduction to Passing Data to Another Case
      • Data propagation
      • Propagating data to another case
      • Reviewing application data

Introduction to Reviewing Application Data

      • Data storage in memory
      • pyWorkPage
      • How to view clipboard data
      • Viewing clipboard data
      • Setting property values using the Clipboard tool

PROCESS DESIGN

Activities

      • Introduction to Activities
      • Activities
      • Activity execution
      • Activity parameters
      • API activities
      • Activities best practice

Configuring a work party

      • Introduction to Configuring a Work Party
      • Work parties
      • How to add a work party to a case
      • Configuring a work party for a casetype

Configuring a service level agreement

      • Introduction to Configuring Service Levels
      • Service level agreement rules
      • The Passed Deadline interval
      • How to adjust assignment urgency
      • Configuring a service level agreement rule

Routing assignmente

      • Introduction to Routing Assignments
      • Routing
      • Work lists and workbaskets
      • Routers
      • Configuring routing

Configuring correspondence

      • Introduction to Configuring Correspondence
      • How to configure correspondence rules
      • How to configure correspondence in a business process
      • Configuring correspondence rules

Circumstancing rule

      • Introduction to Circumstancing Rules
      • Situational processing
      • Rule circumstancing
      • Types of circumstancing conditions
      • Examples of circumstancing conditions
      • Circumstancing a rule

DECISION DESIGN

;">Automated decisions in Pega applications

      • Introduction to Automated Decisions in Pega Applications
      • Types of decisions available in Peg applications
      • Configuring when rules
      • Introduction to Configuring When Rules
      • When conditions
      • How to configure a when condition using a when rule Configuring a when rule
      • Configuring decision tables and decision trees
      • Introduction to Configuring Decision Tables and Decision Trees
      • Decision tables
      • How to configure a decision table
      • Configuring a decision table
      • Decision trees
      • How to configure a decision tree
      • Configuring a decision tree
      • How to unit test a decision table or decision tree

UI DESIGN

      • Designing a UI form
      • Introduction to Designing a UI Form
      • User interface structure
      • Sections and layouts
      • How to build a section
      • Creating a dynamic layout in a section
      • Creating a repeating layout in a section
      • How to build sections for reuse
      • Live UI
      • How to use Live UI
      • Using Live UI
      • Guidelines for UI design

Reusing text with paragraph rules

      • Introduction to Reusing Text with Paragraph Rules
      • Paragraph rules
      • Reusing text with paragraph rules

Configuring responsive UI behavior

      • Introduction to Configuring Responsive UI Behavior
      • Responsive user interface
      • Presentation layer and UIskins
      • How to trigger responsive behavior with responsive breakpoints
      • How to style applications with UI skins
      • Configuring responsive breakpoints on a dynamic layout format
      • Designing a UI form
      • Introduction to Designing a Dynamic UI
      • Dynamic User Interface behavior
      • Hiding and showing UI elements
      • Configuring action sets

Validating user data

      • Introduction to Validating User Data
      • Methods of data validation
      • <liControls
      • Validating with controls
      • Dynamic lists of data entry items
      • How to create a dynamic list
      • Creating a dynamic list
      • Validate rules
      • How to use validate rules
      • Validating a flow action with a validate rule
      • How to use edit validate rules

REPORT DESIGN

Creating reports

      • Introduction to Creating Reports
      • Reports
      • Report columns
      • How to create a report
      • Creating a report
      • Report results organization
      • Organizing report results

Optimizing Report Date

      • Introduction to Optimizing Data
      • DataStorage in Pega applications
      • Property optimization
      • Optimizing properties for reporting

DATA MANAGEMENT

Caching data with data pages

      • Introduction to caching data with a datapage
      • Datapages
      • How to configure a data page
      • Configuring a datapage

Managing reference data

      • Introduction to managing reference data
      • Reference data
      • How to use local data storage
      • Defining reference data for an application

Integration in Pega applications

      • Introduction to Integration in Pega Applications
      • Connectors
      • Services
      • Connecting to an external database

Creating a connector

      • Introduction to Creating a Connector

Creating a connector

APPLICATION DEBUGGING

Debugging applications with the Tracer

    • Introduction to Debugging Pega Applications
    • The Tracer
    • How to investigate application errors with the Tracer

Section 1: 01. Introduction

    1. Working Files - Download These First

    2. What This Course Covers

Section 2: 02. Generic Programming

    1. Building Generic Classes - Part 1

    2. Building Generic Classes - Part 2

    3. Creating Generic Interfaces

    4. Building Generic Methods

    5. Building Generic Classes With Different Types

    6. Generic Programming - Exercise

Section 3: 03. Sequential Collections

    1. The Collection, List, And Set Interfaces

    2. The Queue And DE queue Interfaces

    3. The Array list Class

    4. The Hash set Class

    5. The Tree set Class

    6. The Priority queue Class

    7. Sequential Collections - Exercise 1

    8. Sequential Collections - Exercise 2

Section 4: 04. Associative Collections

    1. The Map Interface

    2. The Tree map Class

    3. The Hash map Class

    4. Associative Collections - Exercise

Section 5: 05. Classic Data Structures

    1. Stacks

    2. Queues

    3. Binary Trees

    4. Classic Data Structures - Exercise 1

    5. Classic Data Structures - Exercise 2

    6. Classic Data Structures - Exercise 3

    7. Classic Data Structures - Exercise 4

Section 6: 06. Sorting And Searching Algorithms

    1. Insertion Sort

    2. Bubble Sort

    3. Merge sort

    4. Quicksort

    5. Linear Search

    6. Binary Search

    7. Sorting And Searching Algorithms - Exercise 1

    8. Sorting And Searching Algorithms - Exercise 2

Section 7: 07. Exception Handling

    1. Exceptions Introduction - Uncaught Exceptions

    2. Try-Catch Statement - Part 1

    3. Try-Catch Statement - Part 2

    4. Multiple Catch Clauses

    5. Try-Catch-Finally

    6. Exception Handling - Exercise

Section 8: 08. Database Programming with JDBC

    1. Installing MySQL On Windows

    2. Installing MySQL On Mac

    3. Installing Connector/J

    4. Connecting To A Database

    5. Querying Data

    6. Creating Database/Tables

    7. Inserting Data

    8. Updating Data

    9. Database Programming With JDBC - Exercise

Section 9: 09. Network Programming

  1. Working With URLs

  2. Socket Programming Example

  3. Socket Server Programming

  4. Client Server Programming

  5. Network Programming - Exercise

Section 10: 10. GUI Development with Swing

    1. A Simple Example

    2. Working With Text Fields

    3. Working With Buttons

    4. Working With Lists

    5. Working With Scroll Panes

    6. GUI Development With Swing - Exercise

Section 11: 11. Multithread Programming

    1. The Main Thread

    2. Creating Threads

    3. Synchronizing Threads

    4. Multithread Programming - Exercise

Section 12: 12. Java Applets

    1. Simple Applet Example

    2. Creating An Applet - Part 1

    3. Creating An Applet - Part 2

    4. Java Applets - Exercise

Section 13: 13. Java Web Applications

    1. Installing Tomcat On Windows

    2. Installing Tomcat On Mac

    3. Simple Servlet

    4. Java Web Applications - Exercise 1 - Get Request

    5. Java Web Applications - Exercise 2 - Post Request

Section 14: 14. JavaBean Programming

    1. Creating A JavaBean Class

    2. Creating A Bean info Class - Part 1

    3. Creating A Bean info Class - Part 2

    4. JavaBean Programming - Exercise

Section 15: 15. Advanced Java Input/output (NIO)

    1. File Copying With NIO

    2. Working With Buffers

    3. Working With File Data

    4. Advanced Java Input/output - Exercise

Section 16: 16. Strings and string builder Class

    1. Problems With Strings

    2. Working With string builder Class

    3. Strings And String builder Class - Exercise

Section 17: 17. Regular Expressions

    1. Introduction To Regular Expressions

    2. Creating Pattern And Match Objects

    3. Using Met characters

    4. Using Regular Expressions To Replace Text

    5. Regular Expressions - Exercise

Section 18: 18. Java Graphics

    1. Drawing Lines

    2. Drawing Shapes

    3. Working With Color

    4. Java Graphics - Exercise

Section 19: 19. Using Eclipse

    1. Installing Eclipse On Windows

    2. Installing Eclipse On Mac

    3. Hello World

    4. Overview Of The Eclipse IDE

    5. Entering Programs

    6. Code Generation

    7. Debugging

 

CORE JAVA COURSE CONTENT

Lesson 1: Introduction to Java

  • Introduction to Java
  • Features of Java
  • Evolution in Java
  • Developing software in Java

Lesson 2: JVM Architechture

Lesson 3: Language Fundamentals

  • Data Types
  • Keywords
  • Operators and Assignments
  • Flow Control: Java’s Control Statements
  • Method with Variable Argument Lists
  • Objects and Classes
  • Arrays
  • Declaring Type Safe Enums
  • OOPS Features in Java – Inheritance and Polymorphism
  • Other Modifiers – abstract, static and final
  • The Object Class
  • The System Class
  • String Handling
  • Wrapper Classes
  • Common Best Practices

Lesson 4: Packages and Interfaces

  • What is an Interface?
  • Packages
  • Access Specifiers and Modifiers

Lesson 5: Reflection API

Lesson 6: Multithread Programming

  • Thread Life cycle
  • Types Of Threads Implentation
  • Overview about Synchronation
  • Examples

Lesson 7: Property Files

  • What are Property Files?
  • Types of Property files
  • User defined Properties

Lesson 8 : Regular Expressions

  • Regular Expressions

Additional concepts:

  • Eclipse3.3 as an IDE
  • Jboss and Tomcate

Lesson 10: Collections

  • Collections Framework
  • Collection Interface Methods
  • Generics
  • Enhanced For Loop
  • AutoBoxing with Collections
  • Implementing Classes
  • The Legacy Classes and Interfaces
  • Common Best Practices on Collections

Lesson 11: Exception Handling

  • Exception Handling – Fundamentals
  • Exception Types
  • Handling Exceptions
  • Creating Application Specific Exceptions
  • Best Practices on Exception Handling

Lesson 12: Java Database Connectivity (JDBC 3.0)

  • Java Database Connectivity - Introduction

Lesson 13: Annotations

  • What are annotations?
  • Advantages of annotations
  • Types of Annotations
  • Creating Annotations
  • Using Annotations
  • Testing the Annotations

 

MicroStrategy Objects

  • Configuration Objects
  • Public Objects
  • Schema Objects
  • Report Objects

MicroStrategy Advanced Reporting

  • Creating Derived Metrics
  • Metric comparison
  • Creating filters
  • Advanced filters
  • Shortcut & Embedded Filters
  • Refreshing Reports
  • Intelligent Cubes

MicroStrategy Office

  • Conditional Formatting
  • Custom Groups
  • Report Cache Flow
  • Data Marts
  • Predictive Models
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