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Syllabus

CompTIA PenTest+

PenTest+ Training and Certification in Malaysia

Duration: 4 days/32 hours

Prerequisites

While there are no official prerequisites for CompTIA PenTest+, it is advisable to have a minimum of three to four years of hands-on experience in information security or a related field.

PenTest+ training may be used for a variety of job roles, including but not limited to:

Network Security Operations
Penetration Tester
Web App Penetration Tester
Cloud Security Specialist
Network Security Specialist
Vulnerability Analyst
Security Consultant
Cloud Penetration Tester
Threat Intelligence Analyst

Limited time Course Price

RM 4000

RM 2500

(Training Only)

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Overview

✔ CompTIA PenTest+ is designed for cybersecurity professionals responsible for conducting penetration testing and managing vulnerabilities.

✔ Unlike some other penetration testing exams, which may only cover certain stages using essay questions and hands-on tasks, CompTIA PenTest+ utilizes a combination of performance-based and knowledge-based questions to comprehensively address all stages of penetration testing.

✔ CompTIA PenTest+ is the singular exam available that encompasses hands-on vulnerability assessment, scanning, analysis, as well as the planning, scoping, and management of weaknesses.

✔ CompTIA PenTest+ mandates that candidates showcase the most pertinent penetration testing skills for a wide spectrum of environments, including cloud, hybrid setups, web applications, Internet of Things (IoT), and traditional on-premises systems.

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Highlights

20 hours of Lab practice guided by an instructor

E-book with tools and software

600 latest exam practice questions

4.8 star rating of google reviews

Exam Practice questions that students will access and practice through a server

Live online or Classroom training

95% exam passing rate with one attempt

Assignments

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The examination format of Network+

Examination Name - PT0-002
CCNA Exam Price - USD 392
CCNA Exam Questions Format - Multiple choice and performance-based
Number of Questions - 85 Questions
Score required to pass exam - 750 out of 1000
Duration of the exam -2.75 Hours (165 Minutes)
Exam mode of language - English, Japanese, Portuguese, and Thai
Exam Venue - Testing Centers, Online Testing

Course Benefits

✔ CompTIA PenTest+ validates that certified professionals possess the most current penetration testing and vulnerability assessment skills.

✔CompTIA PenTest+ is designed for cybersecurity professionals responsible for performing penetration testing and managing vulnerability assessments.

✔ Join now and start preparing for the PT0-002 exam, which gives you a CompTIA PenTest+ Certification

✔ Show your skills and expertise to climb your career ladder as a PenTest+ certified professional

✔ Improve your job prospects as an IT professional by earning a well-recognized certification.

Objectives

Sure, here are some possible course objectives for the PenTest+ course:

CompTIA PenTest+ certifies candidates for planning and scoping penetration testing assessments, including vulnerability scanning.

It covers legal and compliance requirements, result analysis, and report creation.

The certification is relevant to a wide range of environments, including cloud, hybrid setups, web applications, IoT, and on-premises systems.

Candidates learn to identify weaknesses and propose remediation techniques.

Course Outline

Introduction To Data Science and Data Science Libraries

Data science is the field of applying advanced analytics techniques and scientific principles to extract valuable information from data for business decision-making, strategic planning and other uses. Through this module, you will learn the basics, how to analyze data, and then create some beautiful visualizations using Python.

NumPy

It’s a general-purpose array-processing package that provides high-performance multidimensional objects called arrays and tools for working with them. NumPy also addresses the slowness problem partly by providing these multidimensional arrays as well as providing functions and operators that operate efficiently on these arrays.
NumPy Getting Started
NumPy Creating Arrays
NumPy Array Indexing
NumPy Array Slicing
NumPy Data Types
NumPy Copy vs View
NumPy Array Shape
NumPy Array Reshape
NumPy Array Iterating
NumPy Array Join
NumPy Array Split
NumPy Array Search
NumPy Array Sort
NumPy Array Filter
NumPy Random
NumPy Inbuilt Methods

Pandas

Pandas is an important library in Python for Data Science. It is used for data manipulation and analysis. It is well suited for different data such as tabular, ordered and unordered time series, matrix data, etc.

Pandas Getting Started
Pandas Series
Pandas Data Frames
Pandas Read CSV
Pandas Read JSON
Pandas Read Excel
Pandas Analyzing Data

Data Cleaning and Data Wrangling Using Python Pandas

Data scientists spend a large amount of their time cleaning datasets and getting them down to a form with which they can work. In fact, a lot of data scientists argue that the initial steps of obtaining and cleaning data constitute 80% of the job. Therefore, if you are just stepping into this field or planning to step into this field, it is important to be able to deal with messy data, whether that means missing values, inconsistent formatting, malformed records, or nonsensical outliers. In this module, we’ll leverage Python’s Pandas to clean data.

Cleaning a DataFrame
Removing Columns
Removing Rows
Filling Missing Values
Improving Readability
Dropping Columns in a DataFrame
Changing the Index of a DataFrame

Matplotlib Visualization with Python

Matplotlib is a python library used to create 2D graphs and plots by using python scripts. It has a module named pyplot which makes things easy for plotting by providing feature to control line styles, font properties, formatting axes etc. It supports a very wide variety of graphs and plots namely - histogram, bar charts, power spectra, error charts etc.

Python Data Visualization
Python Chart Properties
Python Chart Styling
Python Box Plots
Python Heat Maps
Python Scatter Plots
Python Line Charts
Python Pie Charts
Python Bar Charts
Python Time Series
Python Geographical Data

Python seaborn Library

Seaborn is one of an amazing library for visualization of the graphical statistical plotting in Python. Seaborn provides many color palettes and defaults beautiful styles to make the creation of many statistical plots in Python more attractive. Seaborn library aims to make a more attractive visualization of the central part of understanding and exploring data. It is built on the core of the matplotlib library and also provides dataset-oriented APIs.

Plotting Chart Using seaborn Library
Line plot
Dist plot
Lmplot
Histogram
Bar Plot
Count Plot
Point Plot
Violin Plot
Heatmap

Statistics

What is statistics?
Basic terminology of statistics
Types of statistics
Descriptive statistics
Measure of Central Tendency ( Mean, median, mode )
Measures of Dispersion ( Variance, Standard Deviation, Range-its derivation )
Inferential statistics

Exploratory Data Analysis

In this module, you will learn what is meant by exploratory data analysis, and you will learn how to perform computations on the data to calculate basic descriptive statistical information, such as mean, median, mode, and quartile values, and use that information to better understand the distribution of the data. You will learn about putting your data into groups to help you visualize the data better. Exploratory data analysis (EDA) is an especially important activity in the routine of a data analyst or scientist. It enables an in depth understanding of the dataset, define or discard hypotheses and create predictive models on a solid basis. It uses data manipulation techniques and several statistical tools to describe and understand the relationship between variables and how these can impact business.

Capstone Project 1 : Credit Card Fraud Detection Case Study

Overview : Lots of financial losses are caused every year due to credit card fraud transactions, the financial industry has switched from a posterior investigation approach to an a priori predictive approach with the design of fraud detection algorithms to warn and help fraud investigators.

This case study is focused to give you an idea of applying Exploratory Data Analysis (EDA) in a real business scenario. In this case study, apart from applying the various Exploratory Data Analysis (EDA) techniques, you will also develop a basic understanding of risk analytics and understand how data can be utilized in order to minimize the risk of losing money while lending to customers.

Introduction To Machine Learning

Introduction To Machine Learning
Types Of Machine Learning

Supervised Learning - Classification

Logistic Regression

Capstone Project 4 - Healthcare Industry Predicting Diabetes

This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective of the dataset is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset. In this you will be learning the concepts of Logistic Regression.

Unsupervised Learning

Types of Unsupervised Learning
Applications of Unsupervised Learning
Introduction to Clustering Algorithms
Types of Clustering Algorithms
What is K-Means Clustering?
Implementation Of Apriori Algorithms

Capstone Project 5 - Apriori Algorithm is a Machine Learning algorithm utilized to understand the patterns of relationships among the various products involved. The most prominent practical application of the algorithm is to recommend products based on the products already present in the user’s cart. Walmart especially has made great use of the algorithm in suggesting products to it’s users.

Supervised Learning - Regression

Apply different Machine Learning algorithms

Capstone Project 6 - Bank Churn Prediction using popular classification algorithms

Knowing the customer churn rate is a key indicator for any business. According to a study by Bain & Company, improving the customer retention rate for existing customers by just 5 percent can improve a company’s profitability by 25 to 95 percent.

In this module, we are going to look at the following:
Initial Exploratory Data Analysis
Predicting the churn rate for a customer and classify them by learning about different classification algorithms.
Comparing and evaluating different algorithms based on its performance.
And once we have our best model, we would perform optimization

What You Will Learn

Incorporates updated techniques that highlight the importance of governance, risk, and compliance principles, as well as addressing scoping, organizational/customer requirements, and fostering an ethical hacking mindset.

Integrates updated skills related to conducting vulnerability scanning, both passive and active reconnaissance, vulnerability management, and in-depth analysis of reconnaissance results.

Comprises updated approaches to addressing expanded attack surfaces, researching social engineering techniques, executing network attacks, wireless attacks, application-based attacks, cloud technology attacks, and post-exploitation techniques.

Enhanced to emphasize the significance of reporting and communication within an increasingly regulated environment during the penetration testing process. This includes the analysis of findings and the recommendation of suitable remediation measures within a comprehensive report.

Introduces updated concepts related to identifying scripts in diverse software deployments, analyzing scripts or code samples, and providing explanations of various tool use cases throughout the penetration testing phases. It's worth noting that scripting or coding skills are not mandatory for this.

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