PG Program in Data Science and Artificial Intelligence from SteinbeisPG Program in Data Science and Artificial Intelligence from Steinbeis at Steinbeis University, Berlin
Data science is one of the most sought-after courses of the time and since its amalgamation with artificial intelligence, it has received even higher growth as well as demand from the industry. Due to it being new to the market and only catching the professional attention a little over 2 years ago, most of the candidates still don’t have the proper tools or techniques to master this skill set.
The course PG/Masters Program in Data Science and AI does a good job of filling the gap between demand and supply by providing its students with all the necessary information about the domain. It gives the candidates useful insights into the sector through which they can make it as successful personnel in the field.
The course is widely renowned as is the university that is providing it; Steinbeis University. The materials provided in the course deal with any doubt that may arise in the candidates’ minds beforehand and maintains a smooth exchange of learning with the instructors.
Candidates with a level of expertise in mathematical and analytical skills are eligible for the programme.
Certification Qualifying Details
Applicants will give an online exam after the end of the course in which scoring a minimum of 60% is necessary to ensure their certificate.
What you will learn
Data science knowledgeR ProgrammingKnowledge of PythonKnowledge of Artificial IntelligenceSQL knowledgeKnowledge of Data miningKnowledge of ExcelKnowledge of Apache SparkKnowledge of Amazon Web Services
Applicants will have mastered a great number of skills and gained useful information on many topics by the end of the course.
The candidates will start off by first mastering the skills of Agile.
Structured query language will be an important part of the candidate’s syllabus which will help them greatly in storing and manipulating data.
Applicants will learn the high-performance, object-oriented programming language Python that will work for analyzing machine algorithms and data.
Artificial intelligence taught in the course will convey deep learning mechanisms of image and audio files, as well as textual data.
Applicants will also be taught to store data and build as well as transform models using amazon web services.
Internet of Things (IoT) gives insights into the IoT sensors which teaches the candidates to retrieve streaming data and put it onto Cloud.
Below mentioned are the group of people that will receive the most benefits from the course.
Managers that wish to gain a strong grasp of data science to better optimise the information they get from clients are perfectly suited for the programme.
The course is a tailored fit for team leaders who want to learn to organise and analyse their data in order to develop a better work plan strategy.
Data science professionals are the ones this course was made for as it teaches them the shifts the industry is going through, with the introduction of AI, to not just learn how to grab a foothold in this scenario but how to make the most from the situation here as well.
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
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
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
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 DataBase
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
Savepoints
SQL transactions
TCL statements
ACID properties
Auto commit
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
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 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
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 RStudio
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
Types of Linkages Hierarchical Clustering / Agglomerative ClusteringNon-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
AdaBoost / 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
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
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
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
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
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
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
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
Steinbeis University, Berlin Frequently Asked Questions (FAQ's)
1: What tools will the candidates learn in the course?
The course will teach the candidates a number of great tools to progress in the industry including Amazon web services, Tableau, Agile, IoT, RDBMS, and more.
2: What are the criteria to qualify for the certificate?
For candidates to successfully qualify for the certificate, they need to first pass an examination with a minimum score of 60% at the end of the course.
3: How will the classes be conducted?
The mode of learning will be online which means there will be live sessions from the instructors of the classroom. However, it will be recorded to offer the candidates self-paced learning.
4: How will the candidates prepare for the exam?
The candidates will receive training for exam preparation. They will also have the option of participating in 2 online mock tests before the actual exam.
5: What will happen if candidates fail the exam?
In case a candidate fails to pass the exam or does not score the minimum marks for certification qualification, he/she will have the option of giving the exam once again.
6: What certificates will the candidates gain from the course?
The candidates will attain a course completion certificate from ExcelR, an Internship certificate from the AI variant, PG program certificate from Steinbeis University.
7: Which companies come to ExcelR for candidate placement?
ExcelR has the privilege of calling some of the world’s best companies their clients such as Mercedes-Benz, Metro, Dell, IBM, HP Enterprise, Amazon, Ericsson, Oracle and many more.
8: What is ExcelR?
ExcelR is an organization that provides training in high education programs to both students and professionals by collaborating with great educational institutions. It has awarded more than 30 franchises to entrepreneurs across the world.