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SSBM

MBA in Data Science

507 Ratings

Ranked Top 10 in Europe

Become a Master in Data Science by working on industry-oriented projects through our MBA in Data Science Program in just 12 months!

  • MBA in Data Science program is offered by the Swiss School of Business and Management in Geneva
  • The program helps in acquiring expertise in Data Science
  • Learn MBA in Data Science from top Industry Experts
  • This online course covers Big data basics, Data visualization, and Ethical Considerations in AI.

Accredited & Certified by

ACBSP_CHEA_EDU
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Offer: Get Advanced Certification in Data Analytics by IHub, IIT Roorkee

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Learning Format

Online

Duration

12 Months

Career Services

by Intellipaat

MBA in Data Science

by SSBM

400+

Hiring Partners

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About MBA in Data Science Program

Enroll in the MBA in data science program and gain cutting-edge knowledge and skills to stay ahead in the ever-changing business landscape. The course syllabus covers important topics, including big data basics, decision-making with data analytics, data visualization, ethical considerations in AI, and AI for managers. This MBA in Data Science program offers a great opportunity to develop an understanding of business forces and the skills needed to stay ahead.Read More

Key Highlights

12 Months MBA Program
SSBM Connect
60 ECTS Credits
Personal Advisor
MBA in Data Science from SSBM
Free Adv. Certification in Data Analytics from iHUB, IIT Roorkee
Career Services from Intellipaat
Industry Oriented Projects
Master Classes by SSBM Faculty
Multiple Case Studies
Learn Data Analytics from Industry experts by Intellipaat
24*7 Support
One-on-One with Industry Mentors
Essential Soft Skills Training
No-Cost EMI Option
1:1 Mock Interview
Access to SSBM e-Library and ESBCO
Swiss Quality Education
WES Recognized

About SSBM (Swiss School of Business and Management, Geneva)

SSBM is a renowned college in Geneva, Switzerland, and is known for its Swiss-quality education and excellence all over the world. The institute has partnered with over 30 top companies to design its courses and has a remarkable set of alumni across the globe.

Key Achievements:

  • The university holds the EduQua (a Swiss national quality assurance body) label for delivering quality education to students.
  • It is an ACBSP-accredited institution.
  • Ranked as the #1 leader in providing innovative financial educational programs by Silcom Consulting
  • Ranked #6th best private institution in Switzerland by Primavera
  • Ranked #2 globally for its learning management system by LMS.

Upon Completion of this course, you will:

  • Receive a MBA in Data Science from SSBM
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About iHUB DivyaSampark, IIT Roorkee

iHUB DivyaSampark aims to enable innovative ecosystem in new age technologies like AI, ML, Drones, Robots, data analytics (often called CPS technologies) and becoming the source for the next generation of digital technologies, products and services by promoting, enhancing core competencies, capacity building, manpower training to provide solutions for national strategic sectors andRead More..

Key Achievements of IIT Roorkee:

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Career Transition

55% Average Salary Hike

$1,20,000 Highest Salary

12000+ Career Transitions

400+ Hiring Partners

Career Transition Handbook

*Past record is no guarantee of future job prospects

Who can apply for the MBA in Data Science course?

  • Anyone willing to learn and get certified in management and administration
  • Managers operating at a strategic level in the workplace
  • Individuals with a bachelor’s degree and a keen interest in learning Data Science and Management
  • IT professionals looking for a career transition to Management
  • Professionals aiming to move ahead in their careers
  • MBA aspirants
  • Professionals looking to grow their careers with an MBA in Data Science degree
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What roles can an MBA in Data Science graduate play?

Data Scientist

Develop high-quality applications, apart from designing and implementing scalable code.

Analytics and Insights Analyst

Investigate reported problems in the quality of data and come up with solutions to fix them.

Risk Manager

They analyze risks associated with major business decisions and develop scenarios to avert future damage to the company.

Data Engineer and Data Analyst

Understand the data, data cleansing, data transformation, analyze outcomes, and present the result in the form of reports and dashboards.

Accounting Manager

Accounting Managers are responsible for the management of the daily operations of the accounting department.

Financial Controller

Financial Controllers are responsible for measuring the profitability of the entire company using KPI systems.

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Skills to Master

Big Data

Data Analytics

Business Ethics

Financial Management

Project Management

Strategic Management

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Meet Data Science Mentors

MBA in Data Science Syllabus

Live Course Self Paced Industry Expert Academic Faculty

Required Courses for MBA in Data Science

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Adv. Certification in Data Analytics by iHub IIT Roorkee Live Sessions (Optional)

  1. Introduction to SQL
  2. Database Normalization and Entity Relationship Model(self-paced)
  3. SQL Operators
  4. Working with SQL: Join, Tables, and Variables
  5. Deep Dive into SQL Functions
  6. Working with Subqueries
  7. SQL Views, Functions, and Stored Procedures
  8. Deep Dive into User-defined Functions
  9. SQL Optimization and Performance
  10. Advanced Topics
  11. Managing Database Concurrency
  12. Practice Session

Case Study

Writing comparison data between past year to present year with respect to top products, ignoring the redundant/junk data, identifying the meaningful data, and identifying the demand in the future(using complex subqueries, functions, pattern matching concepts).

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Introduction to Python and IDEs – The basics of the python programming language, how you can use various IDEs for python development like Jupyter, Pycharm, etc.

Python Basics – Variables, Data Types, Loops, Conditional Statements, functions, decorators, lambda functions, etc.
Object Oriented Programming – Introduction to OOPs concepts like classes, objects, inheritance, abstraction, polymorphism, encapsulation, etc.

Data Manipulation with Numpy, Pandas, and Visualization – Using large datasets, you will learn about various techniques and processes that will convert raw unstructured data into actionable insights for further computations i.e. machine learning models, etc.

Case Study – The culmination of all the above concepts with real-world problem statements for better understanding.

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Descriptive Statistics

  1. Measure of central tendency, measure of spread, five points summary, etc.

Probability

  1. Probability Distributions, Probability in Business Analytics
  2. Probability Distributions, Binomial distribution, Poisson distribution, bayes theorem, central limit theorem.

Inferential Advanced Statistics (by Academic Faculty)

  1. Correlation, covariance, confidence intervals, hypothesis testing, F-test, Z-test, t-test, ANOVA, chi-square test, etc.

Case Study

This case study will cover the following concepts:

  1. Building a statistical analysis model that uses quantification, representations, and experimental data
  2. Reviewing, analyzing, and drawing conclusions from the data
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Introduction to Machine learning

  1. Supervised, Unsupervised learning.
  2. Introduction to scikit-learn, etc.

Regression

  1. Introduction classification problems, Identification of a regression problem, dependent and independent variables.
  2. How to train the model in a regression problem.
  3. How to evaluate the model for a regression problem.
  4. How to optimize the efficiency of the regression model.

Classification

  1. Introduction to classification problems, Identification of a classification problem, dependent and independent variables.
  2. How to train the model in a classification problem.
  3. How to evaluate the model for a classification problem.
  4. How to optimize the efficiency of the classification model.

Clustering

  1. Introduction to clustering problems, Identification of a clustering problem, dependent and independent variables.
  2. How to train the model in a clustering problem.
  3. How to evaluate the model for a clustering problem.
  4. How to optimize the efficiency of the clustering model.

Supervised Learning

  1. Linear Regression – Creating linear regression models for linear data using statistical tests, data preprocessing, standardization, normalization, etc.
  2. Logistic Regression – Creating logistic regression models for classification problems – such as if a person is diabetic or not, if there will be rain or not, etc.

Unsupervised Learning

  1. K-means – The k-means algorithm that can be used for clustering problems in an unsupervised learning approach.
  2. Dimensionality reduction – Handling multi dimensional data and standardizing the features for easier computation.
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  1. Classification reports – To evaluate the model on various metrics like recall, precision, f-support, etc.
  2. Confusion matrix – To evaluate the true positive/negative, false positive/negative outcomes in the model.
  3. r2, adjusted r2, mean squared error, etc.
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Feature Selection – Feature selection techniques in python that includes recursive feature elimination, Recursive feature elimination using cross validation, variance threshold, etc.

Feature Engineering – Feature engineering techniques that help in reducing the best features to use for data modeling.

Model Tuning – Optimization techniques like hyperparameter tuning to increase the efficiency of the machine learning models.

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Making use of time series data, gathering insights and useful forecasting solutions using time series forecasting.

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Business Domains – Learn about various business domains and understand how one differs from the other.

  1. Finance
  2. Marketing
  3. Retail
  4. Supply Chain

Understanding the business problems and formulating hypotheses – Learn about formulating hypotheses for various business problems on samples and populations.

Exploratory Data Analysis to Gather insights – Learn about the exploratory data analysis and how it enables a fool proof producer of actionable insights.

Data Storytelling: Narrate stories in a memorable way – Learn to narrate business problems and solutions in simple relatable format that makes it easier to understand and recall.

Case Study
This case study will cover the following concepts:

  1. Create actionable insights from raw unstructured data to solve real world business problems.
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Problem Statement and Project Objectives – You will learn how to formulate various problem statements and understand the business objective of any problem statement that comes as a requirement.

Approach for the Solution – Creating various statistical insights based solutions to approach the problem will guide your learnings to finish a project from scratch.

Optimum Solutions – Formulating actionable insights backed by statistical evidence will help you find the most effective solution for your problem statements.

Evaluation Metrics – You will be able to apply various evaluation metrics to your project/solution. It will validate your approach and point towards shortcomings backed by insights, if any.

Gathering Actionable insights – You will learn about how a problem’s solution isn’t just creating a machine learning model, the insights that were gained from your analysis should be presentable in the form of actionable insights to capitalize on the solutions formulated for the problem statement.

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Customer Churn – The case study involves studying the customer data for a given XYZ company, and using statistical tests and predictive modeling, we will gather insights to efficiently create an action plan for the same.

Sales Forecasting – By studying the various patterns and sales data for a firm/store, we will use the time series forecasting method to forecast the number of sales for the next given time period(weeks, months, years, etc.)

Census – After studying the population data, we will gather insights and through predictive modeling try to create actionable insights on the same, it could be average income of an individual, or most likely profession, etc.

Predictive Modeling – Various case studies on categorical and continuous data, to create predictive models that will predict specific outcomes based on the business problems.

HR Analytics – Based on the data provided by a firm, we will study the HR analytics data, and create actionable insights using various statistical tests and hypothesis testing.

Dimensionality Reduction – To understand the impact of multidimensional data, we will go through various dimensionality reduction techniques and optimize the computational time on the same that will eventually be used for various classification and regression problems.

Housing – A case study that will give you insight into how real estate firms can narrow down on the pricing, customer choices, etc. using various predictive modeling techniques.

Customer Segmentation – Using unsupervised learning techniques, we will learn about customer segmentation, which can be quite useful for e-commerce sectors, stores, marketing funnels, etc.

Inventory Management – In this case study, you will learn about how meaningful insights can be used to drive a supply chain, using predictive modeling and clustering techniques.

Disease Prediction – A medical endeavor that is achieved through machine learning will give you an insight into how the predictive model can prove to be a great marvel in early detection of various diseases.

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Elective

Excel Fundamentals

  1. Reading the Data, Referencing in formulas , Name Range, Logical
  2. Functions, Conditional Formatting, Advanced Validation, Dynamic Tables in Excel, Sorting and Filtering
  3. Working with Charts in Excel, Pivot Table, Dashboards, Data And File Security
  4. VBA Macros, Ranges and Worksheet in VBA
  5. IF conditions, loops, Debugging, etc.

Excel For Data Analytics

  1. Handling Text Data, Splitting, combining, data imputation on text data, Working with Dates in Excel, Data Conversion, Handling Missing Values, Data Cleaning, Working with Tables in Excel, etc.

Data Visualization with Excel

  1. Charts, Pie charts, Scatter and bubble charts
  2. Bar charts, Column charts, Line charts, Maps
  3. Multiples: A set of charts with the same axes, Matrices, Cards, Tiles

Ensuring Data and File Security

  1. Data and file security in Excel, protecting row, column, and cell, the different safeguarding techniques.

Getting started with VBA Macros

  1. Learning about VBA macros in Excel, executing macros in Excel, the macro shortcuts, applications, the concept of relative reference in macros, In-depth understanding of Visual Basic for Applications, the VBA Editor, module insertion and deletion, performing action with Sub and ending Sub if condition not met.

Statistics with Excel

  1. ONE TAILED TEST AND TWO TAILED T-TEST, LINEAR REGRESSION,PERFORMING STATISTICAL ANALYSIS USING EXCEL, IMPLEMENTING LINEAR REGRESSION WITH EXCEL
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Power BI Basics

  • Introduction to PowerBI, Use cases and BI Tools , Data Warehousing, Power BI components, Power BI Desktop, workflows and reports , Data Extraction with Power BI.
  • SaaS Connectors, Working with Azure SQL database, Python and R with Power BI
  • Power Query Editor, Advance Editor, Query Dependency Editor, Data Transformations, Shaping and Combining Data ,M Query and Hierarchies in Power BI.

DAX

  • Data Modeling and DAX, Time Intelligence Functions, DAX Advanced Features

Data Visualization with Analytics

  • Slicers, filters, Drill Down Reports
  • Power BI Query, Q & A and Data Insights
  • Power BI Settings, Administration and Direct Connectivity
  • Embedded Power BI API and Power BI Mobile
  • Power BI Advance and Power BI Premium

Case Study:

This case study will cover the following concepts:

  • Creating a dashboard to depict actionable insights in sales data.
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Program Highlights

12 Months MBA Program
Free Adv. Certification in Data Analytics from iHUB, IIT Roorkee
Access to SSBM e-Library and ESBCO
24*7 Support

Hear From Our Hiring Partners

Career Services By Intellipaat

Career Services
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Career Oriented Sessions

Throughout the course

Over 20+ live interactive sessions with an industry expert to gain knowledge and experience on how to build skills that are expected by hiring managers. These will be guided sessions and that will help you stay on track with your up skilling objective.

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Resume & LinkedIn Profile Building

After 70% of course completion

Get assistance in creating a world-class resume & LinkedIn Profile from our career services team and learn how to grab the attention of the hiring manager at profile shortlisting stage

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Mock Interview Preparation

After 80% of the course completion

Students will go through a number of mock interviews conducted by technical experts who will then offer tips and constructive feedback for reference and improvement.

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1 on 1 Career Mentoring Sessions

After 90% of the course completion

Attend one-on-one sessions with career mentors on how to develop the required skills and attitude to secure a dream job based on a learners’ educational background, past experience, and future career aspirations.

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3 Guaranteed Interviews

Upon movement to the Placement Pool

Guaranteed 3 job interviews upon movement to the placement pool after clearing the Placement Readiness Test ( PRT). Get interviewed by our 400+ hiring partners.

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Exclusive access to Intellipaat Job portal

After 80% of the course completion

Exclusive access to our dedicated job portal and apply for jobs. More than 400 hiring partners’ including top start-ups and product companies hiring our learners. Mentored support on job search and relevant jobs for your career growth.

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Peer Learning

Via Intellipaat PeerChat, you can interact with your peers across all classes and batches and even our alumni. Collaborate on projects, share job referrals & interview experiences, compete with the best, make new friends – the possibilities are endless and our community has something for everyone!

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Admission Details

The application process consists of three simple steps. An offer of admission will be made to selected candidates based on the feedback from the interview panel. The selected candidates will be notified over email and phone, and they can block their seats through the payment of the admission fee.

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Submit Application

Tell us a bit about yourself and why you want to join this program

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Application Review

An admission panel will shortlist candidates based on their application

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Admission

Selected candidates will be notified within 1–2 weeks

Program Fee

Total Admission Fee

$ 7,895

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Upcoming Application Deadline 25th May 2024

Admissions are closed once the requisite number of participants enroll for the upcoming cohort. Apply early to secure your seat.

Program Cohorts

Next Cohorts

Date Time Batch Type
Program Induction 25th May 2024 08:00 PM - 11:00 PM IST Weekend (Sat-Sun)
Regular Classes 25th May 2024 08:00 PM - 11:00 PM IST Weekend (Sat-Sun)

Frequently Asked Questions

Who are the instructors of this MBA in data science from SSBM?

The course instructors for this MBA in data science are experts and leading academicians from SSBM, Geneva.

Having knowledge and expertise in management, administration and data science is a must these days for career advancement. This certification is conducted by leading experts from SSBM, who will assist you in kick-starting your career as a successful manager through the vast industry-relevant experience that they carry.

Also, the course curriculum, along with videos, live sessions, and assignments, will help you gain in-depth knowledge of the modern business environment and processes.

To register for the program, you can reach out to our learning consultants or contact us through the above-given details on this page.

Intellipaat actively provides placement assistance to all learners who have successfully completed the training. For this, we are exclusively tied up with over 80 top MNCs from around the world. This way, you can be placed in outstanding organizations such as Sony, Ericsson, TCS, Mu Sigma, Standard Chartered, Cognizant, and Cisco, among other equally great enterprises. We also help you with the job interview and résumé preparation.

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What is included in this course?

  • Non-biased career guidance
  • Counselling based on your skills and preference
  • No repetitive calls, only as per convenience
  • Rigorous curriculum designed by industry experts
  • Complete this program while you work