Cybersecurity Job Oriented Certificate Program

(6 Months)

Advance your career in cybersecurity with the courses offered by AppliedTech Academy. Our comprehensive six-month cybersecurity program equips you with the most sought-after skills in today’s fast-paced technology environment. Developed in collaboration with leading industry experts, this online course is designed to provide hands-on experience with various cybersecurity tools and techniques, ensuring you stay ahead in the field.

Enroll in AppliedTech Cybersecurity Training Course for Comprehensive Skill Development

In association with

a Singapore based Technology Education Enterprise.

AppliedTech Academy’s 6-month comprehensive certificate program is designed to equip learners with essential skills in applied technology to thrive in today’s rapidly evolving digital landscape. Developed in collaboration with leading industry experts, this program blends theoretical knowledge with hands-on practical training, covering a wide range of topics including software development, data analytics, cloud computing, and artificial intelligence.

The program features real-world projects, allowing participants to gain practical experience by working on industry-relevant challenges. Learners will also engage in a 2-month certified internship, providing critical exposure to real-world scenarios and enhancing job readiness. One-on-one mentorship ensures personalized support and guidance throughout the learning process.

Graduates of this program will be well-equipped with the technical skills needed to succeed in the applied technology field. Whether you are a beginner looking to enter the tech industry or a professional seeking to enhance your skillset, this course will help you meet the demands of the modern tech ecosystem.

An overview of what you will learn from this program.

1. Cybersecurity Foundational Skills

Introduction to Cybersecurity, Network Defense Tools, Web Application Tools, Introduction to Cybercrime

2. Cybersecurity Intermediate Skills

Computer Networks, Network Standards, Network Security, Python for Cybersecurity

3. Cybersecurity Skills Development

Computational Number Theory and Cryptography, Information Security Assurance, Security Analysis and Reporting, Cyberspace and its Governance

4. Gray Hat Hacking

Introduction to Ethical Hacking, Vulnerability Analysis, Malware Threats, DoS and various other concepts!

5. Cybersecurity Skills Application

Applying the skills learnt to a wide variety of areas such as wireless networks, web servers, web applications and others.

Internship Project

2 Months Project Internship in which you will build multiple real-world projects and be mentored by industry professionals.

Program Highlights

Live Projects

Apply cybersecurity concepts to wireless networks, web servers, and applications through hands-on labs, projects, and practical exercises, gaining essential skills in a real-world context.

Personalized Mentorship

Receive personalized guidance from experienced faculty and mentors, benefiting from a low student-to-instructor ratio that ensures you receive tailored support and assistance.

Experienced Faculty Members

Learn from top industry experts. A low student-to-instructor ratio guarantees close interaction with faculty, enabling a personalized learning experience and effective support.

Enhancing Employability

At Our Academy, mentors develop skilled talent for Industry 4.0 by providing comprehensive support, ensuring you gain the expertise employers need.

Internship Certification

Join our 2-month certified internship and dive into action, immersing yourself fully as we journey through an exciting new technological era!

Career Counseling Services

Enhance your soft skills with our Cybersecurity Job Oriented Certificate Program, which also includes career counseling—perfect for advancing your career goals!

About the Cybersecurity Certification Course

Cybersecurity Certification Courses: Your Path to Professional Excellence

Cybersecurity has become a crucial concern for individuals and organizations due to the increasing frequency and sophistication of cyber-attacks. As a result, the demand for skilled cybersecurity professionals is on the rise, making it essential to acquire the skills needed to protect against these threats. Obtaining a cybersecurity certification is an excellent way to develop these skills and advance in this field.

A cybersecurity certification demonstrates an individual’s knowledge and expertise in cybersecurity, making it a valuable asset for career advancement and increasing earning potential. Additionally, the Appliedtech Cybersecurity certification course offers opportunities to network with other professionals, share ideas, and access new job opportunities.

Industry Relevant

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Why choose AppliedTech :

At AppliedTech, we envision a future where individuals are empowered to navigate and excel in the rapidly evolving realm of technology. Our dedicated team is committed to revolutionizing the learning experience, instilling innovative thinking and adaptability to keep pace with the ever-changing technological landscape.

Our mission at AppliedTech is to cultivate a culture of continuous learning and development, nurturing individuals from diverse backgrounds, whether rooted in the world of IT or branching out into non-IT domains. We firmly believe that knowledge has no boundaries, and we are dedicated to breaking down barriers to make technology education accessible to all.

Get Ahead with Cybersecurity Certificate!

Certificate of Completion for the 6 Months Program
Internship Certificate from Participating Companies
Letter of Recommendation

Program Eligibility Criteria and Prerequisites :

Course Syllabus In Detail :

Basics of Python Programming

Session 1: Python Basics
- About Python
- Python Data Types
- Python Variables
- Python comments
- Python Keywords and Identifiers
- Python User Input
- Python Type conversion
- Python Literals
Session 2: Python Operators + if-else + Loops
- Python Operators
- Python if-else
- Python While Loop
- Python for loop
- Break, continue, pass statement in loops
Session 3: Python Strings
- String indexing
- String slicing
- Common String functions
Assignments and Interview Questions

Python Data Types

Session 4: Python Lists
- Array vs List
- How lists are stored in a memory
- All Operations on List
- List Functions
Session 5: Tuples + Set + Dictionary

- Tuple
- Operations on tuple
- Set functions
Session 6: Dictionary
- Operations on dictionary
- Dictionary functions
Assignments and Interview Questions

Python Functions

Create functions.
Arguments and parameters
args and kwargs
map(), filter(), reduce()
Assignments and Interview Questions

Object Oriented Programming (OOP)

Session 7: OOP Part1
- What is OOP?
- What are classes and Objects?
- Methods vs Functions
- Magic/Dunder methods
- What is the true benefit of constructor?
- Concept of ‘self’
- __str__, __add__, __sub__ , __mul__ , __truediv__
Session 8: OOP Part2
- Encapsulation
- Collection of objects
Session 9: OOP Part3
- Class Relationship
- Inheritance and Inheritance class diagram
- Constructor example
- Types of Inheritance (Single, Multilevel, Hierarchical,Multiple )
- Code example and diamond problem
- Polymorphism
- Method Overriding and Method Overloading
Session on Abstraction
- What is Abstraction?
- Abstract class
3 Interview Questions

Advanced Python

Session 10: File Handling + Serialization & Deserialization
- How File I/O is done
- Writing to a new text file
- append()
- Reading a file -> read() and readline()
- Seek and tell
- Working with Binary file
- Serialization and Deserialization
- JSON module -> dump() and load()
- Pickling
Session 11: Exception Handling
- Syntax/Runtime Error with Examples
- Why we need to handle Exception?
- Exception Handling (Try-Except-Else-Finally)
- Handling Specific Error
- Raise Exception
- Create custom Exception
- Exception Logging
Session 12: Decorators
- Decorators with Examples
Session on Generator
- What is a generator?
- Why to use Generator?
- Yield vs Return
4 Interview Questions

Numpy

Session 13: Numpy Fundamentals
- Numpy Theory
- Numpy array
- Matrix in numpy
- Array operations
- Scalar and Vector operations
Session 14: Advanced Numpy
- Numpy array vs Python List
- Broadcasting
- Mathematical operations in numpy
- Sigmoid in numpy
- Mean Squared Error in numpy
- Various functions like sort, append, concatenate, percentile, flip, Set functions, etc.

Pandas

Session 16: Pandas Series
- What is Pandas?
- Introduction to Pandas Series
- Series Methods
Session 17: Pandas DataFrame
- Introduction Pandas DataFrame
- Creating DataFrame and read_csv()
- Selecting cols and rows from dataframe
- Filtering a Dataframe
- Adding new columns
Session 18: Important DataFrame Methods
- Sort, index, reset_index, isnull, dropna, fillna, drop_duplicates, value_counts, apply etc.

Advanced Pandas

Session 19: GroupBy Object
- What is GroupBy?
- Applying builtin aggregation fuctions on groupby objects
Session 20: Merging, Joining, Concatenating
- Pandas concat method
- Merge and join methods
- Practical implementations
Session 21: MultiIndex Series and DataFrames
Session on Pandas Case Study

Data Visualization

Session 23: Plotting Using Matplotlib
- Get started with Matplotlib
- Plotting simple functions, labels, legends, multiple plots
- About scatter plots
- Bar chart
- Histogram
- Pie chart
- Changing styles of plots

Data Visualization Continued

Session 25: Plotting Using Seaborn
- Why seaborn?
- Categorical Plots
- Stripplot
- Swarmplot
- Categorical Distribution Plots
- Boxplot
- Violinplot
- Barplot

Data Preprocessing and Analysis Process

Session on Data Cleaning and Data Preprocessing Case Study
- Quality issues
- Tidiness issues
- Data Cleaning
Session 29: Exploratory Data Analysis (EDA)
- Introduction to EDA
- Why EDA?
- Steps for EDA
- Univariate, Bivariate Analysis
- Feature Engineering
Data Preprocessing steps.

SQL Basics

Session 30: Database Fundamentals
- Introduction to Data and Database
- CRUD operations
- Types of Database
- MySQL workbench
- DDL ,DML ,DQL ,DCL Commands
- Selecting & Retrieving Data with SQL
- Filtering, Sorting, and Calculating Data with SQL
- Sub Queries and Joins in SQL

Descriptive Statistics

Session 38: Descriptive Statistics Part 1
- What is Statistics?
- Types of Statistics
- Population vs Sample
- Types of Data
- Measures of central tendency
- Measure of Dispersion
- Quantiles and Percentiles
- Five Number Summary
- Boxplots
- Scatterplots
- Covariance
- Correlation
Probability Distribution Functions (PDF, CDF, PMF)
- Random Variables
- Probability Distributions
- Probability Distribution Functions and its types
- Probability Mass Function (PMF)
- Cumulative Distribution Function (CDF) of PMF
- Probability Density Function (PDF)
- Density Estimation
- Parametric and Non-parametric Density Estimation
- Kernel Density Estimate (KDE)
- Cumulative Distribution Function (CDF) of PDF.

Probability Distributions

Session 41: Normal Distribution
- How to use PDF in Data Science?
- 2D density plots
- Normal Distribution (importance, equation, parameter, intuition)
- Standard Normal Variate (importance, z-table, empirical rule)
- Skewness
- Use of Normal Distribution in Data Science
Session 42: Non-Gaussian Probability Distributions
- Kurtosis and Types
- Transformation
  - Mathematical Transformation
  - Log Transform

- - Reciprocal Transform / Square or sqrt Transform
  - Power Transformer
  - Box-Cox Transform

Inferential Statistics

Session 43: Central Limit Theorem
- Bernouli Distribution
- Binomial Distribution
- Intuition of Central Limit Theorem (CLT)
- CLT in code
Session 44: Confidence Intervals
- Confidence Interval
  - Ways to calculate CI
  - Applications of CI
  - Confidence Intervals in code

Hypothesis Testing

Session 45: Hypothesis Testing (Part 1)
- Key idea of hypothesis testing
- Null and alternate hypothesis
- Steps in Hypothesis testing
- Performing z-test
- Rejection region and Significance level
- Type-1 error and Type-2 Error
- One tailed vs. two tailed test
- Applications of Hypothesis Testing
- Hypothesis Testing in Machine Learning
Session 46: Hypothesis Testing (Part 2) | p-value and t-tests
- What is p-value?
- Interpreting p-value
- T-test
- Types of t-test
  - Single sample t-Test
  - Independent 2-sample t-Test

- - Paired 2 sample t-Test
  - Code examples of all of the above

Session on Chi-square test
- Chi-square test
- Goodness of fit test (Steps, Assumptions, Examples)
- Test for Independence (Steps, Assumptions, Examples)
- Applications in machine learning
Session on ANOVA
- F-distribution
- One/Two-way ANOVA

Linear Algebra

Session on Tensors | Linear Algebra part 1(a)
- What are tensors?
- 0D, 1D and 2D Tensors
- Nd tensors
- Example of 1D, 2D, 3D, 4D, 5D tensors
Session on Vectors | Linear Algebra part 1(b)
- What is Linear Algebra?
- What are Vectors?
- Vector example in ML
- Row and Column vector
- Distance from Origin
- Euclidean Distance
- Scalar Addition/Subtraction (Shifting)
- Vector Addition/Subtraction
- Dot product
- Angle between 2 vectors
Linear Algebra Part 2 | Matrices (computation)
- What are matrices?
- Types of Matrices
- Matrix Equality
- Scalar Operation
- Matrix Addition, Subtraction, multiplication
- Transpose of a Matrix
- Determinant
- Inverse of Matrix
Linear Algebra Part 3 | Matrices (Intuition)
- Basis vector
- Linear Transformations
- Linear Transformation in 3D
- Matrix Multiplication as Composition
- Determinant and Inverse
- Transformation for non-square matrix?

Linear Regression

Session 48: Introduction to Machine Learning
- About Machine Learning (History and Definition)
- Types of ML
  - Supervised Machine Learning
  - Unsupervised Machine Learning
  - Semi supervised Machine Learning
  - Reinforcement Learning
- Batch/Offline Machine Learning
- Instance based learning
- model-based learning
- Instance vs model-based learning
- Challenges in ML
  - Data collection
  - Insufficient/Labelled data
  - Non-representative date
  - Poor quality data
  - Irrelevant features
  - Overfitting and Underfitting
  - Offline learning
  - Cost
- Machine Learning Development Life-cycle
- Different Job roles in Data Science
- Framing a ML problem | How to plan a Data Science project
Session 49: Simple Linear regression
- Introduction and Types of Linear Regression
- Intuition of simple linear regression
- How to find m and b?
- Regression Metrics
- MAE, MSE, RMSE, R2 score, Adjusted R2 score
Session 50: Multiple Linear Regression
- Introduction to Multiple Linear Regression (MLR)
- Mathematical Formulation of MLR
- Error function of MLR
- Session on Polynomial Regression
  - Why we need Polynomial Regression?
  - Formulation of Polynomial Regression
- Session on Assumptions of Linear Regression
- Session 53: Multicollinearity
  - What is multicollinearity?
  - How to detect and remove Multicollinearity
  - Correlation
  - VIF (Variance Inflation Factor)

Basics of Python Programming

Session 1: Python Basics
- About Python
- Python Data Types
- Python Variables
- Python comments
- Python Keywords and Identifiers
- Python User Input
- Python Type conversion
- Python Literals
Session 2: Python Operators + if-else + Loops
- Python Operators
- Python if-else
- Python While Loop
- Python for loop
- Break, continue, pass statement in loops
Session 3: Python Strings
- String indexing
- String slicing
- Common String functions
Assignments and Interview Questions

Python Data Types

Session 4: Python Lists
- Array vs List
- How lists are stored in a memory
- All Operations on List
- List Functions
Session 5: Tuples + Set + Dictionary

- Tuple
- Operations on tuple
- Set functions
Session 6: Dictionary
- Operations on dictionary
- Dictionary functions
Assignments and Interview Questions

Python Functions

Create functions.
Arguments and parameters
args and kwargs
map(), filter(), reduce()
Assignments and Interview Questions

Object Oriented Programming (OOP)

Session 7: OOP Part1
- What is OOP?
- What are classes and Objects?
- Methods vs Functions
- Magic/Dunder methods
- What is the true benefit of constructor?
- Concept of ‘self’
- __str__, __add__, __sub__ , __mul__ , __truediv__
Session 8: OOP Part2
- Encapsulation
- Collection of objects
Session 9: OOP Part3
- Class Relationship
- Inheritance and Inheritance class diagram
- Constructor example
- Types of Inheritance (Single, Multilevel, Hierarchical,Multiple )
- Code example and diamond problem
- Polymorphism
- Method Overriding and Method Overloading
Session on Abstraction
- What is Abstraction?
- Abstract class
3 Interview Questions

Advanced Python

Session 10: File Handling + Serialization & Deserialization
- How File I/O is done
- Writing to a new text file
- append()
- Reading a file -> read() and readline()
- Seek and tell
- Working with Binary file
- Serialization and Deserialization
- JSON module -> dump() and load()
- Pickling
Session 11: Exception Handling
- Syntax/Runtime Error with Examples
- Why we need to handle Exception?
- Exception Handling (Try-Except-Else-Finally)
- Handling Specific Error
- Raise Exception
- Create custom Exception
- Exception Logging
Session 12: Decorators
- Decorators with Examples
Session on Generator
- What is a generator?
- Why to use Generator?
- Yield vs Return
4 Interview Questions

Numpy

Session 13: Numpy Fundamentals
- Numpy Theory
- Numpy array
- Matrix in numpy
- Array operations
- Scalar and Vector operations
Session 14: Advanced Numpy
- Numpy array vs Python List
- Broadcasting
- Mathematical operations in numpy
- Sigmoid in numpy
- Mean Squared Error in numpy
- Various functions like sort, append, concatenate, percentile, flip, Set functions, etc.

Pandas

Session 16: Pandas Series
- What is Pandas?
- Introduction to Pandas Series
- Series Methods
Session 17: Pandas DataFrame
- Introduction Pandas DataFrame
- Creating DataFrame and read_csv()
- Selecting cols and rows from dataframe
- Filtering a Dataframe
- Adding new columns
Session 18: Important DataFrame Methods
- Sort, index, reset_index, isnull, dropna, fillna, drop_duplicates, value_counts, apply etc.

Advanced Pandas

Session 19: GroupBy Object
- What is GroupBy?
- Applying builtin aggregation fuctions on groupby objects
Session 20: Merging, Joining, Concatenating
- Pandas concat method
- Merge and join methods
- Practical implementations
Session 21: MultiIndex Series and DataFrames
Session on Pandas Case Study

Data Visualization

Session 23: Plotting Using Matplotlib
- Get started with Matplotlib
- Plotting simple functions, labels, legends, multiple plots
- About scatter plots
- Bar chart
- Histogram
- Pie chart
- Changing styles of plots

Data Visualization Continued

Session 25: Plotting Using Seaborn
- Why seaborn?
- Categorical Plots
- Stripplot
- Swarmplot
- Categorical Distribution Plots
- Boxplot
- Violinplot
- Barplot

Data Preprocessing and Analysis Process

Session on Data Cleaning and Data Preprocessing Case Study
- Quality issues
- Tidiness issues
- Data Cleaning
Session 29: Exploratory Data Analysis (EDA)
- Introduction to EDA
- Why EDA?
- Steps for EDA
- Univariate, Bivariate Analysis
- Feature Engineering
Data Preprocessing steps.

SQL Basics

Session 30: Database Fundamentals
- Introduction to Data and Database
- CRUD operations
- Types of Database
- MySQL workbench
- DDL ,DML ,DQL ,DCL Commands
- Selecting & Retrieving Data with SQL
- Filtering, Sorting, and Calculating Data with SQL
- Sub Queries and Joins in SQL

Descriptive Statistics

Session 38: Descriptive Statistics Part 1
- What is Statistics?
- Types of Statistics
- Population vs Sample
- Types of Data
- Measures of central tendency
- Measure of Dispersion
- Quantiles and Percentiles
- Five Number Summary
- Boxplots
- Scatterplots
- Covariance
- Correlation
Probability Distribution Functions (PDF, CDF, PMF)
- Random Variables
- Probability Distributions
- Probability Distribution Functions and its types
- Probability Mass Function (PMF)
- Cumulative Distribution Function (CDF) of PMF
- Probability Density Function (PDF)
- Density Estimation
- Parametric and Non-parametric Density Estimation
- Kernel Density Estimate (KDE)
- Cumulative Distribution Function (CDF) of PDF.

Probability Distributions

Session 41: Normal Distribution
- How to use PDF in Data Science?
- 2D density plots
- Normal Distribution (importance, equation, parameter, intuition)
- Standard Normal Variate (importance, z-table, empirical rule)
- Skewness
- Use of Normal Distribution in Data Science
Session 42: Non-Gaussian Probability Distributions
- Kurtosis and Types
- Transformation
  - Mathematical Transformation
  - Log Transform

- - Reciprocal Transform / Square or sqrt Transform
  - Power Transformer
  - Box-Cox Transform

Inferential Statistics

Session 43: Central Limit Theorem
- Bernouli Distribution
- Binomial Distribution
- Intuition of Central Limit Theorem (CLT)
- CLT in code
Session 44: Confidence Intervals
- Confidence Interval
  - Ways to calculate CI
  - Applications of CI
  - Confidence Intervals in code

Hypothesis Testing

Session 45: Hypothesis Testing (Part 1)
- Key idea of hypothesis testing
- Null and alternate hypothesis
- Steps in Hypothesis testing
- Performing z-test
- Rejection region and Significance level
- Type-1 error and Type-2 Error
- One tailed vs. two tailed test
- Applications of Hypothesis Testing
- Hypothesis Testing in Machine Learning
Session 46: Hypothesis Testing (Part 2) | p-value and t-tests
- What is p-value?
- Interpreting p-value
- T-test
- Types of t-test
  - Single sample t-Test
  - Independent 2-sample t-Test

- - Paired 2 sample t-Test
  - Code examples of all of the above

Session on Chi-square test
- Chi-square test
- Goodness of fit test (Steps, Assumptions, Examples)
- Test for Independence (Steps, Assumptions, Examples)
- Applications in machine learning
Session on ANOVA
- F-distribution
- One/Two-way ANOVA

Linear Algebra

Session on Tensors | Linear Algebra part 1(a)
- What are tensors?
- 0D, 1D and 2D Tensors
- Nd tensors
- Example of 1D, 2D, 3D, 4D, 5D tensors
Session on Vectors | Linear Algebra part 1(b)
- What is Linear Algebra?
- What are Vectors?
- Vector example in ML
- Row and Column vector
- Distance from Origin
- Euclidean Distance
- Scalar Addition/Subtraction (Shifting)
- Vector Addition/Subtraction
- Dot product
- Angle between 2 vectors
Linear Algebra Part 2 | Matrices (computation)
- What are matrices?
- Types of Matrices
- Matrix Equality
- Scalar Operation
- Matrix Addition, Subtraction, multiplication
- Transpose of a Matrix
- Determinant
- Inverse of Matrix
Linear Algebra Part 3 | Matrices (Intuition)
- Basis vector
- Linear Transformations
- Linear Transformation in 3D
- Matrix Multiplication as Composition
- Determinant and Inverse
- Transformation for non-square matrix?

Linear Regression

Session 48: Introduction to Machine Learning
- About Machine Learning (History and Definition)
- Types of ML
  - Supervised Machine Learning
  - Unsupervised Machine Learning
  - Semi supervised Machine Learning
  - Reinforcement Learning
- Batch/Offline Machine Learning
- Instance based learning
- model-based learning
- Instance vs model-based learning
- Challenges in ML
  - Data collection
  - Insufficient/Labelled data
  - Non-representative date
  - Poor quality data
  - Irrelevant features
  - Overfitting and Underfitting
  - Offline learning
  - Cost
- Machine Learning Development Life-cycle
- Different Job roles in Data Science
- Framing a ML problem | How to plan a Data Science project
Session 49: Simple Linear regression
- Introduction and Types of Linear Regression
- Intuition of simple linear regression
- How to find m and b?
- Regression Metrics
- MAE, MSE, RMSE, R2 score, Adjusted R2 score
Session 50: Multiple Linear Regression
- Introduction to Multiple Linear Regression (MLR)
- Mathematical Formulation of MLR
- Error function of MLR
- Session on Polynomial Regression
  - Why we need Polynomial Regression?
  - Formulation of Polynomial Regression
- Session on Assumptions of Linear Regression
- Session 53: Multicollinearity
  - What is multicollinearity?
  - How to detect and remove Multicollinearity
  - Correlation
  - VIF (Variance Inflation Factor)

Ready to be a Cybersecurity Professional?

Enroll in our 6-Month Program today and acquire the essential skills needed in today’s industry!

Cybersecurity is now a crucial concern for both individuals and organizations, as the frequency and complexity of cyber-attacks continue to rise. The growing demand for cybersecurity experts underscores the need for professionals who can effectively protect sensitive information and systems from these evolving threats. Earning a Cybersecurity certification is a great way to gain the essential skills and knowledge required to succeed in this high-stakes field.

Enroll Today!

Testimonials

What they say

As someone with a background in software development, I was initially worried about how I’d transition into cybersecurity. But this course made it so easy. The instructors broke down complex topics, and the mentorship was invaluable. Highly recommend it for anyone looking to enter the field.

This course offered the perfect balance of theory and practice. The detailed modules on network security and ethical hacking were eye-opening, and I could immediately apply my new knowledge to safeguard sensitive information. The personalized learning path was exactly what I needed

I took this course to upskill, and it was one of the best decisions for my career. The modules on threat detection, risk management, and ethical hacking were incredibly detailed and well-structured. I’m now better equipped to protect my company’s systems from cyber threats

I was completely new to IT, but this course helped me build a solid foundation in cybersecurity. The step-by-step approach, hands-on projects, and support from instructors gave me the confidence to pursue a career in this field. I’m now preparing for my first job as a cybersecurity analyst

I was impressed by the practical aspects of the course. It didn’t just teach the theory but also provided opportunities to work on real-world cybersecurity issues. The mentorship and guidance from industry experts made it easier to understand the challenges of the cybersecurity world

Data Science Course FAQs

Why are certifications crucial in the field of cybersecurity?

Certifications are crucial in the field of cybersecurity as they validate a professional’s knowledge and expertise, gaining recognition from leading companies and organizations.

Why are certifications essential in the field of cybersecurity?

The salary of a cybersecurity professional depends on factors such as experience, job role, and location, with an average range of $80,000 to $150,000 per year. Industry trends, company size, and the demand for cybersecurity professionals can also significantly influence these salaries.

Which major companies are hiring cybersecurity professionals?

Major companies such as IBM, Amazon, Microsoft, Cisco, and Google are actively hiring cybersecurity professionals. These organizations are constantly seeking skilled individuals to safeguard their networks and data against cyber threats.

What are the key industries within Cybersecurity?

Cybersecurity is a critical aspect of nearly every industry today, with major sectors including finance, healthcare, government, technology, and e-commerce. These industries are especially susceptible to cyber threats due to the large volumes of sensitive data they manage.

What is the current state of the Cybersecurity job market?

The Cybersecurity job market is growing rapidly due to increasing cyber threats and the need for skilled professionals.

What kind of job can I get with a Cybersecurity certificate?

A cybersecurity certificate enables individuals to pursue roles like Cybersecurity Analyst, Security Consultant, and Information Security Manager.

What are the key features of the AppliedTech Cybersecurity course?

Key features of the AppliedTech Cybersecurity course include hands-on training, real-world projects, and expert guidance from industry professionals.

How long is the validity of this course completion certificate?

The course completion certificate is valid for a lifetime and does not have an expiration date.

How can I enroll in the Cybersecurity course?

To enroll in the Cybersecurity course, visit the AppliedTech website and complete the enrollment form.

Does AppliedTech offer practice exams for the Cybersecurity course?

Yes, AppliedTech offers practice exams related to the Cybersecurity course to help you prepare effectively for assessments and certifications.