PG Program in Data Science and Artificial Intelligence from SteinbeisPG Program in Data Science and Artificial Intelligence from Steinbeis at Steinbeis University, Berlin

By Steinbeis University, Berlin via ExcelR Solutions

Get the skills required to be a top Data Scientist and gain deeper understanding of AI with PG Program in Data Science and AI Course from Steinbeis.

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11 Months

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Quick Facts

Medium Of Instructions	Mode Of Learning	Mode Of Delivery
English	Self Study, Virtual Classroom	Video and Text Based

Course Overview

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 PG Program in Data Science and Artificial Intelligence from Steinbeis 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.

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 SolutionsSteinbeis Global Institute, Tubingen

Eligibility Criteria

Education

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.

Who it is for

Data Scientist

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

Introduction

MS office Versions(similarities and differences)
Interface(latest available version)
Row and Columns
Keyboard shortcuts for easy navigation
Data Entry(Fill series),Find and Select
Clear Options,Ctrl+Enter
Formatting options(Font,Alignment,Clipboard(copy, paste special))

Referencing, Named Ranges,Uses,Arithemetic Functions

Mathematical calculations with Cell referencing(Absolute,Relative,Mixed) Functions with Name Range
Arithmetic functions(SUM,SUMIF,SUMIFS,COUNT,COUNTA,COUNTIFS,AVERAGE,AVERAGEIFS,MAX,MAXIFS,MIN,MINIFS)

Logical Functions

Logical functions:IF,AND,OR,NESTED IFS,NOT,IFERROR Usage of Mathematical and Logical functions nested together

Referring Data From Different Tables: Various Types Of Lookup, Nested IF

LOOKUP
VLOOKUP
NESTED VLOOKUP
HLOOKUP
INDEX
INDEX WITH MATCH FUNCTION
INDIRECT
OFFSET

Advanced Functions

Combination of Arithmatic
Logical
Lookup functions
Data Validation(with Dependent drop down)

Date And Text Functions

Date Functions:DATE,DAY,MONTH,YEAR,YEARFRAC,DATEDIFF,EOMONTH;
Text Functions:TEXT,UPPER,LOWER,PROPER,LEFT,RIGHT,SEARCH,FIND,MID,TTC, Flash Fill

Data Handling::Data Cleaning, Data Type Identification, Remove Duplicates, Formatting And Filtering

Number Formatting(with shortcuts)
- CTRL+T(Converting into an Excel Table)
- Formatting Table
- Remove
- Duplicate
- SORT
- Advanced Sort
- FILTER
- Advanced Filter

Data Visualization: Conditional Formatting, Charts

Conditional formatting(icon sets/Highlighted colour sets/Data bars/custom formatting)
Charts:Bar,Column,Lines,Scatter,Combo,Gantt,Waterfall,pie

Data Summarization: Pivot Report And Charts

Pivot Reports
- Insert,Interface,Crosstable Reports;Filter,Pivot Charts
Slicers
- Add,Connect to multiple reports and charts Calculated field, Calculated item

Data Summarization: Dashboard Creation, Tips And Tricks

Dashboard
- Types,Getting reports and charts together, Use of Slicers
Design and placement
- Formatting of Tables,Charts,Sheets,Proper use of Colours and Shapes

Connecting To Data: Power Query, Pivot, Power Pivot Within Excel

Power Query:
- Interface, Tabs; Connecting to data from other excel files, text files, other sources, Data Cleaning, Transforming, Loading Data into Excel Query

Connecting To Data: Power Query, Pivot, Power Pivot Within Excel

Using Loaded queries
Merge and Append
Insert Power Pivot
Similarities and Differences in Pivot and Power Pivot reporting
Getting data from databases
workbooks
webpages

VBA And Macros

View Tab
Add Developer Tab
Record Macro:Name
Storage Record Macro to Format table(Absolute Ref)
Format table of any size(Relative ref)
Play macro by button,shape,as command(in new tab)
Editing Macros
VBA:Introduction to the basics of working with VBA for Excel: Subs, Ranges, Sheets. Comparing values and conditions, if statements and select cases. Repeat processes with For loops and Do While or Do Until Loops
Communicate with the end-user with message boxes and take user input with input boxes, User Form

Power BI Introduction And Installation

Understanding Power BI Background
Installation of Power BI and check list for perfect installation Formatting and Setting prerequisits
Understanding the difference between Power BI desktop & Power Query

The Power BI User Interface, Including Types Of Data Sources And Visualizations.

Getting familiar with the interface BI Query & Desktop Understanding type of Visualisation Loading data from multiple sources Data type and the type of default chart on drag drop. Geo location Map integration

Sample Dashboard With Animation Visual

Finanical sample data in Power BI
Preparing sample dashboard as get started
Map visual Types and usages in different variation Understanding scatter Plot chart with Play axis and the parameters

Power BI Artificial Intelligence Visual

Understanding the use of AI in power Bi AI analysis in power bi using chart Q&A chat bot and the use in real life Hirarchy tree

Power BI Visualization

Understanding Column Chart Understanding Line Chart Implementation of Conditional formating Implementation of Formating techniques

Power Query Editor

Loading data from folder Understanding Power Query in detail Promote header
Split to limiter
Add columns
append
merge queries etc

Modelling With Power BI

Loading multiple data from different format understanding modelling (How to create relationship) connection type
Data cardinality
Filter direction Making dashboard using new loaded data

Power Query Editor Filter Data

Power Query Custom Column & Conditional Column Manage Parameter
Introduction to Filter and types of filter Trend analysis
Future forecast

Customize The Data In Power BI

Understanding Tool tip with information Use and understanding of Drill Down Visual interaction and customisation of visual interaction
Drill through function and usage Button triggers
Bookmark and different use and implementation Navigation buttons

Dax Expressions

Introduction to DAX Table Dax
Calculated column
DAX measure and difference Eg:- Calendar, Calendar auto, Summarize, Group by etc
Calculated Column Related
Lookup value
switch
Datedif
Rankx
Date functions
Dax Measure and Quick Measure Remove filters
Keep filters
All
Allselected
Time Intelligence Functions
Rolling average
YoY
Running total

Custom Visual

Custom visual and understanding the use of custom Loading custom visual
Pinning visual Loading to template for future use Publishinhg Power Bi

Power BI Service

Introduction to app.powerbi.com Schedule refresh Data flow and use power bi from online Download data as live in power point and more

Introduction To Tableau

What is Tableau ?
What is Data Visulaization ?
Tableau Products
Tableau Desktop Variations
Tableau File Extensions
Data Types
Dimensions
Measures
Aggregation concept Tableau Desktop Installation Data Source Overview Live Vs Extract

Basic Charts & Formatting

Overview of worksheet sections Shelves
Bar Chart
Stacked Bar Chart
Discrete & Continuous Line Charts
Symbol Map & Filled Map
Text Table
Highlight Table
Formatting
- Remove grid lines
- hiding the axes
- onversion of numbers to thousands
- millions
- Shading
- Row divider
- Column divider Marks Card

Filters

What are Filters?
Types of Filters
- Extract
- Data Source
- Context
- Dimension
- Measure
- Quick Filters Order of operation of filters Cascading Apply to Worksheets

Calculations

Need for calculations Types: Basic, LOD's, Table
Examples of Basic Calculations
- Aggregate functions
- Logical functions
- String functions
- Tablea calculation functions
- numerical functions
- Date functions

Calculations Continued

LOD's: Examples
Table Calculations: Examples

Data Combining Techniques

What is Data Combining Techniques ?
Types
- Joins
- Relationships
- Blending & Union

Custom Charts

Dual Axis
Combined Axis
Donut Chart
Lollipop Chart
KPI Cards (Simple)
KPI Cards (With Shape)

Groups, Bins, Hierarchies, Sets, Parameters

What are Groups ? Purpose
What are Bins ? Purpose
What are Hierarchies ? Purpose
What are Sets ? Purpose
What are Parameters ? Purpose and examples

Analytics & Dashboard

Reference Lines
Trend Line
Overview of Dashboard
- Tiled Vs Floating
- All Objects overview
- Layout overview
- Dashboard creation with formatting

Dashboard Actions & Tableau Public

Actions: Filter, Highlight, URL, Sheet, Parameter
Set How to save the workbook to Tableau Public website ?

Introduction To Mysql

Introduction to Databases
Introduction to RDBMS
Explain RDBMS through normalization
Different types of RDBMS
Software Installation(MySQL Workbench)

SQL Commands And Data Types

Types of SQL Commands (DDL,DML,DQL,DCL,TCL) and their applications Data Types in SQL (Numeric, Char, Datetime)

DQL & Operators

SELECT:LIMIT,DISTINCT,WHERE AND,OR,IN, NOT IN,BETWEEN, EXIST, ISNULL ,IS NOT NULL,Wild Cards, ORDER BY

Case When Then And Handling NULL Values

Usage of Case When then to solve logical problems and handling NULL Values (IFNULL, COALESCE)

Group Operations & Aggregate Functions

Group By, Having Clause. COUNT, SUM,AVG,MIN, MAX, COUNT String Functions, Date & Time Function

Constraints

NOT NULL
UNIQUE
CHECK
DEFAULT
ENUM
Primary key
Foreign Key (Both at column level and table level)

Joins

Inner
Left
Right
Cross
Self Joins
Full outer join

DDL Commands

DDL:
- Create
- Drop
- Alter
- Rename
- Truncate
- Modify
- Comment

DML & TCL Commands

DML
- Insert
- Update
- Delete
TCL
- Commit
- Rollback
- Savepoint and Data Partitioning

Indexes And Views

Indexes (Different Type of Indexes) and Views in SQL

Stored Procedure

Stored Procedures
- Procedure with IN Parameter
- Procedure with OUT parameter
- Procedure with INOUT parameter

Function, Constructs

User Define Function
Window Functions
- Rank
- Dense Rank
- Lead
- Lag Row_number

Union, Intersect, Sub-Query

Union
Union all
Intersect
Sub Queries
Multiple Query

Exception Handling, Loops, Cursor

Handling Exceptions in a query
CONTINUE Handler
EXIT handler
Loops: Simple, Repeat, While Cursor

Triggers

Triggers - Before | After DML Statement

Introduction To Python, Variables

Python Introduction
- Programing Cycle of Python
- Python IDE
- Variables
- Data type
- Number
- string
- List
- Tuple
- Dictionary

Operators

Operator
- Arthmatic
- Comparison
- Assignment
- Logical
- Bitwise opeartor

Conditional Statements And Loops

Decision making If
While loop
for loop and nested loop

Number Conversion,Functions

Mathametical functions
Random function
Trigonometric function
Number type conversion - int(), long(). Float ()

Strings

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

List And Tuples

Python List
- Accessing values in list
- Delete list elements
- Indexing slicing & Matrices
Built in Function
- cmp()
- len()
- min()
- max()
Tuples
- Accessing values in Tuples
- Delete Tuples elements
- Indexing slicing & Matrices
Built in tuples functions
- cmp()
- len ()

Dictionary

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

Function

Function
- Define function
- Calling function
- pass by refernece as value
- Function arguments
- Anonymous functions
- return statements
- Scope of variables - local & global

Modules

Import statemnts
Locating modules
current directory
Pythonpath Dir() function
global and location functions and reload () functions .
Packages in Python

Files

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

Directories And Exception Handling

mkdir methid
chdir () method
getcwd method
rm dir Exception handling
List of exceptions
Try and exception Try
finally clause
user defined exceptions

OOPS

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

Regular Expressions

match function
search function
matching vs searching
Regular exp modifiers
patterns

Framework

Introduction to Django framwork
overview
enviorment
Apps life cycle
creating views Application

Data Analysis Libraries

Numpy
Pandas
Matplotlib

Datascience Project Lifecycle

Demo:Introduction to Types of Analytics
Project Life Cycle
LMS walk through

Basic Stat

Data Types
Measure Of central tendency
Measures of Dispersion

Basic Stat Contd..

Graphical Techniques
Skewness & Kurtosis
Box Plot.

R And Basic Stat Contd..

R
R Studio
Descriptive Stats in R

Python

Python (Installation and basic commands)
Libraries
Jupyter note book
Set up Github
Descriptive Stats in Python
Pandas and Matplotlib

Basic Stat Contd..

Random Variable
Probability
Probility Distribution
Normal Distribution
SND
Expected Value
Sampling Funnel
Sampling Variation
Central Limit Theorem
Confidence interval
Assignments Session -1 (1 hr)
Introduction to Hypothesis Testing

Hypothesis Testing

Hypothesis Testing ( 2 proportion test, 2 t sample t test)
Anova and Chisquare

EDA

Exploratory data analysis-1(Data Cleaning, Imputation Techniques,Data analysis and Visualization(Scatter Diagram, Correlation Analysis,Tranformations )

Linear Regression

Priciples of Regression
Intro to Simple Linear Regression
Multiple Linear Regression

Logistic Regression

Logistic Regression

Model Deployment

Python Model Deployment

Assignment

Assignments Session-2(1 hr)
Clustering introduction
Hierarchical clustering

Data Mining : Unsupervised ML Algorithms

Kmeans
DBSCAN

DImensional Reduction Techniques

PCA
tSNE

Market Basket Analysis

Association Rules

Recommendation System And Assignment

Recomender System
Assignments Session-3 (1 hr)

Supervised Machine Learning

Supervised Machine Learning Concept(Regression Tasks/Classification Tasks)

Decision Tree

Decision Tree(C5.0)

EDA-2

EDA -2 ( Encoding Methods - OHE, Label Encoders,Outlier detection-Isolation Forest) and Calculating the Predictive Power Score (PPS)

Feautre Engineering

Feature Engineering (Tree based methods, RFE,PCA)

Modal Validation Techniques

Model Validation Methods (train-test,CV,Shuffle CV, and Accuracy methods)

Ensembled Techniques

Bagging and Random Forest,Boosting
XGBM,LGBM

Classifiers

KNN,Support Vector Machines

Regularization Techniques

Lasso and Ridge Regressions

Neural Network

ANN
Optimization Algorithm(Gradient descent)
stochastic gradient descent(intro)
Back Propagation method

Neural Network And Assignment

Introduction to CNN
Assignments Session-4 (1 hr)

Text Mining

Introduction to Text Mining
VSM
Intro to word embeddings
Word clouds and Doucument Similarity using cosine similarity
Named Entity Recognition

Naive Bayes

Text classification using Naïve Bayes
Emotion Mining

TIme Series

Introduction to Timeseries
Level
Trend and Sesonality
strategy
Scatter plot
Lag plot
ACF
Principles of Visualization
Naïve forecasts

Forecasting

Forecastin Error and it metrics
Model Based Approaches
Model Based approach cont
AR Model for errors
Data driven approaches
MA
Exp Smoothing
ARIMA
Survival Analysis

Project Discussion

End to End Data Science Project

Introduction To Machine Learning / Deep Learning / Artificial Intelligence (AI)

Understanding Machine Learning Fundamentals
Limitations of Machine Learning
Deep Learning
AI vs ML vs Generative AI

Python Programming

Python revision
Introduction to Tensorflow and Keras

Mathematics: Calculus And Vector Algebra

Optimization
Derivatives
Function
Scalar-Vector-Matrix
Vector Operation
Vector spaces
Probability

Machine Learning:(Linear And Logistic Regression)

Priciples of Regression and Classification Model
Assumptions
Model Evaluation Matrices

Machine Learning:(Segmentation)

K-Means & Hierarichal Clustering

Machine Learning:(Bagging)

Understanding Bagging Concepts
Decision Tree
Random Forest

Machine Learning:(Boosting)

Understanding Boosting Concepts
XGBM

Intro To Neural Network & Deep Learning

Understanding Human Brain's functionality
Gradient Descent Optimization

Neural Network Architecture

Neural Network Overview

Backward Propagation

Neural Network Processing
Backward Propogation

Parameters And Hyperparameters

Train
Test & Validation Set
Vanishing & Exploding Gradient
Dropout
Regularization
Bias correction
learning rate
Tuning
Softmax etc

Optimizers

Adam
Ada
AdaBoost
RMS Prop etc

Computer Vision

What is Computer Vision?
Read in Images in Python
Introduction to OpenCV
Basic Image Conversions
Image Resizing
Image Transformations
Contrast Stretching

CNN Architecture

Introduction to CNN architecture
Convolution
Feature detector
Padding
Stride
Activation function
Pooling
Training CNN
Loss function

Transfer Learning In Computer Vision

Object detection concepts
Bounding box
object detection models
pre-trained models
transfer learning
segmentation concepts

Advanced CNN

Advanced CNN models applications
face detection
RCNN
fast RCNN
faster RCNN
mask RCNN
YOLO

Basic NLP Concepts & Models

Text pre-processing
Binary weight
BoW
TF-IDF
Spam detection / Sentiments Analysis
Named Entity Recognition

Deep NLP (DNLP)

Working with word vectors
Word Embedding
Word2Vec
CBOW/Skip-gram

Forecasting Deep Learning -1

Idea behind RNN architecture
Vanishing/Exploding gradient problem

Forecasting Deep Learning -2

LSTM
GRU
RNN vs LSTM vs GRU
Training of RNN model

Seq2Seq Models

Sequential data analysis
seq2seq model
encoder-decoder architecture
application of seq2seq models

Advanced NLP Models

Idea behind transformer
architecture and analysis
pre-trainer models
Attention (Elmo, BERT , T5)
Self-Attention
Transformer Block
Multi-Head Attention
Encoder-Decoder Architecture

Generative AI

Generative AI using LLM's
Introduction to GPT
VAE
GANs

GANs And LLM

DCGAN - intuition
MNIST dataset
Building the generator
Building the discriminator
Loss (error) calculation
WGAN - intuition

Transfer Learning In NLP

pretrained models (GPT, BERT ,BART, T5) models with applications
Intro to Different types of Transformer encoder models- Basic BERT
RoBERTa
DistilBERT etc

Autoencoders

Basics of autoencoders
different types of autoencoders
applications with examples of autoencoders
variational autoencoders

AI Applications

Model evaluation
- ChatGPT
- Bing
- Bard

Speech Analytics

Introduction
Automated Speech Recognition
text to speech conversion
voice assistant devices

Reinforcement Learning-1

Intro to RL
Q learning concept examples
Q learning applications
exploration
exploitation

Reinforcement Learning-2

policy gradient concepts
Actor-critic methods
Proximal policy Optimization (PPO)
Work with deep RL libraries

Introduction To Generative AI

Overview of Generative AI
What is generative AI?
What's latest in GenAI?
Generative AI vs Traditional AI
Common Techniques of Generative AI
Evolution of LLM
Gans and LLM?
Use of GENERATIVE AI
Challenges in Generative AI
Understanding AI: Basics and Use Cases
Differentiating ML,DL, and AI
Domains of AI-ML
Use of AI-ML
Conclusion

Intro To Python

Why Python
Getting Started
First program
Keywords and Identifiers
Python Variables,
Constants and Literals
Python Data Types
Python Input
Output and Import
if...else Statement
Python Loop
Python Functions
Python Modules vs Package vs Libraries vs Frameworks

Machine Learning

Machine Learning Basics and Algorithms

Basics On NLP

What is NLP
History of NLP
NLP End to end workflow
Stopwords
Tokenization
Stemming
Lemmatization
POS tagging
TFIDF
One hot encoding
Bag of words
Unigram
Bigram
ngram
Word
embeddings
Skip Gram
Word2vec model

NLP Models

RNN
LSTM Models & GRU Models
Evolution in NLP

Advance NLP: Foundations Of Generative Models

Foundations of Generative Models & LLM
Encoder Models
Decoder Models
Encoder-decoder architecture
Attention mechanism
reseach paper walkthrough

Transformers

Transformers Models
Introduction
Natural Language Processing Transformers
what can they do?
How do Transformers work?
Load models and Use locally

Use Cases: Chatbot Creation

Real-world applications and case studies of LLMs Chatbot Creation

Finetuning LLM Using Transformers For Different Tasks

Fine-tuning a model process
LSTM vs GRU
Fine tuning Transformers model

Evolution Of GenAI

History Of Generative AI
Generative AI (GenAI): The evolution of creativity through technology

GenAI Models And Basic: GPT Models With Prompt Engineering

Chatgpt- GPT-2, GPT-3, GPT-3.5
LLAMA-2
OpenChat Model
Prompt Engineering
Hallucination in llm

Model Evaluation

Model evaluation
Entropy
Cross Entropy
Perplexity
Parameter efficient fine-tuning (PEFT)
LoRA
Soft prompts
ARC
Hellaswag
MMLU
TruthfulQA

Finetuning LLM LLAMA-2

Simple finetuning of LLM: LLAMA-2

Model Architectures In LLM

What is GGUF
What is GGML
Use of Open Source LLM on Local Machine
How to use pretrain LLM models using Ctransfromers
LM Studio

Generative Adversarial Networks (GANs)

Generative Adversarial Networks (GANs)
Image Generation
Pix2pix Gan
Dall-e and prompts in GenAI Images

Reinforcement Learning

Reinforcement Learning
Techniques of RL
OPENAI Gym
Reinforcement Learning
LLM Applications

Deployment Strategies, Hardware Requirements APIs, Dockers Etc

Deployment Strategies
Hardware Requirements
APIs
Dockers etc

Langchain

Langchain: A Framework for LLMs

Model Serving

Demo with FastAPI and Streamlit

LLM Operations, Scalability, Best Practices, Ethical Issues

LLM Operations
Scalability
Best Practices
Responsible AI
Google's Approach
Ethical Issues

Understanding Of AI, Intro To NLP

The nature of AI
Comparison of Descriptive AI and Generative AI
Core concepts of NLP
Basics of language understanding

Intro To GPT And ChatGPT, Fundamentals Of Prompt Engineering

What is GPT
Its evalution and generational changes
What is Prompt engineering
its importance,tpes of prompts

Content Generation With Prompts

Stratagies for generating text
music and video using prompt

Applications Of Prompt Engineering

Question-Answering systems
Conversational AI
Sentiment Analysis

Code Generation

GitHub Copilot Exploration

Image & Video Creation

Exploring the capabilities and limitations of DALL-E 2 and GPT-4

Project

Demonstrate mastery of course content with a comprehensive projects

Introduction To ChatGPT

History and Development of ChatGPT
Examples of ChatGPT use in various industries

Generative AI

Key concepts and principles of Generative AI
Examples of Generative AI models including ChatGPT
LamDA and Others

ChatGPT In Everyday Life

ChatGPT applications in everyday life such as writing
translation and creativity
Explore ChatGPT potential for Entertainment and Education

Coding Using ChatGPT, Customer Service

Practical coding tasks and automation using ChatGPT
Implementing ChatGPT as Virtual assistant or Chatbot for customer service

ChatGPT In Business

Utilizing ChatGPT for Content creation
Social media engagement and personalized marketing campaigns

ChatGPT Architectures

Overview of GPT
ChatGPT Capabilities
GPT Architecture
GPT-3 vs GPT-4
Advancements in GPT-4

Training And Optimization

Accessing Open AI API-API keys and Rate Limits
Conversion flows-GPT in Chatbots Testing and Iteration
Dataset preparation
Fine-tuning Techniques
Model Performance Monitoring

Prompt Engineering With ChatGPT

Writing Clear,Concise and Focused prompts to maximize ChatGPT's Performance

Introduction To Azure And MS Entra ID

Introduction to Cloud Computing
Azure
Configure Azure account

Administer Identity

Microsoft Entra ID, Configure User and Group Accounts

Administer Governance

Configure Subscriptions
Configure Role Based Access Control

Compliance And Administer Resource

Configure Azure Policy
Configure Azure Resources with Tools

Administer Virtual Networking

Introduction to Virtual Networking
Configure Virtual Networks, subnets
Configure Network Groups
Application security group
Configure
user defined routes
Configure Network Watcher
Azure Firewall

Administer Intersite Connectivity

Introduction to Vnet connectivity
Configure VNet Peering

Administer Network Traffic

Configure Azure Load Balancer,Configure Azure Application Gateway

Cloud & AWS

What is cloud ?
Services offered by different cloud providers
What is AWS ?

AWS Cloud Services

S3 Service
EC2 Instances
Databases
IAM - Identity Access Management
Billing Details
Machine Learning

S3 Service

What is AWS Storage ?
Storage architecture of S3 buckets
Concepts of Data Encryption

EC2 Instances

What is Region ?
What is availability Zones ?
Pre EC2 and EC2 instance Types
Procedure for launching AWS EC2 Instance

AWS Databases

Types Of Databases available in AWS
Introduction to Amazon RDS
Read Replicas in Amazon RDS
What is No SQL Database ?

IAM - Identity Access Management

Why access management
What is ARN Service (Amazon Resource Name), IAM features
What is IAM policies
IAM permision
IAM roles
identity federation

Amazon Virtual Private Cloud

Introduction to Amazon VPC
Amazon VPC Components
IP Addresses
Elastic Network Interface

Intro To Big Data Technologies

What is big data
characteristics of big data
technologies in big data etc.

Intro To Spark Environment

what is spark environment
spark documentation
installation of spark
spark concepts

Integration Of Spark Platform

Integration with different languages like python
r
scala, etc.
Introducing pyspark environment
pyspark basics and functions

Pyspark Concepts

Pyspark RDD structures
dataframe modules
sql modules
examples
exercise problems
working on datasets

Pyspark ML Concepts

Pyspak ML libraries
Regression models
linear and logistic regression and clustering basics
tree based models
ensemble concepts

Pyspark ML Applications

Pyspark ML applications
with excercises
visualizations

Databricks Environment

What is databricks
account creation
cluster creation
working on pyspark applications in databricks with r
python and scala

Intro To AWS Cloud

What is aws cloud
account creation
understanding basic aws enevironment and knowledge

Hadoop Environment

What is hadoop
hadoop architecture
creating hadoop environment on AWS cloud
install java
install hadoop and related concepts

Hadoop Applications

Running applications like map reduce on data
getting insights
doing analysis
word count problems etc.

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.