Kickstart your international journey with FOM University, Germany

M.Sc in Big Data & Business Analytics

1st Year in India, 2nd Year in Germany

Program Accredited by

& Global Certifications by in

  • Data Science
  • Artificial Intelligence
  • Data Analytics

Leading to a Masters in Data Science, Artificial Intelligence & Business Analytics

Admissions Open

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Duration

2 Years + Full Time

Batches

Sep - Nov

Program Overview

  • The Master programme — Big Data & Business Analytics is aimed at (economic) computer scientists or natural scientists, such as mathematicians and statisticians or students from Engineering background.
  • The programme builds on the knowledge and competences of the first-degree course.
  • Big Data specialists span the spectrum from logic and quantitative methods through programming languages, frameworks and infrastructures to the interpretation and implementation of the results in corporate processes.
  • Unlike other institutes, Vepsun provides plenty of opportunities to students so they can be well prepared to analyse and interpret large amounts of data and to derive recommendations from them for companies.
  • The students are prepared to analyse data mathematically or statistically and then evaluate it against a business background.
  • At Vepsun, Big Data students are trained to specialise in specialist and management tasks at the interface between IT, management and control.
  • Upon completion of the programme, the students will be able to obtain positions like Big Data Manager, Big Data Analyst, Product Manager of Data Integration, Market Data Analyst, or Data Scientist in renowned companies.

Features

Key Highlights

fom highlights

4th

Rank in Germany
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FIBAA

Accreditation
 
fom highlights

1000+

Corporate Connection
 
fom highlights

German

Language Certification
 
fom highlights

55K+

Students
 
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33+

Centers in Germany
 
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2100+

Faculty
 
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-Azure, AI, Data Science & Data Analytics Certification included

ABOUT FOM UNIVERSITY

  • With more than 55,000 students, FOM University is ranked in 4th position among 420 universities of applied sciences and universities in Germany.
  • It is also Germany's largest private university.
  • They have a total number of 33 Higher education centres in Germany..
  • More than 2100 professors and lecturers are there to provide world-class education..
  • The university has been state-recognized since 1993.
  • It is known as the Best Practice University of the German UNESCO Commission in the UN Decade of Education for Sustainable Development at universities.
  • They have 37 cooperation colleges worldwide.
  • Carrier of the largest European study project in China.
  • Over 1000 corporate collaborations in Germany, including Allianz, AOK, Bertelsmann, BP, Deutsche Telekom, Ford, IBM, City of Munich, Peek & Cloppenburg Vienna, Siemens, thyssenkrupp.
FOM

About VEPSUN

The fast pace of innovation and business today requests a learning approach that fits the necessities of both the individual and the organization. We built a learning system to reflect that need. Adapting today requires a guided methodology through the intricate number of formal and casual learning alternatives. It requires a methodology that envelops the top learning techniques utilized today and adjusts them to help hierarchical results.

Our learning ecosystem is designed to support how learning is done today and evolves to meet advances in technology and individual learning needs. Integrating the world’s largest collection of proprietary and IT partner content, resources, and expertise with a global instructor pool of more than 300 real-world experts, Vepsun Technologies delivers custom learning to global organizations no matter where their workforce is located to drive quantifiable results.

  • Designed for Working Professionals/Students
  • Instructor-led Sessions
  • Dedicated Student Success Manager
  • Real-life Case Studies
  • Lifetime Access
  • 1-on-1 Industry Mentor
  • Career Assist
  • Assignments
  • Certification

ACCREDITATION

fibaa

FOM became the first private university in Germany to be accredited by the FIBAA (Foundation for International Business Administration Accreditation) the agency for Quality assurance in the university sector. This shows that FOM has extremely well-managed internal quality management system and satisfies the highest international standards.

un

FOM has been titled as the Best Practice University of the German UNESCO Commission in the UN Decade of Education for Sustainable Development at universities.

accreditation

WR Institutional Accreditation by Science Council .FOM is the first university in North-Rhine Westphalia to receive a quality seal from the German Council of Science and Humanities in 2004.

accreditation

EKS The Accreditation procedure is conducted by the evolation committee for cources of study (EKS) as an external body.

accreditation

System Accreditation The Foundation for International Business Administration Accreditation has repeatedly confirmed the academic quality and professional relevance of FOM's Bachelor and Master's Degree Programmes.

Syllabus

M.Sc in Big Data & Business Analytics

Big Data Architecture & Infrastructure

Credit Points: 5

  • Enterprise Architecture Management (EAM)
  • Technological requirements for Big Data
  • Vital infrastructures for data-driven business models
  • Complex processing by continuing
  • Data Categories

Leadership and Sustainability

Credit Points: 6

  • Leadership as part of the normative, strategic and operational business management and in context of diversity management
  • Leadership styles, techniques and instruments Ethics and sustainability

Information Security

Credit Points: 6

  • Foundations : Security mindsets,Essential concepts
  • Software Security: Vulnerabilies & protections, Malware,Pragram Analysis
  • Practical Cryptography: Encryption Authentication, Hashing,Symmetric and Asymmetric Cryptography
  • Networks : Wired and Wireless Network protocols, attacks and counter measures

Decision focussed management

Credit Points: 6

  • Classical Decision Making
  • Management decisions from psychological view
  • Decisions in the strategy context

Big Data Analytics

Credit Points: 6

  • Data sources and data categorization
  • Visual Analytics / Data Discovery / Exploratory data analysis
  • AI methods such as machine learning
  • Computational Intelligence: fuzzy logic, neural
  • Networks, Evolutionary Algorithms

Applied programming

Credit Points: 5

  • Basics and application of Programming languages for Big data: SQL, R and Python
  • Languages & tools for data management
  • Data integration
  • ETL vs. ELT (Data Lake)

Project Management of big data projects

Credit Points: 6

  • Planning, management and control of dig data projects
  • Challenges, Special Features & Success factors in the management of Big Data projects
  • Architectural and technological features
  • Introduction of big data applications
  • Integration and harmonization of data sources and planning of data analysis and reporting

Analysis of semi- & unstructured data

Credit Points: 6

  • Crawling and preprocessing
  • Text Mining / Web Mining
  • Social media analysis
  • Ontologies
  • Semantic and graphic
  • Modeling / technologies

Area of Application : Business Analytics

Credit Points: 6

  • Goals and responsibilities for Big Data applications
  • Sector and type of data sources
  • Application of procedures such as association analysis, Decision tree procedure, neural networks, cluster analysis

Ethics & Law

Credit Points: 6

  • Ethical aspects of using big data
  • Legal aspects of big data usage
  • Compliance

Deutsch

Credit Points: 6

  • Fundamentals in listening, reading, writing and speaking
  • Basic grammatical skills
  • Application in situations of everyday life

Big Data Consulting Project

Credit Points: 5

  • Selection of an application field for the analysis project
  • Data storytelling
  • Addressing a management question
  • Data acquisition, processing, & analysis
  • Preparation of the insights for the management

Big Data Analysis Project

Credit Points: 6

  • Selection of an application field for the analysis project
  • Project work with first completely own data analysis

Research Methods

Credit Points: 6

  • Foundation of research and Scientific Methods, Understanding the language of Research
  • Problem identification and formulation,Hypothesis,Hypothesis Testing, Logic and Importance
  • Research Design: Features of a good research design, exploratory & Discriptive research designs
  • Qualitative and Quantitative Research
  • Data Analysis: Data Preparation, Analysis and Testing
  • Interpretation of Data
  • Use of Tools/Techniques for Research

Strategic business model development

Credit Points: 6

  • Foundation of Business Strategy, Role of Tasks of a Strategy Manager
  • Formulating a strategy and its implementation
  • Tools and Techniques for Situational Analysis
  • Methods for implementing a good business judgement and making sound decisions
  • Results of big data analysis as a driver for the Business Model Development
  • Planning the Big Data strategy / business analytics strategy
  • Strategic approaches and strategic planning and management tools
  • Data-based business models and business transformation
  • Open Innovation / Innovation Management

Applied Project

Credit Points: 6

Master-Thesis, Colloquium

Credit Points: 25

    Applied Project

    Credit Points: 6

      Masters in Data Science, Artificial Intelligence & Business Analytics

      INTRODUCTION TO PYTHON

      Build a foundation for the most in-demand programming language of the 21st century.

      • Understanding the Jupyter Anconda,Coding Console
      • Data Structures in Python
      • Control Structure and Functions
      PYTHON FOR DATA SCIENCE

      Learn how to manipulate datasets in Python using Pandas which is the most powerful library for data preparation and analysis.

      • Introduction to NumPy
      • Operations on NumPy Arrays
      • Introduction to Pandas Getting and Cleaning Data
      VISUALIZATION IN PYTHON

      Humans are visual learners and hence no task related to data is complete without visualisation. Learn to plot and interpret various graphs in Python and observe how they make data analysis and drawing insights easier.

      • Introduction to Data Visualization
      • Basics of Visualization: Plots, Subplots and their Functionalities Plotting Data Distributions Plotting Categorical and Time-Series Data
      EXPLORATORY DATA ANALYSIS

      Learn how to find and analyze the patterns in the data to draw actionable insights.

      • Data Sourcing
      • Data Cleaning
      • Univariate Analysis
      • Segmented Univariate
      • Bivariate Analysis
      • Derived Metrics
      R Programming:

      a) Reading and Getting Data into R

      b) Data Objects-Data Types & Data Structure.

      c) Viewing Named Objects, Structure of Data Items, Manipulating and Processing Data in R (Creating, Accessing, Sorting data frames, Extracting, Combining, Merging, reshaping data frames)

      d) Control Structures, Functions in R (numeric, character, statistical)

      e) working with objects, Viewing Objects within Objects, Constructing Data Objects, Packages – Tidyverse, Dplyr, Tidyr etc., Queuing Theory,

      f) Non parametric Tests- ANOVA, chi-Square, t-Test, U-Test

      g) Interactive reporting with R markdown

      INVESTMENT ASSIGNMENT

      Learners will fill in the shoes of an analyst at an investment bank and determine where the firm should invest. They will then have to explain their recommendations in lieu of the analysis conducted

      • Problem Statement
      • Evaluation Rubric
      • Final Submission
      • Solution
      INFERENTIAL STATISTICS

      Build a strong statistical foundation and learn how to ‘infer’ insights from a huge population using a small sample.

      • Basics of Probability
      • Discrete Probability Distributions
      • Continuous Probability Distributions
      • Central Limit Theorem
      HYPOTHESIS TESTING

      Understand how to formulate and validate hypotheses for a population to solve real-life business problems.

      • Concepts of Hypothesis Testing - I: Null and Alternate Hypothesis, Making a Decision, and Critical Value Method.
      • Concepts of Hypothesis Testing - II: p-Value Method and Types of Errors
      • Industry Demonstration of Hypothesis Testing: Two-Sample Mean and Proportion Test, A/B Testing.
      l
      LINEAR REGRESSION

      Venture into the machine learning community by learning how one variable can be predicted using several other variables through a housing dataset where you will predict the prices of houses based on various factors.

      • Introduction to Simple Linear Regression
      • Simple Linear Regression in Python
      • Multiple Linear Regression
      • Multiple Linear Regression in Python
      • Industry Relevance of Linear Regression
      LOGISTIC REGRESSION

      Learn your first binary classification technique by determining which customers of a telecom operator are likely to churn versus who are not to help the business retain customers.

      • Univariate Logistic Regression
      • Multivariate Logistic Regression - Model Building
      • Multivariate Logistic Regression - Model Evaluation
      • Logistic Regression - Industry Applications
      NAIVE BAYES

      Understand the basic building blocks of Naive Bayes and learn how to build an SMS Spam Ham Classifier using Naive Bayes technique.

      • Bayes Theorem and Its Building Blocks
      • Naive Bayes For Categorical Data
      • Naive Bayes for Text Classification
      MODEL SELECTION

      Learn the pros and cons of simple and complex models and the different methods for quantifying model complexity, along with regularisation and cross validation.

      • Principles of Model Selection
      • Model Evaluation
      SUPPORT VECTOR MACHINE (OPTIONAL)

      Learn how to find a maximal marginal classifier using SVM, and use them to detect spam emails, recognize alphabets and more!

      • SVM - Maximal Margin Classifier
      • SVM - Soft Margin Classifier
      • Kernels
      TREE MODELS

      Learn how the human decision-making process can be replicated using a decision tree and other powerful ensemble algorithms.

      • Introduction to Decision Trees
      • Algorithms for Decision Tree
      • Construction Truncation and Pruning
      • Random Forests
      BOOSTING

      Learn how weak learners can be ‘boosted’ with the help of each other and become strong learners using different boosting algorithms such as Adaboost, GBM, and XGBoost.

      • Introduction to Boosting and AdaBoost
      • Gradient Boosting
      UNSUPERVISED LEARNING: CLUSTERING

      Learn how to group elements into different clusters when you don’t have any pre-defined labels to segregate them through K-means clustering, hierarchical clustering, and more.

      • Introduction to Clustering
      • K Means Clustering
      • Executing K Means in Python
      • Hierarchical Clustering
      • Other Forms of Clustering
      TELECOM CHURN CASE STUDY

      Solve the most crucial business problem for a leading telecom operator in India and southeast Asia - predicting customer churn

      • Problem Statement
      • Evaluation
      • Rubric
      • Final Submission
      • Solution
      LEXICAL PROCESSING

      Do you get annoyed by the constant spams in your mail box? Wouldn’t it be nice if we had a program to check your spellings? In this module learn how to build a spell checker & spam detector using techniques like phonetic hashing, bag-of-words, TF-IDF, etc.

      • Introduction to NLP
      • Basic Lexical Processing
      • Advanced Lexical Processing
      SYNTACTIC PROCESSING

      Learn how to analyze the syntax or the grammatical structure of sentences with the help of algorithms & techniques like HMMs, Viterbi Algorithm, Named Entity Recognition (NER), etc.

      • Introduction to Syntactic Processing
      • Parsing
      • Information Extraction
      • Conditional Random Fields
      SYNTACTIC PROCESSING -ASSIGNMENT

      Build a POS tagger for tagging unknown words using HMMs and modified Viterbi algorithm

      • Problem Statement
      • Evaluation
      • Rubric
      • Final Submission
      • Solution
      SEMANTIC PROCESSING

      Learn the most interesting area in the field of NLP and understand different techniques like word-embeddings, LSA, topic modeling to build an application that extracts opinions about socially relevant issues (such as demonetization) on social media platforms.

      • Introduction to Semantic Processing
      • Distributional Semantics Topic Modelling
      • Social Media Opinion Mining
      • Semantic Processing
      • Case Study
      BUILDING CHATBOTS WITH RASA

      Imagine if you could make restaurant booking without opening Zomato. Build your own restaurant-search chatbot with the help of RASA - an open source framework and deploy it on Slack.

      • Problem Statement
      • Evaluation
      • Rubric
      • Final Submission
      • Solution
      INTRODUCTION TO NEURAL NETWORKS

      Learn the most sophisticated and cutting-edge technique in machine learning - Artificial Neural Networks or ANNs.

      • Structure of Neural Networks
      • Feed Forward in Neural Networks
      • Backpropagation in Neural Networks
      • Modifications to Neural Networks
      • Hyperparameter Tuning in Neural Networks
      NEURAL NETWORKS - ASSIGNMENT

      Build a neural network from scratch in Numpy to identify the type of skin cancer from images.

      • Problem Statement
      • Evaluation
      • Rubric
      • Final Submission
      • Solution
      CONVOLUTIONAL NEURAL NETWORKS -INDUSTRY APPLICATIONS

      Learn the basics of CNN and OpenCV and apply it to Computer Vision tasks like detecting anomalies in chest X-Ray scans, vehicle detection to count and categorise them to help the government ascertain the width and strength of the road.

      • Building CNNs with Python and Keras
      • CNN Architectures and Transfer Learing
      • Style Transfer and Object Detection Industry
      • Demo:Using CNNs with Flowers Images Industry
      • Demo:Using CNNs with X-ray Images
      CREATE MACHINE LEARNING MODELS

      CREATE MACHINE LEARNING MODELS

      • Explore and analyze data with Python
      • Train and evaluate machine learning models
      • Train and evaluate regression models
      • Train and evaluate classification models
      • Train and evaluate clustering models
      • Train and evaluate deep learning models
      CREATE NO-CODE PREDICTIVE MODELS WITH AZURE MACHINE LEARNING
      • Use Automated machine learning in Azure Machine Learning
      • Creating a regression model with Azure Machine Learning designer
      • Creating a classification model with Azure Machine Learning designer
      • Creating a clustering model with Azure Machine Learning designer
      BUILD AI SOLUTIONS WITH AZURE MACHINE LEARNING

      • Introduction to Azure machine learning SDK
      • Train a machine learning model with Azure Machine Learning
      • Work with Data in Azure Machine Learning
      • Work with Compute in Azure Machine Learning
      • Orchestrate machine learning with pipelines
      • Deploy real-time machine learning services with Azure Machine Learning
      • Deploy batch inference pipelines with Azure Machine Learning
      • Tune hyper parameters with Azure Machine Learning
      • Automate machine learning model selection with Azure Machine Learning
      • Explore differential privacy
      • Explain machine learning models with azure machine learning
      • Detect and mitigate unfairness in models with azure machine learning
      • Monitor models with azure machine learning
      • Monitor data drift with azure machine learning.
      Introduction

      Big Data - Beyond The Hype, Big Data Skills And Sources Of Big Data, Big Data Adoption, Research And Changing Nature Of Data Repositories, Data Sharing And Reuse Practices And Their Implications For Repository Data Curation, Overlooked And Overrated Data Sharing, Data Curation Services In Action, Open Exit: Reaching The End Of The Data Life Cycle, The Current State Of Meta-Repositories For Data, Curation Of Scientific Data At Risk Of Loss: Data Rescue And Dissemination

      Hadoop:

      Introduction of Big data programming-Hadoop, The ecosystem and stack, The Hadoop Distributed File System (HDFS), Components of Hadoop, Design of HDFS, Java interfaces to HDFS, Architecture overview, Development Environment, Hadoop distribution and basic commands, Eclipse development, The HDFS command line and web interfaces, The HDFS Java API (lab), Analyzing the Data with Hadoop, Scaling Out, Hadoop event stream processing, complex event processing, MapReduce Introduction, Developing a Map Reduce Application, How Map Reduce Works, The MapReduce Anatomy of a Map Reduce Job run, Failures, Job Scheduling, Shuffle and Sort, Task execution, Map Reduce Types and Formats, Map Reduce Features, Real-World MapReduce,

      Hadoop Environment:

      Setting up a Hadoop Cluster, Cluster specification, Cluster Setup and Installation, Hadoop Configuration, Security in Hadoop, Administering Hadoop, HDFS – Monitoring & Maintenance, Hadoop benchmarks,

      Introduction to HIVE,

      Programming with Hive: Data warehouse system for Hadoop, Optimizing with Combiners and Practitioners (lab), Bucketing, more common algorithms: sorting, indexing and searching (lab), Relational manipulation: map-side and reduce-side joins (lab), evolution, purpose and use, Case Studies on Ingestion and warehousing

      HBase:

      Overview, comparison and architecture, java client API, CRUD operations and security

      Apache Spark APIs for large-scale data processing:

      Overview, Linking with Spark, Initializing Spark, Resilient Distributed Datasets (RDDs), External Datasets, RDD Operations, Passing Functions to Spark, Job optimization, Working with Key-Value Pairs, Shuffle operations, RDD Persistence, Removing Data, Shared Variables, EDA using PySpark, Deploying to a Cluster Spark Streaming, Spark MLlib and ML APIs, Spark Data Frames/Spark SQL, Integration of Spark and Kafka, Setting up Kafka Producer and Consumer, Kafka Connect API, Mapreduce, Connecting DB’s with Spark

      Data warehousing Concepts

      Understand how to formulate and validate hypotheses for a population to solve real-life business problems.

      • What is DWH?
      • Characteristics of Datawarehouse
      • Difference between OLTP and DWH
      • Architecture of DWH
      • Various BI tools
      • Types of DWH
      • Types of Dimensional Data Modeling
      • Surrogate key
      • Types of Dimension
      Tableau Desktop (Introduction)
      • Introduction Tableau
      • Connecting to Excel, CSV Text Files
      • Getting Started
      • Product Overview
      • Connecting to Databases
      • Working with Data
      • Analyzing
      • Formatting
      • Introduction to Calculations
      • Dashboard Development
      • Sharing
      • Data Calculations
      • Aggregate Calculations
      • User Calculations
      • Table Calculations
      • Logical Calculations
      • String Calculations
      • Number Calculations
      • Type Conversion
      • Parameters
      • Filtering Conditions
      • Filtering Measures
      • Histograms
      • Sorting
      • Grouping
      • Sets
      • Tree maps, word clouds and bubble charts
      • Pareto Charts
      • Waterfall Charts
      • Bump Charts
      • Funnel Charts
      • Bollinger Bands
      Module 1: Get Started with Microsoft Data Analytics

      This module explores the different roles in the data space, outlines the important roles and responsibilities of a Data Analysts, and then explores the landscape of the Power BI portfolio.

      Lessons
      • Data Analytics and Microsoft
      • Getting Started with Power BI
      Lab : Getting Started
      • Getting Started

      After completing this module, you will be able to:

      • Explore the different roles in data
      • Identify the tasks that are performed by a data analyst
      • Describe the Power BI landscape of products and services
      • Use the Power BI service
      Module 2: Prepare Data in Power BI

      This module explores identifying and retrieving data from various data sources. You will also learn the options for connectivity and data storage, and understand the difference and performance implications of connecting directly to data vs. importing it.

      Lessons
      • Get data from various data sources
      • Optimize performance
      • Resolve data errors
      Lab : Preparing Data in Power BI Desktop
      • Prepare Data

      After completing this module, you will be able to:

      • Identify and retrieve data from different data sources
      • Understand the connection methods and their performance implications
      • Optimize query performance
      • Resolve data import errors
      Module 3: Clean, Transform, and Load Data in Power BI

      This module teaches you the process of profiling and understanding the condition of the data. They will learn how to identify anomalies, look at the size and shape of their data, and perform the proper data cleaning and transforming steps to prepare the data for loading into the model.

      Lessons
      • Data shaping
      • Enhance the data structure
      • Data Profiling
      Lab : Transforming and Loading Data
      • Loading Data

      After completing this module, students will be able to:

      • Apply data shape transformations
      • Enhance the structure of the data
      • Profile and examine the data
      Module 4: Design a Data Model in Power BI

      This module teaches the fundamental concepts of designing and developing a data model for proper performance and scalability. This module will also help you understand and tackle many of the common data modeling issues, including relationships, security, and performance.

      Lessons
      • Introduction to data modeling
      • Working with tables
      • Dimensions and Hierarchies
      Lab : Data Modeling in Power BI Desktop
      • Create Model Relationships
      • Configure Tables
      • Review the model interface
      • Create Quick Measures
      Lab : Data Modeling in Power BI Desktop
      • Create Model Relationships
      • Configure Tables
      • Review the model interface
      • Create Quick Measures
      Lab : Advanced Data Modeling in Power BI Desktop
      • Configure many-to-many relationships
      • Enforce row-level security

      After completing this module, you will be able to:

      • Understand the basics of data modeling
      • Define relationships and their cardinality
      • Implement Dimensions and Hierarchies
      • Create histograms and rankings
      Module 5: Create Measures using DAX in Power BI

      This module introduces you to the world of DAX and its true power for enhancing a model. You will learn about aggregations and the concepts of Measures, calculated columns and tables, and Time Intelligence functions to solve calculation and data analysis problems.

      Lessons
      • Introduction to DAX
      • DAX context
      • Advanced DAX
      Lab : Introduction to DAX in Power BI Desktop
      • Create calculated tables
      • Create calculated columns
      • Create measures
      Lab : Advanced DAX in Power BI Desktop
      • Use the CALCULATE() function to manipulate filter context
      • use Time Intelligence functions

      After completing this module, you will be able to:

      • Understand DAX
      • Use DAX for simple formulas and expressions
      • Create calculated tables and measures
      • Build simple measures
      • Work with Time Intelligence and Key Performance Indicators
      Module 6: Optimize Model Performance

      In this module you are introduced to steps, processes, concepts, and data modeling best practices necessary to optimize a data model for enterprise-level performance.

      Lessons
      • Optimze the model for performance
      • Optimize DirectQuery Models
      • Create and manage Aggregations

      After completing this module, you will be able to:

      • Understand the importance of variables
      • Enhance the data model
      • Optimize the storage model
      • Implement aggregations
      Module 7: Create Reports

      This module introduces you to the fundamental concepts and principles of designing and building a report, including selecting the correct visuals, designing a page layout, and applying basic but critical functionality. The important topic of designing for accessibility is also covered.

      Lessons
      • Design a report
      • Enhance the report
      Lab : Designing a report in Power BI
      • Create a live connection in Power BI Desktop
      • Design a report
      • Configure visual fields adn format properties
      Lab : Enhancing Power BI reports with interaction and formatting
      • Create and configure Sync Slicers
      • Create a drillthrough page
      • Apply conditional formatting
      • Create and use Bookmarks

      After completing this module, you will be able to:

      • Design a report page layout
      • Select and add effective visualizations
      • Add basic report functionality
      • Add report navigation and interactions
      • Improve report performance
      • Design for accessibility
      Module 8: Create Dashboards

      In this module you will learn how to tell a compelling story through the use of dashboards and the different navigation tools available to provide navigation. You will be introduced to features and functionality and how to enhance dashboards for usability and insights.

      Lessons
      • Create a Dashboard
      • Real-time Dashboards
      • Enhance a Dashboard
      Lab : Designing a report in Power BI Desktop - Part 1
      • Create a Dashboard
      • Pin visuals to a Dashboard
      • Configure a Dashboard tile alert
      • Use Q&A to create a dashboard tile

      After completing this module, students will be able to:

      • Create a Dashboard
      • Understand real-time Dashboards
      • Enhance Dashboard usability
      Module 9: Create Paginated Reports in Power BI

      This module will teach you about paginated reports, including what they are how they fit into Power BI. You will then learn how to build and publish a report.

      Lessons
      • Paginated report overview
      • Create Paginated reports
      Lab : Creating a Paginated report
      • Use Power BI Report Builder
      • Design a multi-page report layout
      • Define a data source
      • Define a dataset
      • Create a report parameter
      • Export a report to PDF

      After completing this module, you will be able to:

      • Explain paginated reports
      • Create a paginated report
      • Create and configure a data source and dataset
      • Work with charts and tables
      • Publish a report
      Module 10: Perform Advanced Analytics

      This module helps you apply additional features to enhance the report for analytical insights in the data, equipping you with the steps to use the report for actual data analysis. You will also perform advanced analytics using AI visuals on the report for even deeper and meaningful data insights.

      Lessons
      • Advanced Analytics
      • Data Insights through AI visuals
      Lab : Data Analysis in Power BI Desktop
      • Create animated scatter charts
      • Use teh visual to forecast values
      • Work with Decomposition Tree visual
      • Work with the Key Influencers visual

      After completing this module, you will be able to:

      • Explore statistical summary
      • Use the Analyze feature
      • Identify outliers in data
      • Conduct time-series analysis
      • Use the AI visuals
      • Use the Advanced Analytics custom visual
      Module 11: Create and Manage Workspaces

      This module will introduce you to Workspaces, including how to create and manage them. You will also learn how to share content, including reports and dashboards, and then learn how to distribute an App.

      Lessons
      • Creating Workspaces
      • Sharing and Managing Assets
      Lab : Publishing and Sharing Power BI Content
      • Map security principals to dataset roles
      • Share a dashboard
      • Publish an App

      After completing this module, you will be able to:

      • Create and manage a workspace
      • Understand workspace collaboration
      • Monitor workspace usage and performance
      • Distribute an App
      Module 12: Manage Datasets in Power BI

      In this module you will learn the concepts of managing Power BI assets, including datasets and workspaces. You will also publish datasets to the Power BI service, then refresh and secure them.

      Lessons
      • Parameters
      • Datasets

      After completing this module, you will be able to:

      • Create and work with parameters
      • Manage datasets
      • Configure dataset refresh
      • Troubleshoot gateway connectivity
      Module 13: Row-level security

      This module teaches you the steps for implementing and configuring security in Power BI to secure Power BI assets.

      Lessons
      • Security in Power BI

      After completing this module, you will be able to:

      • Understand the aspects of Power BI security
      • Configure row-level security roles and group memberships

      Certifications

      Masters in Data Science, Artificial Intelligence & Business Analytics certified by Microsoft.

      certificate

      Certification by Vepsun

      certificate

      Certification by Microsoft

      certificate

      Certification by Microsoft

      certificate

      Certification by Microsoft

      Admission Process

      FOM Career Assistance

      myFOM

      FOM University provides an excellent opportunity to their students for better coordination of studies and training. The university also has their personal portal named myFOM, which specifically focusses on nearly 1000 cooperation partners of the FOM University.

      • myFOM is the service-oriented communication channel. Training and HR managers can receive insights into the current status of the FOM students’ studies through myFOM - as long as the students give their consent.
      • The student overview also provides information about examination regulations, performance records, course and semester plan as well as grades and credit points of the students. The data can be exported to Excel for offline work.
      • Companies willing to advertise jobs in coodination with a FOM degree program can quickly create ads and post them on the FOM's website.

      Salary Ranges

      Head of Data Science
      €90000
      Data Architect
      €79000
      Senior Data Engineer
      €74000
      Senior Data Scientist
      €73000
      Lead Data Scientist
      €71000
      Senior Software Engineer
      €59000
      Machine Learning Engineer
      €57000
      Data Engineer
      €57000

      Source: payscale.com

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      Testimonials

      Program Fee

      M.Sc in Big Data & Business Analytics

      FOM University, Germany1st Year in India, 2nd Year in Germany

      • Global Certifications by Microsoft in Data Science, Artificial Intelligence & Data Analytics
      • German Language Certification included
      • Easy Loan Facilitation
      Program Fee for 2 years

      INR. 13,00,000

      Frequently Asked Questions

      Why should I study at Vepsun?

      A. Well, you must be wondering what is special about Vepsun Technologies that makes it exceptional.
      So, here are the top reasons –

      • Vepsun offers various unique courses which have global prospect.
      • Students learn different foreign languages as part of the curriculum.
      • Our students get opportunities to do their internship abroad.
      • Dual Specializations for all the programme.

      Why should I study at FOM University?

      • Offering professionals the chance to study in the 4th ranked university in applied sciences and also one of the largest private university in Germany
      • Courses taught by faculty with International exposure
      • Industrial visits, projects, and events would be part of the programme
      • Placement assistance
      • FOM University is accredited by the FIBBA, Institutional accreditation by the Science Council

      How is the German Higher Education System?

      • World-class education by highly qualified staff.
      • Hundreds of academic courses to choose from.
      • The medium of communication in both English and German.
      • Endless opportunities to work in Germany after graduation.

      Why should I study Big Data and Business Analytics?

      • Data Science and Business Analytics are at the core of every modern globalized industry. Working in today’s technology-centric workforce not only requires superior leadership skills, but also the ability to translate data problems into the bigger picture for the organization.
      • Data Science and Business Analytics are no longer just buzzwords – they are essential business tools. Every day, 2.5 quintillion bytes of data are created, and international Data Corporation estimates that by 2020 the Big Data Analytics market, just one slice of the larger Data Science and Analytics market – will grow to over USD 187 billion. Big Data and Business Analytics have a great scope of usage across industry verticals globally.
      • Organizations using Big data and Business Analytics for their data-driven decision making are more productive and profitable than their competitors.

      What are the eligibility criteria?

      • Bachelor’s degree in Science or Engineering from any recognized University
      • Should have studied Mathematics or Statistics as a subject during their graduation and should have an inclination for number crunching.

      How many admission intakes are there in each academic year?

      A. The first intake starts in September and the second intake is in November for this academic year.

      Why should I consider Germany for my higher studies?

      A. Germany can be easily considered as the ideal destination for all the international students

      • Germany is rated World’s third most attractive study destination.
      • Top ranked universities
      • Better career prospect
      • Work permit for international students
      • Affordable cost of living
      • Explore the 22 European countries with your student visa
      • Part-time work opportunity for 20 hours in a week.
      • After completion, you will get 18 months of Visa extension

      Why is Germany better than any other country?

      • From the snow-covered peaks of the Alps in the south to the pristine sands of the beaches in the north, Germany has more breathtaking natural beauty than other EU countries.
      • The cost of living in Germany is quite reasonable compared to other European countries.
      • More than any other country, Germany has more a traditional top globally ranked university.
      • According to German law, international students are allowed to work part-time for up to 20 hours per week or 120 full days of a year. Data shows that more than 60% of current international students work part-time during study tenure in Germany.
      • It is easier to get a job in Germany than any other EU country, thanks to their booming economy.

      Is it compulsory to learn the German language?

      A. It is not compulsory to learn the German language as the medium of instruction in most of the German institutes and universities is English. Although German is the third most spoken foreign language in the world. But we, at Vepsun, provide German language training to our students – A1 and A2 levels for their proficiency.

      What is the cost of living in Germany?

      • The cost of living in Germany is quite reasonable compared to other European countries. You will need around 853 euros a month (around $957 US dollars) to cover your living expenses in Germany or 10,236 euros per year (around $11,484).
      • The prices for food, accommodation, bills, clothes, and entertainment are basically in line with the EU average. Your monthly rent is your largest expense in Germany.

      How to finance your study?

      A. If you are not able to pay the fees by yourself, then you can take an education loan to finance your study. In that case, we have a dedicated loan department to assist you.

      Do I need any particular skill to get a part-time job in Germany?

      A. No, you don’t need any particular skill to get a job in Germany. The unskilled workforce can earn up to 10 euros per hour. And, if you have experience in a certain field, then it is a plus for you.

      How much amount do I need to deposit in a Blocked Bank Account?

      A.From January 2020, the annual requirement of the amount that must be paid into the blocked account when applying for a visa is 10,236 Euros. This increased amount applies to all the German visa applications submitted from September 1, 2019, onwards

      What are the requirements for Visa?

      A. It is advisable to contact the German Embassy or Consulate General in your country of residence to find out what documents are required. Generally, you will have to submit the following:

      • Antrag auf Erteilung eines Visums für die Bundesrepublik Deutschland (Application for a visa for the Federal Republic of Germany, available at your German agency abroad or on the website of the Federal Foreign Office)
      • Valid passport
      • Cover letter
      • Bonafide certificate
      • Transcript – Graduation
      • Admission Letter from FOM
      • IELTS exemption letter
      • Semester – I Transcript
      • Photograph
      • Passport photocopy
      • PAN Card photocopy
      • AADHAR Card photocopy
      • Insurance
      • Ticket details
      • Blocked account

      Our Recruiters

      Contact Us

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      We offer most advanced technologies than any other computer and business training company. Businesses and individuals can choose from the course offerings, delivered by experts.

      Soul Space Paradigm, 3rd Floor, West Wing, next to Hotel Radisson Blu, Marathahalli, Bengaluru, Karnataka 560037

      info@vepsun.com

      +91 90-363-63007

      +91 90-353-53007