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SSBM

Post Graduate Diploma in Business Analytics

6,377 Ratings

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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Learning Format

Online

Live Classes

7 Months

Career Services

by Intellipaat

SSBM Diploma

Certification

EMI Starts

at ₹8,000/month*

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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 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

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

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 quality education to students.
  • It is an ACBSP-accredited institution.
  • Ranked #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.
Post Graduate Diploma in Business Analytics Click to Zoom

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 apply

What roles can a business analysis professional play?

Business Analyst

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

Data Analyst

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

BI Developer

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

Data Scientist

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

Marketing and Sales Analyst

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

Data Architect

Creates 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

Querying

Databases

Machine learning

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Tools to Master

excel SQL 1 Power BI Azure ML python 2 jupyter 1 Power Query

Curriculum

Self Paced
  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.
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  • 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 Analytics? – Introduction to data analytics, data analytics life cycle, steps involved in data analytics, the 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.
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  • Linear Algebra and Advanced Statistics 
      • Descriptive Statistics – Measure of central tendency, the 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 is an iterative optimization approach to finding the 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  multidimensional 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 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.
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  • 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
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  • 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.
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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
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  • 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-Define 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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Disclaimer
Intellipaat reserves the right to modify, amend or change the structure of module & the curriculum, after due consensus with the university/certification partner.

Program Highlights

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

Projects

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.

Write Complex Sub-queries

The project aims at helping you work with SQL subqueries and also use them in numerous situations. The various queries to be used and covered as a part of the project are - IN or NOT IN, EXISTS or NOT EXISTS, ANY or ALL, etc.

Query a Large Relational Database

This interesting project on relational databases allows the learners to work with various data types, table basics, SQL functions, and SQL operators to acquire customer details through database querying.

Calculation of Employee Bonuses

The comprehensive project lets the learners use the ‘IF’ function to calculate the bonuses of all the employees at 10% of their salary and rate the sales employees as per the respective sales figures and rating scale.

Deal with Financial Data

The project is included to provide learners with hands-on experience in financial data analysis. Analyze global sales numbers and profit data by developing an interactive map, and using map styles and layers for enhanced visualization.

Work with Agricultural Data

As a part of the project, learners will have to display district-wise data to successfully build an interactive treemap with state labels. Also, the districts must be displayed by hovering over the respective states.

Design a Relational Database

As a part of the project, learn to successfully design a relational database and convert relational design, consisting of the details of the various users, user accounts and user roles, etc., in the SQL server table.

Planning Poker

Each estimator gets a deck of cards and each card has a valid estimate written on it. Each estimator selects a card as an estimate and the cards are turned over to discuss the differences and re-estimate until estimates converge.

Identify the Global Mortality Rate due to COVID-19

The project aims at identifying the mortality rate of COVID-19 at a global level. Analyze and create a dashboard to gain insights into the COVID-19 cases. Use a world map to compare the confirmed cases and death cases worldwide.

Work with the Customer Data of the UK Bank

The project has been included to let learners analyze and create a dashboard to gain insight from the UK bank’s customer data. Build an asymmetric drop-down consisting of the region and the corresponding user details.

Career Services By Intellipaat

Career Services
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Placement Assistance
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Mock Interview Preparation
1 on 1 Career Mentoring Sessions
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Resume & LinkedIn Profile Building
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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

₹ 1,10,010 (Inclusive of All)

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EMI Starts at

₹ 8,000

We partnered with financing companies to provide competitive finance options at 0% interest rate with no hidden costs

Financing Partners

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The credit facility is provided by a third-party credit facility provider and any arrangement with such third party is outside Intellipaat’s purview.

Upcoming Application Deadline 14th Dec 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 14th Dec 2024 08:00 PM - 11:00 PM IST Weekend (Sat-Sun)
Regular Classes 14th Dec 2024 08:00 PM - 11:00 PM IST Weekend (Sat-Sun)
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Frequently Asked Questions

Who are the instructors of this post graduate diploma in business nalytics 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 draduate 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

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