Post Graduate Masters Program in Data Science and AI at Steinbeis University, Berlin, Program, Fees, Course, Admission, Application, Syllabus

Post Graduate Masters Program in Data Science and AI at Steinbeis University:- Data science is one of the most in-demand disciplines right now, and since it combined with artificial intelligence, the industry has demanded even more of it.

The majority of candidates still lack the skills and methods necessary to become proficient in this skill set because it is still relatively new to the market and just began to attract the attention of professionals little over two years ago.


Post Graduate Masters Program in Data Science and AI at Steinbeis University

What's In the Article

The Post Graduate Masters Program in Data Science and AI at Steinbeis University course effectively bridges the knowledge gap between supply and demand by giving students all the subject knowledge they need. It provides the candidates with insightful knowledge about the industry that will help them succeed as field workers.

Both the course and Steinbeis University, the institution offering it, are well-known. The training materials address any questions that candidates could have beforehand and facilitate an easy learning exchange between them and the teachers.

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Post Graduate Masters Program in Data Science and AI at Steinbeis University

Post Graduate Masters Program in Data Science and AI at Steinbeis University Details

Article Name Post Graduate Masters Program in Data Science and AI at Steinbeis University, Berlin
Year 2024
Category Courses
Official site Click Here

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The Highlights

  • Steinbeis University certification
  • 10-month long course
  • ExcelR offered programme
  • Dual certification
  • Faculty of Steinbeis University
  • SGIT alumnus status
  • Online sessions
  • Shareable certificate
  • Self-paced learning

Programme Offerings

  • Projects
  • assignments
  • videos
  • blended learning
  • E-learning
  • Placement Support
  • Instructor-led classroom

Courses and Certificate Fees

Certificate Availability Certificate Providing Authority
Yes ExcelR Solutions Steinbeis Global Institute, Tubingen

Eligibility Criteria


Applicants with a level of expertise in mathematical and analytical skills are eligible for the programme.

Certification Qualifying Details

After the course, applicants will take an online exam, on which they must receive a minimum of 60% in order to receive their credential.

What you will learn

Post Graduate Masters Program in Data Science and AI at Steinbeis University of Python Knowledge of Artificial Intelligence SQL knowledge Knowledge of Data mining Knowledge of Excel Knowledge of Apache Spark Knowledge of Amazon Web Services

By the end of the course, applicants will have obtained valuable knowledge on a wide range of topics and developed a great number of skills.

  • First, the candidates will need to become proficient in Agile abilities.
  • The candidate’s curriculum will include structured query language, which will be crucial for data manipulation and archiving.
  • The object-oriented, high-performance programming language Python will be taught to applicants, who will use it to analyse data and machine algorithms.
  • The course’s instruction on artificial intelligence will cover deep learning techniques for textual, audio, and picture data.
  • Additionally, candidates will learn how to create, modify, and save models using Amazon Web Services.
  • Candidates learn how to retrieve streaming data and upload it to the cloud by studying the Internet of Things (IoT), which provides insights into IoT sensors.

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Who it is for

Data Scientist

The group of persons who will get the most from the course is listed below.

  • The curriculum is ideal for managers who want to better understand data science so they can better utilise the information their clients provide.
  • Team leaders who wish to learn how to organise and analyse their data to create a more effective work plan strategy will find the course to be a perfect fit.
  • This course was designed with data science professionals in mind, teaching them not only how to get a foothold in this environment but also how to take full advantage of the circumstance by educating them on the changes the industry is going through due to the arrival of AI.

The Syllabus


Agile communications

  • Agile Tooling
  • Daily Stand-ups
  • Osmotic Communication
  • Information Radiator
  • Team Space

Planning and Monitoring

  • Progressive Elaboration
  • Iteration and Release Planning
  • Innovation Games
  • Kanban Boards
  • Time Boxing
  • Retrospectives
  • Cumulative Flow Diagram
  • Burn Charts
  • WIP Limits

Risk Management

  • Mock Test
  • Motivational Theories
  • Manage Project Team
  • Acquire Project Team
  • Develop Project Team
  • Plan Human Resource Management
  • What is Human Resource Management?

Product Quality

  • Incremental Delivery
  • Continuous Improvement
  • Continues Integration
  • Definition of Done
  • Test-Driven Development
  • Frequent Verification and Validation–
  • Feedback Techniques

Agile Analysis and Design (continues)

  • Charting
  • Agile modeling
  • Wireframes
  • Personas

Agile Analysis and Design

  • Story Maps
  • Personas
  • Product Roadmap
  • Charting
  • Backlog
  • Agile Modeling
  • Wireframes

Agile Metrics and Estimations

  • Cycle Time
  • Ideal time
  • Escaped Defects
  • Wideband Delphi Technique
  • Story Points
  • Velocity
  • Planning Poker
  • EVM
  • Relative Sizing
  • Affinity Diagram

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Knowledge and Skills

  • Level 1, Level2, Level 3

Introduction to Agile

  • Agile Methodology
  • Project Charter for Agile Project
  • Agile Frameworks and Terminology
  • Agile Manifesto and Principles
  • Agile Principles

Value Based Prioritization

  • Customer Value Prioritization
  • Value Stream Mapping
  • Relative Prioritization
  • Minimum Marketable Feature
  • Compliance

Agile Methodologies (Brief Introduction)

  • XP
  • Scrum

Agile Project Management Office

  • Failure mode analysis
  • Vendor management

Project Communications Management

  • Risk-based spike
  • Risk burn down charts
  • Risk-adjusted backlog

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Internet of Things (IoT)

Hands-on using Raspberry Pi board

  • Making raspberry Pi webserver
  • Running python on Raspberry Pi, GPIO programming
  • Booting up Raspberry Pi
  • Sending data to cloud 2 using Raspberry Pi board
  • Making a few projects
  • Making raspberry Pi UDP client and server
  • Sending data to cloud 3 using Raspberry Pi board
  • Interfacing sensors and LED (Input and output devices)
  • Setting up board
  • Making raspberry PI TCP client and server


  • Barrier in IoT
  • Existing Product in Market


  • Leveraging different cloud platforms
  • Importance of Cloud Computing in IoT
  • Concept & Architecture of Cloud
  • Public cloud vs Private cloud
  • Different Services in cloud (IAAS / PAAS / SAAS)

IoT Device Design

  • Embedded Development Boards
  • Sensors

Use cases

  • Remote controlling with Node MCU
  • Raspberry Pi controlling Esp8266 using MQTT
  • Obstacle detection using an IR sensor and Arduino
  • Weather monitoring system using Raspberry Pi and Microsoft Azure cloud
  • Temperature monitoring using a Raspberry Pi as a local server
  • Esp8266 WIFI controlled Home automation
  • A cloud-based temperature monitoring system using Arduino and Node MCU

IoT Communication Protocols

  • Transport layer protocols – TCP vs UDP
  • Wireless Communication Protocols
  • IP- IPv4 vs IPv6
  • Wired Communication Protocols
  • Application Protocols – MQTT, CoAP, HTTP, AMQP

Introduction- Concepts and Technologies behind Internet of Things (IoT)

  • Machine learning
  • IoT Applications
  • Artificial Intelligence
  • Why IoT is essential
  • IoT system overview
  • Carrier in IoT
  • Node, Gateway, Clouds
  • Business with IoT
  • Concepts & Definitions
  • Myth with IoT

IoT Architecture

  • IoT Device Architecture
  • Publish-Subscribe architecture
  • IoT Network Architecture
  • IoT Device Architecture

Designing the IoT product

  • Design Considerations – Cost, Performance & Power Consumption tradeoffs
  • Design Considerations – Cost, Performance & Power Consumption tradeoffs


  • Arduino
  • Python
  • Embedded C

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SQL Commands

  • DDL commands
  • SQL data types
  • DCL and TCL
  • DDL commands
  • Data query language
  • Types of SQL commands
  • Data manipulation language
  • Data definition language

Stored Procedures and Functions

  • Joining tables
  • Programming
  • Advantages of procedures
  • Stored objects
  • Operators and functions
  • Stored procedures

Database Triggers Accessing Database from R and Python

  • Triggers
  • Python database access
  • Accessing the database from R

Database Constraints

  • Types of constraints
  • After tables
  • Types of constraints

Introduction to Databases

  • Comparison
  • Normalization
  • Popular DBMS Software
  • Database
  • NoSQL databases
  • Concepts of RDBMS
  • Introduction to DBMS

Database Objects

  • Indexes
  • Sequences
  • Views
  • Tables

Basics of MYSQL

  • Creating to DB
  • Different operations in SQL
  • Joining 2 tables
  • Where clause usage
  • How to Connect to your applications from MYSQL includes R and Python
  • Introduction to What is Data Base
  • How to Install MYSQL and Workbench
  • Difference between SQL and NoSQL DB
  • Select statement and using Queries for seeing your data
  • What are the Languages inside SQL How to Create Tables inside DB and Inserting the Records
  • Indexes and views
  • Connecting to DB

SQL Transactions

  • Save points
  • SQL transactions
  • TCL statements
  • ACID properties
  • Auto commit

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How to Import Dataset in R

  • Read Excel Files
  • Read SAS Files
  • Read STATA Files
  • Read CSV Files
  • Read SPSS Files
  • Read Text Files
  • Read JSON Files


  • Data table
  • Dplyr
  • Ggplot2
  • Caret
  • Hmisc or mise

Data Structures in R

  • R overview
  • Variable in R
  • Conditional statements
  • Operators in R

Programming Statistical

  • Line Chart
  • Pareto Chart
  • Box Plots
  • Bar Charts
  • Pie Chart
  • Histogram
  • Scatterplot

Introduction To R Programming

  • Data types in R
  • Introduction to R

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  • Overview
  • Environment
  • Application
  • Creating views
  • Apps life cycle
  • Introduction to Django framework


  • Built-in tuples functions
  • Tuples


  • Bitwise operator
  • Assignment
  • Operator-Arithmetic
  • Logical
  • Comparison


  • Overriding methods like _init_, Overloading operators, Data hiding
  • OOP concepts, class, objects, Inheritance


  • Date & time -Time Tuple, calendar module and time module
  • Properties of Dist., Built-in Dist functions & Methods.
  • Dictionary – Accessing values from the dictionary, Deleting and updating elements in Dict.

GUI Programming

  • Tkinter widgets
  • Tkinter programming
  • Introduction


  • create, insert, update and delete operation, Handling errors
  • Methods- MySQL, oracle, how to install MYSQL, DB connection
  • Database connectivity


  • Scope of variables – local & global
  • pass by reference as value, Function arguments, Anonymous functions, return statements
  • Function – Define function, Calling function


  • Python IDE
  • Python introduction – programming cycle of python


  • Packages in Python
  • Dir() function , global and location functions and reload () functions
  • Import statements, Locating modules – current directory, Pythonpath


  • Number
  • Variables
  • List
  • String
  • Data type
  • Dictionary
  • Tuple

Exception Handling

  • Try- finally, clause and user defined exceptions
  • Exception handling – List of exceptions – Try and exception


  • Built in Function – cmp(), len(), min(), max()
  • Python List – Accessing values in list, Delete list elements, Indexing slicing & Matrices

Decision making – Loops

  • Number type conversion – int(), long(). Float ()
  • Mathematical functions, Random function, Trigonometric function
  • While loop, if loop and nested loop


  • Build in string methods – center(), count()decode(), encode()
  • Strings- Escape char, String special Operator , String formatting Operator


  • Files object attribute- open, close. Reading and writing files, file Position.
  • Files in Python- Reading keyboard input, an input function
  • Opening and closing files. Syntax and list of modes
  • Renaming and deleting files

Regular Expressions

  • Regular exp modifiers and patterns
  • Match function, search function, matching vs searching


  • What is CGI? Architecture of CGI, Web server support, get and post () methods.

Multi Threading

  • Threading module
  • Creating thread
  • Into Multithreading
  • Multithreaded Priority Queue
  • Synchronizing threads


  • Mkdir method, Chdir () method, Getcwd method, rm dir

Data Analysis Libraries

  • Numpy, Pandas, Matplotlib

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Data science

Regularization Techniques

  • Lasso and Ridge Regressions

Linear Regression

  • Model Quality metrics
  • Understanding Overfitting (Variance) vs Underfitting (Bias)
  • Correlation Analysis
  • Scatter Diagram
  • Generalization error and Regularization techniques
  • Principles of regression
  • Heteroscedasticity / Equal Variance
  • Introduction to Simple Linear Regression
  • Multiple Linear Regression
  • Splitting the data into training, validation and testing datasets
  • Deletion diagnostics
  • Multiple Linear Regression
  • Introduction to R shiny and Python Flask (deployment)
  • Ordinary least squares
  • Scatter diagram
  • Principles of Regression
  • LINE assumption
  • Introduction to Simple Linear Regression
  • R shiny and Python Flask

Introduction to R and Python Basic Statistics

  • Probability and Probability Distribution – Continuous probability distribution / Probability density function and Discrete probability distribution / Probability mass function
  • Normal Distribution
  • T-distribution / Student’s-t distribution
  • The various Data Types namely continuous, discrete, categorical, count, qualitative, quantitative and its identification and application. Further classification of data in terms of Nominal, Ordinal, Interval and Ratio types
  • Sample size calculator
  • Confidence interval
  • Standard Normal Distribution / Z distribution
  • Balanced vs Imbalanced datasets
  • Various graphical techniques to understand data
  • A high-Level overview of Data Science / Machine Learning project management methodology
  • The measure of Dispersion Expected value of probability distribution
  • Central Limit Theorem
  • Installation of Python IDE
  • Anaconda and Spyder
  • Measure of Skewness
  • Various sampling techniques for handling balanced vs imbalanced datasets
  • Introduction to R and Studio
  • Measure of Kurtosis
  • What is Sampling Funnel, its application and its components Measure of central tendency
  • Videos for handling imbalanced data will be provided
  • Videos for Data Collection – Surveys  and Design of Experiments will be provided
  • Working with Python and R with some basic commands
  • Random Variable and its definition
  • Z scores and the Z table
  • Sampling Variation
  • QQ Plot / Quantile-Quantile plot

Data Science Project Lifecycle

  • Recap of Demo
  • Project life cycle
  • Introduction to Types of Analytics

Hypothesis Testing

  • 2 sample t-test
  • Hypothesis testing using Python and R
  • Comparative study of sample proportions using Hypothesis testing
  • Non-Parametric test continued
  • Chi-Square test
  • Non-Parametric test
  • Choosing Null and Alternative hypothesis
  • Formulating a Hypothesis
  • 1 sample t-test
  • 2 Proportion test
  • Type I and Type II errors
  • 1 sample z test
  • Parametric vs Non-parametric tests

Multinomial Regression

  • Additional videos are provided on Lasso / Ridge regression for identifying the most significant variables
  • Modeling Nominal categorical data
  • Logit and Log-Likelihood
  • Category Baselining

Logistic Regression

  • Analysis of Simple Logistic Regression result
  • Confusion matrix
  • Multiple Logistic Regression
  • Principles of Logistic Regression
  • Assumption and Steps in Logistic Regression
  • Receiver operating characteristics curve (ROC curve)
  • Lift charts and Gain charts
  • Types of Logistic Regression

Data Mining Unsupervised – Clustering

  • Measure of distance
  • Supervised vs Unsupervised learning
  • Types of Linkages Hierarchical Clustering / Agglomerative Clustering Non-clustering Additional videos are provided to understand K-Medians, K-Medoids, K-Modes, Clustering Large Applications (CLARA), Partitioning Around Medoids (PAM), Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Ordering Points To Identify the Clustering Structure (OPTICS)
  • Data Mining Process

Data Mining Unsupervised – Network Analytics

  • Introduction to Google Page Ranking
  • Definition of a network (the LinkedIn analogy)
  • The measure of Node strength in a Network

Natural Language Processing

  • Lexicons and Emotion Mining
  • Sentiment Extraction
  • LDA
  • Topic Modeling

Data Mining Unsupervised – Association Rules

  • Apriori Algorithm
  • Measure of association
  • Sequential Pattern Mining
  • What is Market Basket / Affinity Analysis

Dimension Reduction

  • Basics of Matrix algebra
  • 2D Visualization using Principal components
  • SVD – Decomposition of matrix data
  • Why dimension reduction
  • Calculation of PCA weights
  • Advantages of PCA

Data Mining Unsupervised – Recommender system

  • The measure of distance/similarity between users
  • Search based methods / Item to item collaborative filtering
  • User-based collaborative filtering
  • The vulnerability of recommender systems
  • Driver for recommendation
  • Computation reduction techniques
  • SVD in recommendation

Text Mining

  • Semantic network
  • Clustering
  • Pre-processing, corpus Document-Term Matrix (DTM) and TDM
  • Extract Tweets from Twitter
  • Bag of words
  • Word Clouds
  • Sources of data
  • Extraction and text analytics in Python
  • Extract user reviews of the products from Amazon, Snapdeal and TripAdvisor
  • Install Libraries from Shell
  • Corpus level word clouds


  • Industry: E-commerce
  • Industry: Oil and Gas
  • Industry: Aviation
  • Industry: Manufacturing
  • Industry: Daily Analysis of a product
  • Industry: Automotive


  • Lag Plot
  • Random walk
  • Model-Based approaches
  • Data-driven approach to forecasting
  • AR (Auto-Regressive) model for errors
  • Steps of forecasting
  • Forecasting using R
  • ARMA (Auto-Regressive Moving Average), Order p and q
  • Model-Based approaches
  • Forecasting using Python
  • Naive forecast methods
  • Econometric Models
  • Introduction to time series data
  • Errors in forecast and its metrics
  • Forecasting Best Practices
  • Visualization principles
  • Scatter plot and Time Plot
  • ACF – Auto-Correlation Function / Correlogram
  • ARIMA (Auto-Regressive Integrated Moving Average), Order p, d and q
  • Components of time series data
  • Smoothing techniques
  • De-seasoning and de-trending

Machine Learning Classifiers – KNN

  • Understanding the various generalization and regulation techniques to avoid overfitting and underfitting
  • Building a KNN model by splitting the data
  • Deciding the K value

Decision Tree and Random Forest

  • Decision Tree C5.0 and understanding various arguments
  • Greedy algorithm
  • Measure of Entropy
  • Ensemble techniques
  • Random Forest and understanding various arguments
  • Attribute selection using Information Gain
  • Elements of Classification Tree – Root node, Child Node, Leaf Node, etc.

Classifier – Naive Bayes

  • Naive Bayes Classifier
  • Probability – Recap
  • Text Classification using Naive Bayes
  • Bayes Rule

Bagging and Boosting

  • Stacking
  • Extreme Gradient Boosting (XGB)
  • Boosting / Bootstrap Aggregating
  • Gradient Boosting
  • Ada Boost / Adaptive Boosting

Resume Prep and Interview support

  • Interview support
  • Resume preparation


  • ANN
  • Hierarchical Clustering
  • Survival analysis
  • Association Rules
  • Hypothesis Testing
  • Multinomial Regression
  • Network Analytics
  • Multiple Linear Regression
  • R shiny and Flask
  • Forecasting
  • XGB and GLM
  • Recommendation Engine
  • NLP
  • Naive Bayes
  • Lasso and Ridge Regression
  • Decision Tree and Random Forest
  • Text mining
  • PCA
  • SVM
  • Text mining, Web Extraction
  • KNN Classifier
  • Logistic Regression
  • Forecasting model-based
  • K means Clustering
  • Linear regression
  • Basic Statistics

Black Box Methods

  • Activation function
  • Best fit “boundary”
  • Classification Hyperplanes
  • Network Topology
  • Biological Neuron vs Artificial Neuron
  • ANN structure
  • Support Vector Machines
  • Kernel Trick
  • Artificial Neural Network

Survival Analysis

  • The concept with a business case

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Artificial intelligence

Intro to Neural Network & Deep Learning

  • Loss Function
  • Intro
  • Importance of Non-linear Activation Function
  • Neural Network Representation
  • Gradient Descent for Neural Network
  • Activation Function
  • SP | MLP
  • Neural Network Overview
  • Deep Learning Importance [Strength & Limitation]

Introduction to Machine Learning

  • Computation Graph
  • ML Strategy
  • Train, Test & Validation Distribution
  • Human-Level Performance
  • Evaluation Metric


  • Negative Sampling
  • Word Embedding
  • LSTM
  • Backpropagation through time
  • Bidirectional LSTM
  • Deep RNN
  • RNN Model
  • Beam Search
  • Attention Model
  • Why Sequence Model
  • Debiasing
  • Different Type of RNNs
  • GRU
  • Elmo & Bert


  • Parameter & Hyperparameter
  • Machine Learning
  • Intro to Neural Network & Deep Learning
  • Data Processing
  • RNN
  • Generative
  • CNN
  • Python Programming
  • Introduction to Machine Learning
  • Mathematics Foundation
  • Reinforcement Learning

Python Programming

  • NLP Libraries
  • Basic Programming
  • OpenCV

Data Processing

  • Object Detection
  • Speech Data Analytics
  • Image Processing
  • Environment
  • Feature Extraction
  • Text Processing

Reinforcement Learning

  • Exploration and exploitation
  • Q learning


  • Deep Convolution Model
  • Face Recognition
  • Detection Algorithm
  • CNN


  • Face detection from CC camera feed
  • Chatbot project
  • Emotion Analytics
  • Object Detection

Machine Learning

  • Unsupervised
  • Supervised

Generative Adversarial Network

  • Active Learning
  • Autoencoders & Decoders
  • Adversarial Network

Mathematics Foundation

  • Basic Statistics
  • Probability
  • Calculus
  • Linear Algebra

Parameter & Hyperparameter

  • Optimization
  • Practical aspect

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Intermediate Chart

  • Dual Combination
  • Time Series Hands-On
  • Dual Lines
  • Time Series Charts

Connecting Tableau with R

  • What is R?
  • Tableau Prep
  • How to integrate Tableau with R?

Creating Calculated Fields

  • ZN Function
  • Else-If Function
  • Level of Detail (LoD)
  • Exclude LoD
  • Fixed LoD
  • Ad-Hoc Calculations
  • Logical Functions
  • Include LoD
  • Quick Table Calculations
  • Case-If Function

Maps in Tableau

  • Radial & Lasso Selection
  • Polygon Maps
  • Data Layers
  • Types of Maps in Tableau
  • Custom Geocoding
  • Connecting with WMS Server

Responsive Tool Tips

  • Dashboards
  • Story
  • Actions at Sheet level and Dashboard level

Connecting Tableau with Tableau Server

  • Publishing dataset on to Tableau Server
  • Setting Permissions on Tableau Server
  • Publishing our Workbooks in Tableau Server

Tableau User Interface

  • Understanding about Data Types and Visual Cues

Basic Chart types

  • Pie Chart, Tree Chart
  • Bar Charts, Circle Charts
  • Text Tables, Highlight Tables, Heat Map

Adding Background Image

  • Filters and their working at different levels
  • Creating Data Extracts
  • Worksheet level filters
  • Context, Dimension Measures Filter
  • How to get Background Image and highlight the data on it
  • Usage of Filters on at Extract and Data Source level

Advanced Charts

  • Donut Chart, Word Cloud
  • Introduction to Correlation Analysis
  • Pareto Chart
  • Bullet Chart
  • Forecasting ( Predictive Analysis)
  • Introduction to Regression Analysis
  • Scatter Plot
  • Box Plot
  • Histograms
  • Bin Sizes in Tableau
  • Trendlines

Tableau – Data Visualization Tool

  • Rename and Aliases
  • Data Interpretation
  • Introduction to Tableau
  • Hiding
  • Architecture Of Tableau
  • Split Tables
  • What is Tableau? Different Products and their functioning
  • Pivot Tables

What is data visualization?

  • Principles of Visualizations
  • Tufte’s Principles for Analytical Design
  • Tufte’s Graphical Integrity Rule
  • Importance of Visualizing Data
  • Why did Visualization come into the Picture?
  • The goal of Data Visualization
  • Visual Rhetoric
  • Poor Visualizations Vs. Perfect Visualizations

Data connectivity in-depth understanding

  • Data Blending
  • Unions
  • Cross-Database Joins
  • Parameters
  • Joins
  • Groups
  • Sets

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Cloud azure

Cloud computing

  • Creation of Free tire account inside Azure
  • Creating DB instance
  • Storage options and Creating Extra Storage and attaching to the VMs
  • Types of Service Models
  • Sample Instance Creating Both UNIX and Windows and connecting them on cloud
  • Advantages of Cloud Computing
  • Creating Custom VN
  • Difference between On-Premise and Cloud
  • A brief introduction to Machine Learning Services on Cloud and more
  • Blob Storage
  • Introduction to Cloud Computing
  • Azure Global Infrastructure

Hadoop and Spark

Introduction to Big data

  • Introduction to Hadoop and its Components
  • Spark MLlib and Hands-on (one ML model in spark)
  • Spark Components
  • Introduction to Spark
  • Introduction to Big Data
  • Challenges in Big Data and Workarounds
  • Hadoop components and Hands-on
  • Understand the MapReduce (Distributed Computation Framework) and its Drawback

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Advanced Excel -Sorting and Filtering Data

  • Using advanced filter options
  • Filtering data for the selected view (AutoFilter)
  • Sorting tables
  • Using custom sorting
  • Sorting tables

Advanced Excel –Basic

  • Protecting and unprotecting worksheets
  • Various selection techniques
  • Shortcuts Keys
  • Format Cells
  • Worksheets
  • Customizing the Ribbon

Advanced Excel -VBA-Macro

  • What can you do with VBA?
  • What can you do with VBA?
  • What is VBA?
  • Introduction to VBA
  • Procedures and Function in VBA

Advanced Excel -Pivot Tables

  • Grouping Based on number and Dates
  • Basic and Advanced value field setting
  • Calculated field and Calculated items
  • Creating Simple Pivot Tables

Advanced Excel -Variable in VBA

  • Using Non-declared variables
  • Variable Data Types
  • What are Variables?

Advanced Excel –Charts & Slicers

  • Sharing Charts with PowerPoint / MS Word, Dynamically
  • Formatting Charts
  • Using the Secondary Axis in Graphs
  • Using Bar and Line Chart together
  • Using Charts
  • Using 3D Graphs

VBA Coding Advanced function

  • The automated report will be shown
  • Mail Function –send an automated email
  • If and Select statement
  • Looping in VBA

Advanced Excel -Message-Box and Input-box functions

  • Reading cell values into messages
  • Various button groups in VBA
  • Customize Message-Box and Input-box

Advanced Excel -Working with Templates

  • Using templates for standardization of worksheets
  • Designing the structure of a template

Advanced Excel -Text Function

  • Trim, Len, Exact
  • Concatenate
  • Upper, Lower, Proper
  • Left, Mid, Right

Advanced Excel -Data Validation

  • Specifying custom validations based on the formula for a cell
  • Specifying a valid range of values for a cell
  • Specifying a list of valid values for a cell

Advanced Excel -Function & Formula

  • Date & Time Function
  • V-lookup with Tables, Dynamic Ranges
  • Mathematical Functions
  • Lookup and reference functions (VLOOKUP, HLOOKUP, MATCH, INDEX)
  • Basic Function –Sum, Average, Max, Min, Count, Count A
  • Nested V-lookup with Exact Match
  • SumIf, CountIf, AverageIf etc
  • Using V-lookup to consolidate Data from Multiple Sheets
  • Conditional Formatting
  • Logical functions (AND, OR, NOT)
  • V-lookup with Exact Match, Approximate Match
  • Nested V-V-lookup with Exact Match

Also Check:- Advanced Management Programme in Business Analytics at ISB Hyderabad

Amazon web services

AWS Elastic Compute Cloud Services(EC2)

  • Feature of Elastic Compute Cloud
  • Types of instances offered by AWS in EC2
  • About Autoscaling & Use Cases
  • Elastic Compute Cloud Essentials
  • Elastic IP Addressing
  • Elastic Block Store Volumes Use Cases
  • AWS Pricing & Calculating
  • EBS based Snapshot
  • Configure and Deploy EC2 instances.
  • Working with Amazon Machine Image

IAM & Monitoring services

  • Multi-Factor Authentication using MFA Device
  • Identity and Access Management (IAM)
  • Authorization & Authentication for Users & Groups
  • Creation of Users & Groups in IAM

AWS Relational Database Service(RDS)

  • Deploying RDS Instance & Configuring it.
  • Amazon DynamoDB
  • About RDS

Amazon AWS Route 53

  • Configuring AWS Route 53
  • Features of Route 53

About Cloud Technology

  • Various Advantages of Cloud Technology
  • Cloud Computing Technology & its Concepts
  • Comparison between On-Premise & Cloud Infrastructure
  • Types of Cloud Services being offered

AWS Cloud Architecture & Infrastructure Details

  • About Edge Locations
  • A Region and Availability Zone
  • AWS Cloud Legal & Compliance Overview

Chronology & History of AWS

  • E Chronology & Events of AWS Cloud
  • Evolution of Amazon Web Services
  • Eglobal Clients of AWS Cloud

AWS Monitoring & Notification Services

  • Simple Notification Service (SNS)
  • Amazon Simple Queue Service (SQS)
  • AWS Cloud Watch

Amazon Web Services Network Services

  • Network Address Translation (NAT) Gateway
  • About Cloud Front and ways to Configure it
  • Virtual Private Cloud Setup
  • Use Case of NAT Gateway
  • Introduction to AWS Cloud Networking services
  • Public & Private Subnets Creation within a VPC
  • Establishing Connection between two VPCs through VPC Peering
  • Configuring Internet Gateway

AWS Storage Services(S3)

  • Static Website Hosting
  • Creating S3 Bucket.
  • Simple Storage Service (S3)
  • AWS Glacier
  • Storages Classes in S3 Bucket
  • Configuring EFS and its Use Case
  • AWS Elastic File System & its Advantages
  • Versioning in S3
  • Cross-Region Replication of Data through S3
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Post Graduate Masters Program in Data Science and AI at Steinbeis University FAQ’S

Who is eligible for MSc in data science and artificial intelligence?

A bachelor’s or master’s degree in a relevant field, such as computer science, mathematics, statistics, science, technology, or engineering, provides a strong foundation in quantitative analysis, programming, and data management.

What is the cost of Steinbeis certification in India?

Self-paced learning is priced at Rs 49999 and the live virtual classroom is priced at Rs 20999.

Which is better masters in AI or data science?

One of the chief differences between the two closely related disciplines is that, while data science focuses on gaining insights from data using analysis, visualization, and prediction in processes controlled by humans, artificial intelligence focuses on autonomous decision making and actions — relying on humans to set …

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