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.

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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 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

Education

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

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
ROI/NPV/IRR
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

Closer

Barrier in IoT
Existing Product in Market

Cloud

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

Programming

Arduino
Python
Embedded C

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SQL

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

R-Packages

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

Python

Framework

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

Tuples

Built-in tuples functions
Tuples

Operators

Bitwise operator
Assignment
Operator-Arithmetic
Logical
Comparison

OOP

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

Dictionary

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

My SQL

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

Function

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

Introduction

Python IDE
Python introduction – programming cycle of python

Modules

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

Variables

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

List

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

String

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

Files

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

CGI

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

Directories

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
ANOVA
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

Projects

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

Forecasting

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

Assignments

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

Introduction to Machine Learning

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

RNN

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

Assignments

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

CNN

Deep Convolution Model
Face Recognition
Detection Algorithm
CNN

Projects

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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Tableau

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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Excel

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

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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

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

WhatsApp Group

Telegram Group

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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