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Post Graduate Diploma in Business Analytics

Kickstart your career in Business Analytics through SSBM’s Post Graduate diploma in Business Analytics. Master the core Business Analysis skills with numerous industry based case studies and projects from switzerland.

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Upskill for Your Dream Job

Learning Format


Live Classes

7 Months

Career Services

by Intellipaat

SSBM Diploma



Hiring Partners

About Program

The objective of the program is to help learners develop a strong skill set in Business Analytics and train them to become data-savvy managers who can utilize analytics to ask the right questions, and most importantly, interpret data to make better decisions. Some of the important concepts covered include Data Analytics, Visualization, Data Processing and Power BI. The capstone project and multiple exercises have been included to provide you with a practical exposure.Read More

Key Highlights

7 Months of live classes
25 Hrs of Self-Paced Learning
50+ Industry Projects & Case Studies
1:1 Industry Mentorship
24*7 Support
5 ECTS Credits
Learn from SSBM Faculty & Industry Practitioners
SSBM Post Graduate Diploma in Business Analytics
3 Guaranteed Interviews by Intellipaat
Designed for Working Professionals & Fresher's

Free Career Counselling

We are happy to help you 24/7

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 for designing its courses and has a remarkable set of powerful alumni across the globe.

Key Achievements:

  • The university holds the EduQua ( a Swiss national quality assurance body) label for delivering a quality education to the students.
  • It is an ACBSP accredited institution.
  • Ranked as #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 Certification of Post Graduate Diploma in Business Analytics from SSBM.

Who can apply for the course?

  • Anyone willing to learn and pursue a career in Business Analytics
  • Individuals with a bachelor’s degree and a keen interest to learn Business and Data Analytics.
  • IT professionals looking for a career transition to Business Analysts
  • Professionals aiming to move ahead in their IT career
  • Business Analysis professionals willing to validate and develop skills in the domain.
  • Developers and Project Managers
  • Freshers who aspire to build their career in the field of Business Analysis and Data Science
Who can aaply

What roles can a Business Analysis professional play?

Business Analyst

Extract data from the respective sources to perform business analysis, and generate reports, dashboards, and metrics to monitor the company’s performance.

Data Analyst

Build a cross-brand and robust strategy of data acquisition and analytics, along with designing raw data transformation for analytical application.

BI Developer

Create the required infrastructure to get optimal data extraction, transformation, and loading from a range of data sources.

Data Scientist

Use data analysis and data processing to understand business challenges and offer the best solutions to the organization.

Marketing and Sales Analyst

Improve user experience with the help of personalization and help clients leverage analytical and digital technologies.

Data Architect

Create blueprints for managing data so as to facilitate easy integration, centralization, and protection of the database along with due security precautions.

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Skills to Master

Data Exploration

Data Aggregation

Data Visualization

Business Analysis

Data Analysis

Decision making

Data Processing



Machine learning

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Tools to Master

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Live Course
  1. 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. 
  2. Python Basics – Variables, Data Types, Loops, Conditional Statements, functions, decorators, lambda functions, file handling, exception handling ,etc.
  3. Object Oriented Programming – Introduction to OOPs concepts like classes, objects, inheritance, abstraction, polymorphism, encapsulation, etc.
  4. Hands-on Sessions And Assignments for Practice – The culmination of all the above concepts with real-world problem statements for better understanding.
  • What is Data – Introduction to data, various types of data, how data can be represented in various formats, how you can make use of unstructured data, etc. 
  • What is Data AnalyticsIntroduction to data analytics, data analytics life cycle, steps involved in data analytics, importance of data analytics, how data analytics is used to gather important insights from unstructured data, real world applications of successful campaigns driven by data analytics.
  • What is Decision Making – Introduction to decision making, importance of decision making, components and steps involved in decision making, data driven decision making and real world applications.
  • Linear Algebra And Advanced Statistics 
      • Descriptive Statistics – Measure of central tendency, measure of spread, five points summary, etc. 
      • Probability -Probability Distributions, bayes theorem, central limit theorem. 
      • Inferential Statistics –  Correlation, covariance, confidence intervals, hypothesis testing, F-test, Z-test, t-test, ANOVA, chi-square test, etc. 
  • Python And Data Analytics
      • Extract Transform Load – Web Scraping, Interacting with APIs 
      • Data Handling with NumPy
        • NumPy Arrays, CRUD Operations,etc. 
        • Linear Algebra – Matrix multiplication, CRUD operations, Inverse, Transpose, Rank, Determinant of a matrix, Scalars, Vectors, Matrices.
      • Data Manipulation Using Pandas – Loading the data, dataframes, series, CRUD operations, splitting the data, etc.
      • Data Preprocessing
        • Exploratory Data Analysis, Feature engineering, Feature scaling, Normalization, standardization, etc. 
        • Null Value Imputations, Outliers Analysis and Handling, VIF, Bias-variance trade-off, cross validation techniques, train-test split, etc.
      • Data Visualization
        • Bar charts, scatter plots, count plots, line plots, pie charts, donut charts, etc, with Python matplotlib.
        • Regression plots, categorical plots, area plots, etc, with Python seaborn
      • Introduction to Machine learning 
        • Supervised – What is supervised learning, what kind of data can be used for supervised learning, supervised learning models, supervised learning lifecycle, etc. 
        • Unsupervised learning – What is unsupervised learning, how is it different from supervised learning, unsupervised learning models, unsupervised learning approach and lifecycle. 
        • Introduction to scikit-learn, Keras, etc. 
      • Regression 
        • Introduction classification problems, Identification of a regression problem, dependent and independent variables. 
        • How to train the model in a regression problem. 
        • How to evaluate the model for a regression problem. 
        • How to optimize the efficiency of the regression model. 
      • Classification 
        • Introduction to classification problems, Identification of a classification problem, dependent and independent variables. 
        • Training  a model in a classification problem, how it is different from a regression problem, training with single and multiple features. 
        • Evaluating the accuracy of the classification models.  
      • Clustering 
        • Introduction to clustering problems, Identification of a clustering problem, dependent and independent variables. 
        • How the clustering problems are different from supervised learning and unsupervised learning.
        • How to train the model in a clustering problem. 
        • Evaluating the efficiency of the models created for clustering models. 
      • Supervised Learning 
        • Linear Regression – Creating linear regression models for linear data using statistical tests, data preprocessing, standardization, normalization, etc. 
        • 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. 
        • Decision Tree – Creating decision tree models on classification problems in a tree like format with optimal solutions.   
        • Random Forest – Creating random forest models for classification problems in a supervised learning approach. 
        • Support Vector Machine – SVM or support vector machines for regression and classification problems. 
        • Gradient Descent – Gradient descent algorithm that is an iterative optimization approach to finding local minimum and maximum of a given function. 
        • K-Nearest Neighbors – A simple algorithm that can be used for classification problems. 
        • Time Series Forecasting – Making use of time series data, gathering insights and useful forecasting solutions using time series forecasting. 
      • Unsupervised Learning 
        • K-means – The k-means algorithm that can be used for clustering problems in an unsupervised learning approach.
        • Dimensionality reduction – Handling multi dimensional data and standardizing the features for easier computation.
        • Linear Discriminant Analysis –  LDA or linear discriminant analysis to reduce or optimize the dimensions in the multidimensional data.
        • Principal Component Analysis – PCA follows the same approach in handling the multidimensional data.
      • Performance Metrics
        • Classification reports – To evaluate the model on various metrics like recall, precision, f-support, etc. 
        • Confusion matrix – To evaluate the true positive/negative, false positive/negative outcomes in the model. 
        • r2, adjusted r2, mean squared error, etc.
  • Excel Fundamentals 
      • Reading the Data, Referencing in formulas , Name Range, Logical Functions, Conditional Formatting, Advanced Validation, Dynamic Tables in Excel, Sorting and Filtering 
      • Working with Charts in Excel, Pivot Table, Dashboards, Data And File Security 
      • VBA Macros, Ranges and Worksheet in VBA 
      • IF conditions, loops, Debugging, etc.
  • Excel For Data Analytics 
      • 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
      • Charts, Pie charts, Scatter and bubble charts
      • Bar charts, Column charts, Line charts, Maps
      • Multiples: A set of charts with the same axes, Matrices, Cards, Tiles
  • Excel Power Tools 
      • Power Pivot, Power Query and Power View
  • Classification Problems using Excel
      • Binary Classification Problems, Confusion Matrix, AUC and ROC curve
      • Multiple Classification Problems  
  • Information Measure in Excel
      • Probability, Entropy, Dependence 
      • Mutual Information 
  • Regression Problems Using Excel
      • Standardization, Normalization, Probability Distributions 
      • Inferential Statistics, Hypothesis Testing, ANOVA, Covariance, Correlation
      • Linear Regression, Logistic Regression, Error in regression, Information Gain using Regression
  • Visualization Basics 
    • Introduction to Data Visualization
    • Various Plots for data Analysis 
    • Determining Which Visual Representation goes best with specific data – categorical, non-categorical plots. 
    • Finding relationships between various features in the data using visual representations. 
  • Visualization Tools
    • Introduction to various visualization tools
    • Creating dashboards using various visualization tools. 
    • Gathering insights from the visual representations.
  • 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
  • Introduction to Databases
      • What are databases?
      • Different types of databases, how to manage databases, etc.
  • SQL Basics 
      • Fundamentals of Structured Query Language
      • SQL Tables, Joins, Variables 
  • Advanced SQL   
      • SQL Functions, Subqueries, Rules, Views
      • Nested Queries, string functions, pattern matching
      • Mathematical functions, Date-time functions, etc. 
  • Deep Dive into User Defined Functions
      • Types of UDFs, Inline table value, multi-statement table. 
      • Stored procedures, rank function, triggers, etc. 
  • SQL Optimization and Performance
      • Record grouping, searching, sorting, etc. 
      • Clustered indexes, common table expressions.
  • Managing Database Concurrency
      • Implicit explicit transactions, Isolation levels management, concurrency and locking behavior. 
      • Memory optimized tables.
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Program Highlights

Live Sessions for a period of 7 - 10 weeks
25 Hrs of Self-Paced Learning
5 ECTS credits
24*7 Support

Interested in This Program? Secure your spot now.

The application is free and takes only 5 minutes to complete.


This Post Graduate diploma in Business Analytics has several industry-based projects that progress through beginner, intermediate and advanced levels to help you gain hands-on skills and experience.

Hear From Our Hiring Partners

Career Services By Intellipaat

Career Services

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.

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

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.

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.

Assured Interviews

After 80% of the course completion

Assured Interviews upon submission of projects and assignments. Get interviewed by our 500+ hiring partners.

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.

Our Alumni Works At

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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!


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.

Submit Application

Submit Application

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

Application Review

Application Review

An admission panel will shortlist candidates based on their application


Application Review

Selected candidates will be notified within 1–2 weeks

Program Fee

Total Admission Fee

$ 1,579

Upcoming Application Deadline 1st Apr 2023

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 1st Apr 2023 08:00 PM IST Weekend (Sat-Sun)
Regular Classes 1st Apr 2023 08:00 PM IST Weekend (Sat-Sun)

Frequently Asked Questions

Who are the instructors of this Post Graduate Diploma in Business Analytics and how are they selected?

The course instructors of this Post graduate diploma in Business Analytics are experts and leading academicians from SSBM Geneva and professionals at Intellipaat. 

Upon completion of the training and successful completion of the capstone project, you will receive a Post Graduate diploma in Business Analytics from SSBM.

This post graduate diploma in Business Analytics is conducted by leading experts from SSBM and Intellipaat who will assist you in kick-starting your career in the domain of Business Analytics 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 in Data  Analytics and Business Analytics, apart from providing hands-on experience in these domains through real-time projects.

If you fail to attend any of the live lectures, you will get the recorded session in the next 12 hours.

To register for the program, you can reach out to our course advisors.

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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

I’m Interested in This Program

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