Get PenTest+ Training and Certification Online in Malaysia
Most in-demand in IT is PenTest+ certification for cybersecurity professionals and alike.
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✔ 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.
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
✔ 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.
Sure, here are some possible course objectives for the PenTest+ course:
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.
- 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 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
- Bar Plot
- Count Plot
- Point Plot
- Violin Plot
- 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.
- 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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Will I receive a certificate upon finishing this course?
Yes, you will get a personalized digital certificate downloadable as a PDF. You will need to complete over 85% of the curriculum lessons
Do you provide support for this course?
Yes, our trainers are there to support you during training and post training.
Can I request a refund if I am not happy with the training?
Yes, subject to our refund policies.
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