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MBA in Cybersecurity

(12 Months)

Embark on a transformative journey with AppliedTech’s MBA in Cybersecurity, crafted in partnership with Xaltius and BHS Switzerland. This comprehensive program is designed for future leaders who aim to navigate the complex landscape of digital threats and safeguard organizations from cyber risks. By integrating business management principles with cutting-edge cybersecurity strategies, you will develop the expertise necessary to assess vulnerabilities, implement robust defenses, and spearhead cybersecurity initiatives.

Enroll in AppliedTech MBA in Cybersecurity: Upgrade Your Skills and Advance Your Career!

In association with
Xaltius Logoa Singapore based Technology Education Enterprise.

Elevate your career with the AppliedTech MBA in Cybersecurity, meticulously crafted to equip you with the essential skills and knowledge required to thrive in the dynamic field of digital security. Our program offers a deep dive into the principles of cybersecurity, covering crucial topics such as risk management, advanced security strategies, and incident response. You’ll gain comprehensive insights into the evolving landscape of cyber threats while receiving hands-on experience through practical projects and a 12-month certified internship, allowing you to apply your learning in real-world scenarios.

Throughout the course, you’ll engage with industry experts who bring a wealth of experience and knowledge to the classroom. You’ll explore vital subjects such as networking, ethical hacking, social engineering, and forensic computing, ensuring you develop a well-rounded skill set. This program emphasizes the importance of practical application, enabling you to tackle complex security challenges and understand the intricacies of protecting sensitive information and systems.

Additionally, you will benefit from personalized mentorship and tailored career support designed to enhance your employability. Our dedicated team will assist you in refining your resume, preparing for interviews, and navigating job opportunities in the cybersecurity field. This support, combined with your academic achievements, will help you stand out in a competitive job market and align your career path with your professional goals.

Don’t miss the chance to transform your career trajectory and make a meaningful impact in the cybersecurity realm. Enroll in the AppliedTech MBA in Cybersecurity today and position yourself as a leader in safeguarding organizations against emerging cyber threats. Secure your spot now and take your career to new heights!

An overview of what you will learn from this program.

Gain a solid foundation in the fundamental principles, frameworks, and best practices of cybersecurity to effectively protect digital assets.

Explore the essentials of networking and understand how network architectures and protocols play a critical role in maintaining secure communications and defending against cyber threats

Learn the tools and techniques used in web development, equipping you to assess and secure web applications against potential vulnerabilities and attacks.

Delve into the mindset and methodologies of hackers to develop an in-depth understanding of their techniques, enabling you to proactively identify and mitigate security risks.

Study the art of human manipulation and deception employed by cybercriminals, equipping you to recognize and defend against social engineering attacks.

Explore the security challenges associated with the Internet of Things (IoT) and gain knowledge of cryptographic protocols and algorithms to ensure secure communication and data protection.

Acquire skills in digital forensics and investigation techniques to gather and analyze digital evidence, aiding in incident response and legal proceedings.

Learn strategies and technologies to secure applications and devices, implementing access controls and ensuring data integrity throughout their lifecycle.

Understand the importance of cybersecurity administration and audits, ensuring compliance with industry standards, regulatory requirements, and implementing robust security measures.

Showcase your expertise and research skills by undertaking a comprehensive master’s thesis and participate in a colloquium, engaging in intellectual discussions and knowledge exchange with scholars and experts in your field of study.

Download Curriculum

Program Highlights

Real-World Projects

Work on live projects reflecting industry scenarios, providing practical exposure to data-driven decision-making and advanced analytics in a real-world context for enhanced learning.

Instructor Training

Engage with top industry experts through interactive, live sessions, ensuring you receive personalized guidance and support throughout your educational journey and skill development.

Experienced Faculty Members​

Learn from leading industry experts. With a low student-to-instructor ratio, we ensure close interaction with faculty and provide personalized support for every student.

Globally Recognized MBA Degree

Earn an internationally recognized MBA degree from BHS Switzerland, validating your expertise in both business administration and data science for career advancement.

Cutting-Edge Curriculum

The MBA in Cybersecurity at AppliedTech features a comprehensive curriculum designed to equip you with essential skills to effectively combat emerging digital threats.

Career Counseling Services

If you seek an MBA in Cybersecurity with robust career counseling assistance, this is your perfect opportunity to enhance your valuable skills and effectively advance your career!

About the MBA in Cybersecurity

MBA in Cybersecurity: Your Path to Professional Excellence

The MBA in Cybersecurity program at AppliedTech, in collaboration with Xaltius and BHS Switzerland, is designed to equip future leaders with the skills needed to navigate the complex landscape of digital threats. This comprehensive program blends advanced cybersecurity principles with essential business management strategies, empowering you to safeguard organizations against cyber risks.

With a focus on real-world applications, the curriculum covers critical areas such as cybersecurity fundamentals, networking concepts, ethical hacking, social engineering defenses, and digital forensics. Participants will gain hands-on experience through practical projects, industry collaborations, and mentorship from cybersecurity experts.

Whether you are a seasoned professional aiming to enhance your leadership skills or a newcomer seeking to specialize in cybersecurity, this program offers the knowledge and expertise to thrive in this rapidly evolving field. Join us on a transformative journey toward professional excellence and become a vital contributor to the security of our digital world.

Get Ahead with BHS Switzerland and Xaltius’ Academy MBA in Cybersecurity!

Globally Recognized MBA Degree Certificate from BHS Switzerland
Internship Certificate from Participating Companies
Letter of Recommendation

Why to choose AppliedTech :

AppliedTech is a leading provider of instructor-led training, boasting over 100,000 learners across more than 20 countries. Our mission is to democratize education, ensuring that everyone has access to high-quality learning opportunities. Courses are delivered by industry experts from top multinational corporations, and our world-class teaching methods enable learners to grasp complex topics quickly and effectively. With 24/7 technical support and comprehensive career services, we are committed to helping individuals launch successful careers in their desired fields.

The demand for cybersecurity professionals is steadily increasing each year, driven by a significant skills gap and the escalating threat of cybercrime. According to the U.S. Bureau of Labor Statistics, cybersecurity job openings are projected to grow by 33% over the next decade. Notably, the demand for cybersecurity talent has outpaced the overall job growth in the U.S. economy, increasing 2.4 times faster.

These skills are not only in high demand, but professionals in this space continue to command impressive compensation packages, even in industries that are tightening their budgets elsewhere. Experience and training in critical areas of cybersecurity can significantly enhance a candidate’s resume, positioning them at the forefront of the job market.

Cybersecurity is a multifaceted discipline that involves everything from threat mitigation and vulnerability assessment to data recovery following cyber incidents. Recognizing this urgent need, AppliedTech has developed a specialized program designed to elevate your cybersecurity expertise. Whether you are aiming to enter the field, advance your career, or build valuable applications for your organization, our courses will equip you with the best industry practices and the latest technologies to effectively combat cyber threats.

Program Eligibility Criteria and Prerequisites :

Ready to be a Cybersecurity Professional?

The MBA in Cybersecurity program offered by AppliedTech in collaboration with BHS Switzerland is designed for professionals eager to advance their careers in the dynamic field of cybersecurity. This program is tailored for individuals with backgrounds in information technology, computer science, or related disciplines who aspire to lead efforts in protecting organizations from cyber threats. Whether you’re an established cybersecurity expert looking to refine your managerial and leadership capabilities or a business professional seeking to specialize in cybersecurity, this program equips you with the essential knowledge and skills needed to navigate the constantly evolving landscape of digital security. Join us to gain valuable insights and hands-on experience, empowering you to safeguard organizations and make a meaningful impact in the cybersecurity realm.

Enroll Today!

    Course Syllabus In Detail :

    • 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
    • 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
    • Create functions.
    • Arguments and parameters
    • args and kwargs
    • map(), filter(), reduce()
    • Assignments and Interview Questions
    • 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
    • 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
    • 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.
    • 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.
    • 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
    • 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
    • Session 25: Plotting Using Seaborn
      • Why seaborn?
      • Categorical Plots
      • Stripplot
      • Swarmplot
      • Categorical Distribution Plots
      • Boxplot
      • Violinplot
      • Barplot
    • 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.
    • 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
    • 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.
    • 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
    • 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
    • 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
    • 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?
    • 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)
    •  
    •  
    •  
      1.  
    • 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
    • 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
    • Create functions.
    • Arguments and parameters
    • args and kwargs
    • map(), filter(), reduce()
    • Assignments and Interview Questions
    • 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
    • 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
    • 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.
    • 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.
    • 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
    • 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
    • Session 25: Plotting Using Seaborn
      • Why seaborn?
      • Categorical Plots
      • Stripplot
      • Swarmplot
      • Categorical Distribution Plots
      • Boxplot
      • Violinplot
      • Barplot
    • 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.
    • 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
    • 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.
    • 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
    • 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
    • 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
    • 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?
    • 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)
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    •  
      1.  

    Testimonials

    What they say

    “I enrolled in this Data Science Certification Course with no prior experience, and by the end, I had a solid understanding of Python, machine learning, and data visualization. The hands-on projects and personalized mentorship were game-changers. I was able to land a job within two months of completing the course!"
    Amit SData Analyst
    “The course not only taught me technical skills like SQL and Python, but it also helped me understand how to apply these skills to solve real-world problems. The career counseling and interview prep really boosted my confidence. I highly recommend this course to anyone looking to break into data science.”
    Priya M.Junior Data Scientist
    “The faculty were incredibly knowledgeable and patient, guiding us through each module step-by-step. The live projects we worked on during the course gave me the confidence to work independently on my own. This course helped me transition from a non-technical background to a data science career.”
    Rahul GBusiness Intelligence Analyst
    “What stood out for me was the 1-on-1 mentorship and career support. Whenever I was stuck, my mentor was always available to help. The resume and LinkedIn profile building sessions were very useful, and I got multiple interview calls soon after completing the course.”
    Shreya D.Machine Learning Engineer
    “This course covered everything I needed to know to get started in data science, from Python basics to advanced machine learning. The real-world projects were especially valuable for building my portfolio, and the career services team was instrumental in helping me secure my first data science job.”
    Karthik N.Data Scientist
    "I feel immensely grateful for being a part of this outstanding academic community that has empowered me to pursue my dreams and achieve success in my career."
    Pratiksha N,Student
    "I feel immensely grateful for being a part of this outstanding academic community that has empowered me to pursue my dreams and achieve success in my career."
    Nisha D.Student
    "I feel immensely grateful for being a part of this outstanding academic community that has empowered me to pursue my dreams and achieve success in my career."
    Anushka C.Student
    "I loved the way they take care of everything related to the course and support."
    Sandhya T.Student
    "I learned Stats and ML and I liked the way sir taught me the concepts. Easy to understand."
    Vijay D.Student

    MBA in Cybersecurity FAQs

    The MBA in Cybersecurity program typically spans two years, designed to provide a comprehensive understanding of the field while accommodating the busy schedules of working professionals.

    Applicants generally need to hold a bachelor’s degree from an accredited institution. While a background in IT or business is advantageous, it is not strictly required.

    Yes, individuals without a background in computer science or programming can still pursue an MBA in Cybersecurity. The program is structured to accommodate various educational backgrounds, and foundational courses are often included to help bridge any knowledge gaps.

    The program offers various specializations, including Information Security Management, Cybersecurity Risk Management, Digital Forensics, and Security Analytics, allowing students to tailor their education to their career goals.

    Absolutely! The MBA in Cybersecurity program at AppliedTech is designed with flexibility in mind, enabling students to balance their studies with full-time employment through evening and online classes.

    Graduates can explore a wide range of career paths, including roles such as Cybersecurity Manager, Information Security Analyst, Risk Management Consultant, and Chief Information Security Officer (CISO).

    Key features of the AppliedTech Cybersecurity course include hands-on training with industry-standard tools, real-world projects, and mentorship from experienced professionals in the cybersecurity field.

    The course completion certificate from AppliedTech is valid indefinitely, serving as a testament to your skills and knowledge in cybersecurity.

    1. To enroll in the MBA in Cybersecurity program, you can visit the AppliedTech website, fill out the application form, and submit the required documents. Our admissions team will guide you through the process to ensure a smooth enrollment experience.

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