MBA in Data Science
(12 Months)
The MBA in Data Science, offered by AppliedTech in collaboration with Xaltius and BHS Switzerland, combines business management with advanced analytics. This 12-month, fully online program equips professionals with the skills to make data-driven decisions, featuring real-world projects, personalized mentorship, and global networking opportunities. Unlock career potential at the intersection of business and data science.
Enroll in AppliedTech MBA in Data Science: Upgrade Your Skills and Advance Your Career!
a Singapore based Technology Education Enterprise.Elevate your career with AppliedTech’s MBA in Data Science, a meticulously crafted 12-month program that merges essential business management expertise with advanced data analytics skills. Developed in collaboration with BHS Switzerland, this unique curriculum provides participants with the opportunity to learn directly from seasoned industry leaders through engaging live instructor-led sessions and hands-on real-world projects. This blend of theoretical knowledge and practical application ensures that you are well-prepared to tackle the challenges of today’s dynamic business environment.
Throughout the course, you will delve into critical subjects such as machine learning, artificial intelligence, data visualization, and advanced analytics techniques, which are indispensable in our increasingly data-driven society. The program places a strong emphasis on skill development, ensuring that you not only understand the concepts but also apply them effectively in practical scenarios. Additionally, you’ll receive personalized mentorship and comprehensive hands-on training that reinforces your learning experience and prepares you for impactful roles in both business and data science.
One of the standout features of the MBA in Data Science is the 12-month certified internship that allows you to gain valuable industry experience. This internship provides a vital opportunity to apply your skills in a professional setting, enabling you to understand the real-world implications of data-driven decision-making. By engaging in practical projects, you will not only enhance your technical proficiency but also build a strong portfolio that showcases your capabilities to potential employers.
Whether you are just starting your career or looking to advance in your field, this globally recognized MBA program will empower you to make informed, data-driven decisions and significantly boost your employability. Join AppliedTech’s MBA in Data Science and become an integral part of the evolving landscape where business meets technology, positioning yourself as a leader in the field of business intelligence. Embrace the opportunity to shape the future of data science and unlock limitless career possibilities.
An overview of what you will learn from this program.
Master the language of data science and leverage its powerful libraries for data manipulation, analysis, and machine learning in the context of your MBA studies
Learn the principles and techniques to effectively manage and analyze large datasets, enabling you to make informed business decisions based on reliable data
Gain proficiency in R programming language, exploring its applications in statistical analysis, data visualization, and predictive modeling to uncover valuable insights for business strategies.
Dive deep into data exploration techniques, uncover patterns, and gain meaningful insights from complex datasets to drive data-driven decision-making in the business context
Acquire the skills to develop and apply machine learning algorithms, enabling you to extract actionable insights and predict trends for informed business strategies.
Learn the process of deploying machine learning models into real-world scenarios, ensuring their scalability and usability for practical business applications.
Explore the frontiers of AI and deep learning, understanding their potential in revolutionizing business processes and driving innovation in various industries.
Master the art of visual storytelling by creating compelling and informative visualizations using popular tools like Tableau or Power BI to effectively communicate data-driven insights to stakeholders
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
Program Highlights
Real-World Projects
Work on live projects that reflect real-world industry scenarios, providing practical exposure to data-driven decision-making and advanced analytics for enhanced professional experience.
Instructor Training
Engage with top industry experts through interactive, live sessions, ensuring you receive personalized guidance and insights throughout your learning journey and skill development.
Experienced Faculty Members
Learn from leading industry experts. With a favorable student-to-instructor ratio, we ensure close interaction with faculty, providing personalized support tailored to your needs.
Global MBA Degree
Earn an internationally recognized MBA degree from BHS Switzerland, validating your expertise in both business administration and data science for future career opportunities.
Cutting-Edge Curriculum
Master essential skills like Python programming, machine learning, AI, and data visualization (Tableau/Power BI), all tailored to meet the demands of today’s data-driven landscape.
Career Counseling Services
Enhance your soft skills with dedicated career counseling assistance, preparing you for successful transitions and advancements in your professional journey across industries.
About the MBA in Data Science
MBA in Data Science: Your Path to Professional Excellence
Embark on a transformative journey with AppliedTech’s MBA in Data Science, developed in collaboration with BHS Switzerland. This innovative program is tailored to meet the demands of the modern business landscape by seamlessly blending essential business management principles with advanced data analytics techniques. Designed for aspiring leaders and decision-makers, our MBA program empowers you to harness the power of data, enabling informed decision-making and strategic insights that drive success in today’s data-driven world.
Over the course of 12 months, you will engage in live instructor-led training that provides an interactive learning environment. Our curriculum covers a wide range of topics, including Python programming, machine learning, data visualization, and artificial intelligence, ensuring you develop a comprehensive understanding of the data science field. You will also have the opportunity to work on real-world projects that enhance your practical skills and prepare you for the challenges of the industry.
One of the standout features of our MBA program is the certified internship component, which allows you to gain invaluable hands-on experience in a professional setting. This internship, coupled with personalized one-on-one mentorship from industry experts, ensures that you not only learn the theoretical aspects of data science but also apply them effectively in real-world scenarios.
Our program is designed for individuals from diverse backgrounds—whether you are a recent graduate looking to kickstart your career or a seasoned professional seeking to advance your skills and knowledge. With a globally recognized degree from BHS Switzerland, you will stand out in the competitive job market, unlocking limitless career opportunities in data science and business analytics.
At AppliedTech, we are committed to empowering our students with the tools they need to succeed. Join us and become a leader in the dynamic field of data science, where business meets analytics, and shape the future of business intelligence. Enroll in our MBA in Data Science today and take the first step towards achieving your professional excellence!
Get Ahead with BHS Switzerland and Xaltius’ Academy MBA in Data Science!
Globally Recognized MBA Degree Certificate from BHS Switzerland
Internship Certificate from Participating Companies
Letter of Recommendation
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.
Program Eligibility Criteria and Prerequisites :
☆ Applicants must be at least 18 years old.
☆ A copy of a valid government-issued photo ID is required for submission.
☆ An updated resume must be submitted along with your application.
☆ Any documents not in English must include a certified translation.
☆ Proficiency in English is required; you may need to provide evidence of passing a recognized language proficiency test within the last three years.
☆ Access to a computer and the internet is necessary for all programs.
☆ After expressing your interest, you will receive an application form to complete.
☆ Once you’ve filled it out and submitted it, your application will be processed, and you will be notified regarding your eligibility status.
☆ If you do not meet our entrance criteria automatically, your application will be reviewed by the Admissions Panel for further evaluation.
☆ The Admissions Panel will assess qualifications not listed in the Entrance Qualifications Schedule, incomplete qualifications, and significant relevant work experience.
☆ Should your current qualifications and experience not meet our acceptance criteria, we will provide guidance on the qualifications you can pursue to fulfill our entrance requirements in the future.
☆ If your application is successful, you will receive an offer letter via email along with detailed registration instructions from AppliedTech.
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
- Confidence Interval
- 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)
- 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
- Confidence Interval
- 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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MBA in Data Science Course FAQs
The MBA in Data Science 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 Data Science . 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, Data Science Risk Management, Digital Forensics, and Security Analytics, allowing students to tailor their education to their career goals.
Graduates of an MBA in Data Science program can explore a variety of career paths, including Data Scientist, Business Analyst, Data Engineer, Machine Learning Engineer, and Data Analyst, among others. The demand for skilled professionals in this field continues to grow across various industries.
The course completion certificate from AppliedTech is valid indefinitely, serving as a testament to your skills and knowledge in cybersecurity.
To enroll in the MBA in Data Scientist 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.