Kickstart your International journey with Berlin International University Of Applied Sciences

Post Graduate Program in Data Science, Artificial Intelligence, Business Analytics and Data Engineering

Become the First Generation Leader of the Data Science Revolution.

Program Accredited by Berlin International University Of Applied Sciences

& Global Certifications by in

  • Data Science
  • Artificial Intelligence
  • Data Analytics
  • Data Engineering
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Online-Live

Format

Duration

12 Months

September 11, 2021

Start Date

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PROGRAM OVERVIEW

Through this 12-month-long Virtual Online Live Postgraduate program, learners can become proficient Data Science professionals by mastering Data Visualization, Exploratory Data Analysis, Artificial Intelligence & Neural Networks and implementing Big Data techniques using tools, like R, Excel, Tableau, SQL, NoSQL, Hadoop, and more. Certified by Berlin International University Of Applied Sciences, with industry partner Vepsun, this immersive, guaranteed placement assistance. The program is taught by the best minds in the industry where students get hands-on learning experience in Data Science labs that are equipped with the latest analytics software & applications.

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ABOUT Berlin UNIVERSITY

  • Students from 73 nations are studying at Berlin International University of Applied Sciences. Berlin International is part of a global network that allows students to travel and pursue their studies in different locations.
  • Berlin International facts
    • 85 different nationalities are studying and teaching at Berlin International
    • 68% of our students are international and from four continents
    • 84% of our teaching staff have studied or worked at international universities
    • 100% personal contact to Professors and Lecturers
  • BAU Global Network. BAU Global operates at 3 continents in 7 countries and includes 5 universities - Istanbul, Washington, DC, Berlin, Cyprus and Batumi, 2 language schools and 5 academic centers.
  • These students come from about 60, our lecturers from 15 different countries.
  • The BAU university was founded in 2014 and is already one of the top universities in Germany.
  • Over 1000 corporate collaborations in Germany, including Allianz, AOK, Bertelsmann, BP, Deutsche Telekom, Ford, IBM, City of Munich, Peek & Cloppenburg Vienna, Siemens, thyssenkrupp.
FOM

ABOUT VEPSUN

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

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

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

Syllabus

Python

INTRODUCTION TO PYTHON

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

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

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

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

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

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

Statistics and EDA

EXPLORATORY DATA ANALYSIS

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

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

a) Reading and Getting Data into R

b) Data Objects-Data Types & Data Structure.

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

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

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

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

g) Interactive reporting with R markdown

INVESTMENT ASSIGNMENT

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

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

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

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

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

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

Machine Learning 1

LINEAR REGRESSION

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

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

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

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

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

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

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

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

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

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

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

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

Machine Learning 2

BOOSTING

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

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

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

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

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

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

Deep Learning

INTRODUCTION TO NEURAL NETWORKS

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

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

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

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

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

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

Azure Machine Learning (DP 100)

CREATE MACHINE LEARNING MODELS

CREATE MACHINE LEARNING MODELS

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

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

1. Wine Classification

2. Real Estate Regression

3. Safari CNN using TensorFlow

Project 1:

File Handling with Python (Management System)

Project 2:

Predictive Modelling with a real-time problem statement choose by Student.

Natural Language Processing

LEXICAL PROCESSING

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

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

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

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

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

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

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

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

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

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

Big Data Technologies

Introduction

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

Hadoop:

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

Hadoop Environment:

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

Introduction to HIVE,

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

HBase:

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

Apache Spark APIs for large-scale data processing:

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

Tableau For Data Visualization

Data warehousing Concepts

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

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

Power BI

Module 1: Get Started with Microsoft Data Analytics

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

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

After completing this module, you will be able to:

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

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

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

After completing this module, you will be able to:

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

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

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

After completing this module, students will be able to:

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

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

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

After completing this module, you will be able to:

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

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

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

After completing this module, you will be able to:

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

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

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

After completing this module, you will be able to:

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

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

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

After completing this module, you will be able to:

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

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

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

After completing this module, students will be able to:

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

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

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

After completing this module, you will be able to:

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

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

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

After completing this module, you will be able to:

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

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

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

After completing this module, you will be able to:

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

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

Lessons
  • Parameters
  • Datasets

After completing this module, you will be able to:

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

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

Lessons
  • Security in Power BI

After completing this module, you will be able to:

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

Data Engineering on Microsoft

Module 1: Explore compute and storage options for data engineering workloads

This module provides an overview of the Azure compute and storage technology options that are available to data engineers building analytical workloads. This module teaches ways to structure the data lake, and to optimize the files for exploration, streaming, and batch workloads. The student will learn how to organize the data lake into levels of data refinement as they transform files through batch and stream processing. Then they will learn how to create indexes on their datasets, such as CSV, JSON, and Parquet files, and use them for potential query and workload acceleration.

  • Introduction to Azure Synapse Analytics
  • Describe Azure Databricks
  • Introduction to Azure Data Lake storage
  • Describe Delta Lake architecture
  • Work with data streams by using Azure Stream Analytics
Lab : Explore compute and storage options for data engineering workloads
  • Combine streaming and batch processing with a single pipeline
  • Organize the data lake into levels of file transformation
  • Index data lake storage for query and workload acceleration
After completing this module, students will be able to:
  • Describe Azure Synapse Analytics
  • Describe Azure Databricks
  • Describe Azure Data Lake storage
  • Describe Delta Lake architecture
  • Describe Azure Stream Analytics
Module 2: Design and implement the serving layer

This module teaches how to design and implement data stores in a modern data warehouse to optimize analytical workloads. The student will learn how to design a multidimensional schema to store fact and dimension data. Then the student will learn how to populate slowly changing dimensions through incremental data loading from Azure Data Factory.

  • Design a multidimensional schema to optimize analytical workloads
  • Code-free transformation at scale with Azure Data Factory
  • Populate slowly changing dimensions in Azure Synapse Analytics pipelines
Lab : Designing and Implementing the Serving Layer
  • Design a star schema for analytical workloads
  • Populate slowly changing dimensions with Azure Data Factory and mapping data flows
After completing this module, students will be able to:
  • Design a star schema for analytical workloads
  • Populate a slowly changing dimensions with Azure Data Factory and mapping data flows
Module 3: Data engineering considerations for source files

This module explores data engineering considerations that are common when loading data into a modern data warehouse analytical from files stored in an Azure Data Lake, and understanding the security consideration associated with storing files stored in the data lake.

  • Design a Modern Data Warehouse using Azure Synapse Analytics
  • Secure a data warehouse in Azure Synapse Analytics
Lab : Data engineering considerations
  • Managing files in an Azure data lake
  • Securing files stored in an Azure data lake
After completing this module, students will be able to:
  • Design a Modern Data Warehouse using Azure Synapse Analytics
  • Secure a data warehouse in Azure Synapse Analytics
Module 4: Run interactive queries using Azure Synapse Analytics serverless SQL pools

In this module, students will learn how to work with files stored in the data lake and external file sources, through T-SQL statements executed by a serverless SQL pool in Azure Synapse Analytics. Students will query Parquet files stored in a data lake, as well as CSV files stored in an external data store. Next, they will create Azure Active Directory security groups and enforce access to files in the data lake through Role-Based Access Control (RBAC) and Access Control Lists (ACLs).

  • Explore Azure Synapse serverless SQL pools capabilities
  • Query data in the lake using Azure Synapse serverless SQL pools
  • Create metadata objects in Azure Synapse serverless SQL pools
  • Secure data and manage users in Azure Synapse serverless SQL pools
Lab : Run interactive queries using serverless SQL pools
  • Query Parquet data with serverless SQL pools
  • Create external tables for Parquet and CSV files
  • Create views with serverless SQL pools
  • Secure access to data in a data lake when using serverless SQL pools
  • Configure data lake security using Role-Based Access Control (RBAC) and Access Control List
After completing this module, students will be able to:
  • Understand Azure Synapse serverless SQL pools capabilities
  • Query data in the lake using Azure Synapse serverless SQL pools
  • Create metadata objects in Azure Synapse serverless SQL pools
  • Secure data and manage users in Azure Synapse serverless SQL pools
Module 5: Explore, transform, and load data into the Data Warehouse using Apache Spark

This module teaches how to explore data stored in a data lake, transform the data, and load data into a relational data store. The student will explore Parquet and JSON files and use techniques to query and transform JSON files with hierarchical structures. Then the student will use Apache Spark to load data into the data warehouse and join Parquet data in the data lake with data in the dedicated SQL pool.

  • Understand big data engineering with Apache Spark in Azure Synapse Analytics
  • Ingest data with Apache Spark notebooks in Azure Synapse Analytics
  • Transform data with DataFrames in Apache Spark Pools in Azure Synapse Analytics
  • Integrate SQL and Apache Spark pools in Azure Synapse Analytics
Lab : Explore, transform, and load data into the Data Warehouse using Apache Spark
  • Perform Data Exploration in Synapse Studio
  • Ingest data with Spark notebooks in Azure Synapse Analytics
  • Transform data with DataFrames in Spark pools in Azure Synapse Analytics
  • Integrate SQL and Spark pools in Azure Synapse Analytics
After completing this module, students will be able to:
  • Describe big data engineering with Apache Spark in Azure Synapse Analytics
  • Ingest data with Apache Spark notebooks in Azure Synapse Analytics
  • Transform data with DataFrames in Apache Spark Pools in Azure Synapse Analytics
  • Integrate SQL and Apache Spark pools in Azure Synapse Analytics
Module 6: Data exploration and transformation in Azure Databricks

This module teaches how to use various Apache Spark DataFrame methods to explore and transform data in Azure Databricks. The student will learn how to perform standard DataFrame methods to explore and transform data. They will also learn how to perform more advanced tasks, such as removing duplicate data, manipulate date/time values, rename columns, and aggregate data.

  • Describe Azure Databricks
  • Read and write data in Azure Databricks
  • Work with DataFrames in Azure Databricks
  • Work with DataFrames advanced methods in Azure Databricks
Lab : Data Exploration and Transformation in Azure Databricks
  • Use DataFrames in Azure Databricks to explore and filter data
  • Cache a DataFrame for faster subsequent queries
  • Remove duplicate data  Manipulate date/time values
  • Remove and rename DataFrame columns
  • Aggregate data stored in a DataFrame
After completing this module, students will be able to:
  • Describe Azure Databricks
  • Read and write data in Azure Databricks
  • Work with DataFrames in Azure Databricks
  • Work with DataFrames advanced methods in Azure Databricks
Module 7: Ingest and load data into the data warehouse

This module teaches students how to ingest data into the data warehouse through T-SQL scripts and Synapse Analytics integration pipelines. The student will learn how to load data into Synapse dedicated SQL pools with PolyBase and COPY using T-SQL. The student will also learn how to use workload management along with a Copy activity in a Azure Synapse pipeline for petabyte-scale data ingestion.

  • Use data loading best practices in Azure Synapse Analytics
  • Petabyte-scale ingestion with Azure Data Factory
Lab : Ingest and load Data into the Data Warehouse
  • Perform petabyte-scale ingestion with Azure Synapse Pipelines
  • Import data with PolyBase and COPY using T-SQL
  • Use data loading best practices in Azure Synapse Analytics
After completing this module, students will be able to:
  • Use data loading best practices in Azure Synapse Analytics
  • Petabyte-scale ingestion with Azure Data Factory
Module 8: Transform data with Azure Data Factory or Azure Synapse Pipelines

This module teaches students how to build data integration pipelines to ingest from multiple data sources, transform data using mapping data flowss, and perform data movement into one or more data sinks.

  • Data integration with Azure Data Factory or Azure Synapse Pipelines
  • Code-free transformation at scale with Azure Data Factory or Azure Synapse Pipelines
Lab : Transform Data with Azure Data Factory or Azure Synapse Pipelines
  • Execute code-free transformations at scale with Azure Synapse Pipelines
  • Create data pipeline to import poorly formatted CSV files
  • Create Mapping Data Flows
After completing this module, students will be able to:
  • Perform data integration with Azure Data Factory
  • Perform code-free transformation at scale with Azure Data Factory
Module 9: Orchestrate data movement and transformation in Azure Synapse Pipelines

In this module, you will learn how to create linked services, and orchestrate data movement and transformation using notebooks in Azure Synapse Pipelines.

  • Orchestrate data movement and transformation in Azure Data Factory
Lab : Orchestrate data movement and transformation in Azure Synapse Pipelines
  • Integrate Data from Notebooks with Azure Data Factory or Azure Synapse Pipelines
After completing this module, students will be able to:
  • Orchestrate data movement and transformation in Azure Synapse Pipelines
Module 10: Optimize query performance with dedicated SQL pools in Azure Synapse

In this module, students will learn strategies to optimize data storage and processing when using dedicated SQL pools in Azure Synapse Analytics. The student will know how to use developer features, such as windowing and HyperLogLog functions, use data loading best practices, and optimize and improve query performance.

  • Optimize data warehouse query performance in Azure Synapse Analytics
  • Understand data warehouse developer features of Azure Synapse Analytics
Lab : Optimize Query Performance with Dedicated SQL Pools in Azure Synapse
  • Understand developer features of Azure Synapse Analytics
  • Optimize data warehouse query performance in Azure Synapse Analytics
  • Improve query performance
After completing this module, students will be able to:
  • Optimize data warehouse query performance in Azure Synapse Analytics
  • Understand data warehouse developer features of Azure Synapse Analytics
Module 11: Analyze and Optimize Data Warehouse Storage

In this module, students will learn how to analyze then optimize the data storage of the Azure Synapse dedicated SQL pools. The student will know techniques to understand table space usage and column store storage details. Next the student will know how to compare storage requirements between identical tables that use different data types. Finally, the student will observe the impact materialized views have when executed in place of complex queries and learn how to avoid extensive logging by optimizing delete operations.

  • Analyze and optimize data warehouse storage in Azure Synapse Analytics
Lab : Analyze and Optimize Data Warehouse Storage
  • Check for skewed data and space usage
  • Understand column store storage details
  • Study the impact of materialized views
  • Explore rules for minimally logged operations
After completing this module, students will be able to:
  • Analyze and optimize data warehouse storage in Azure Synapse Analytics
Module 12: Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link

In this module, students will learn how Azure Synapse Link enables seamless connectivity of an Azure Cosmos DB account to a Synapse workspace. The student will understand how to enable and configure Synapse link, then how to query the Azure Cosmos DB analytical store using Apache Spark and SQL serverless.

  • Design hybrid transactional and analytical processing using Azure Synapse Analytics
  • Configure Azure Synapse Link with Azure Cosmos DB
  • Query Azure Cosmos DB with Apache Spark pools
  • Query Azure Cosmos DB with serverless SQL pools
Lab : Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link
  • Configure Azure Synapse Link with Azure Cosmos DB
  • Query Azure Cosmos DB with Apache Spark for Synapse Analytics
  • Query Azure Cosmos DB with serverless SQL pool for Azure Synapse Analytics
After completing this module, students will be able to:
  • Design hybrid transactional and analytical processing using Azure Synapse Analytics
  • Configure Azure Synapse Link with Azure Cosmos DB
  • Query Azure Cosmos DB with Apache Spark for Azure Synapse Analytics
  • Query Azure Cosmos DB with SQL serverless for Azure Synapse Analytics
Module 13: End-to-end security with Azure Synapse Analytics

In this module, students will learn how to secure a Synapse Analytics workspace and its supporting infrastructure. The student will observe the SQL Active Directory Admin, manage IP firewall rules, manage secrets with Azure Key Vault and access those secrets through a Key Vault linked service and pipeline activities. The student will understand how to implement column-level security, row-level security, and dynamic data masking when using dedicated SQL pools.

  • Secure a data warehouse in Azure Synapse Analytics
  • Configure and manage secrets in Azure Key Vault
  • Implement compliance controls for sensitive data
Lab : End-to-end security with Azure Synapse Analytics
  • Secure Azure Synapse Analytics supporting infrastructure
  • Secure the Azure Synapse Analytics workspace and managed services
  • Secure Azure Synapse Analytics workspace data
After completing this module, students will be able to:
  • Secure a data warehouse in Azure Synapse Analytics
  • Configure and manage secrets in Azure Key Vault
  • Implement compliance controls for sensitive data
Module 14: Real-time Stream Processing with Stream Analytics

In this module, students will learn how to process streaming data with Azure Stream Analytics. The student will ingest vehicle telemetry data into Event Hubs, then process that data in real time, using various windowing functions in Azure Stream Analytics. They will output the data to Azure Synapse Analytics. Finally, the student will learn how to scale the Stream Analytics job to increase throughput.

  • Enable reliable messaging for Big Data applications using Azure Event Hubs
  • Work with data streams by using Azure Stream Analytics
  • Ingest data streams with Azure Stream Analytics
Lab : Real-time Stream Processing with Stream Analytics
  • Use Stream Analytics to process real-time data from Event Hubs
  • Use Stream Analytics windowing functions to build aggregates and output to Synapse Analytics
  • Scale the Azure Stream Analytics job to increase throughput through partitioning
  • Repartition the stream input to optimize parallelization
After completing this module, students will be able to:
  • Enable reliable messaging for Big Data applications using Azure Event Hubs
  • Work with data streams by using Azure Stream Analytics
  • Ingest data streams with Azure Stream Analytics
Module 15: Create a Stream Processing Solution with Event Hubs and Azure Databricks

In this module, students will learn how to ingest and process streaming data at scale with Event Hubs and Spark Structured Streaming in Azure Databricks. The student will learn the key features and uses of Structured Streaming. The student will implement sliding windows to aggregate over chunks of data and apply watermarking to remove stale data. Finally, the student will connect to Event Hubs to read and write streams.

  • Process streaming data with Azure Databricks structured streaming
Lab : Create a Stream Processing Solution with Event Hubs and Azure Databricks
  • Explore key features and uses of Structured Streaming
  • Stream data from a file and write it out to a distributed file system
  • Use sliding windows to aggregate over chunks of data rather than all data
  • Apply watermarking to remove stale data
  • Connect to Event Hubs read and write streams
After completing this module, students will be able to:
  • Process streaming data with Azure Databricks structured streaming
Module 16: Build reports using Power BI integration with Azure Synapase Analytics

In this module, the student will learn how to integrate Power BI with their Synapse workspace to build reports in Power BI. The student will create a new data source and Power BI report in Synapse Studio. Then the student will learn how to improve query performance with materialized views and result-set caching. Finally, the student will explore the data lake with serverless SQL pools and create visualizations against that data in Power BI.

  • Create reports with Power BI using its integration with Azure Synapse Analytics
Lab : Build reports using Power BI integration with Azure Synapase Analytics
  • Integrate an Azure Synapse workspace and Power BI
  • Optimize integration with Power BI
  • Improve query performance with materialized views and result-set caching
  • Visualize data with SQL serverless and create a Power BI report
After completing this module, students will be able to:
  • Create reports with Power BI using its integration with Azure Synapse Analytics
Module 17: Perform Integrated Machine Learning Processes in Azure Synapse Analytics

This module explores the integrated, end-to-end Azure Machine Learning and Azure Cognitive Services experience in Azure Synapse Analytics. You will learn how to connect an Azure Synapse Analytics workspace to an Azure Machine Learning workspace using a Linked Service and then trigger an Automated ML experiment that uses data from a Spark table. You will also learn how to use trained models from Azure Machine Learning or Azure Cognitive Services to enrich data in a SQL pool table and then serve prediction results using Power BI.

  • Use the integrated machine learning process in Azure Synapse Analytics
Lab : Perform Integrated Machine Learning Processes in Azure Synapse Analytics
  • Create an Azure Machine Learning linked service
  • Trigger an Auto ML experiment using data from a Spark table
  • Enrich data using trained models
  • Serve prediction results using Power BI
After completing this module, students will be able to:
  • Use the integrated machine learning process in Azure Synapse Analytics
Capstone:

1. Scrape Real-Estate Properties with Python and create a dashboard with it.

2. Focus On Analytics With Stack Overflow Data.

Project 1:

Building Chatbot using Dialog Flow

Project 2:

Real-time Dashboard Designing.

Certifications

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Certification by Vepsun & Berlin International University Of Applied Sciences

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Certification by Microsoft

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Certification by Microsoft

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Certification by Microsoft

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Certification by Microsoft

143000

Happy Clients Our success is Measured by Results.

572000

Projects- Our focus in on Delivering a better content.

12

Years of experience In Imparting Quality Training across Verticals.

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Students Placed in Top MNC's

Vepsun Career Assist

WHAT IS CAREER ASSIST?

Career Assist is an integration between Vepsun Technologies and hirist.com to provide career assistance to improve the job search experience & bring you closer to your desired job. With Career Assist, you get a chance to put the gained knowledge towards creating a positive, lifelong impact and move forward into the future with a great deal of opportunities in the area of your interest.

As a part of Career Assist, you will get the Spotlight & Pro-Features for a time period of 6 months. This upgrade on your profile will not only improve your job search experience but also give you multiple benefits like Spotlight and Pro Features.

Platforms Covered

aws-ec2

Jupyter Notebooks

These notebooks are widely used for coding in Python.

AwsRDsorignal

Python

This is one of the most dominant languages for data science in the industry today because of its ease, flexibility, open-source nature. It has gained rapid popularity and acceptance in the ML community.

DB

Numpy

It is Numerical computing tool

Vpc

Pandas

It is data processing and data manipulation tool

route53

Matplotlib

It is data visualization tool.

Ebs

Scikit learn

Simple and efficient tools for predictive data analysis. It features various Classification, Regression and clustering algorithms .

Vpc

TensorFlow

It is easily the most widely used tool in the industry today. Google might have something to do with that!

route53

PyTorch

This super flexible deep learning framework is giving major competition to TensorFlow. PyTorch has recently come into the limelight and was developed by researchers at Facebook

Ebs

Keras

It is used extensively for building deep learning applications

route53

NLTK and Textblob

It is used for natural language processing

Ebs

Azure Machine Learning

It is a platform for operating machine learning workloads in the cloud. Azure Machine Learning enables you to manage:

  • Scalable on-demand compute for machine learning workloads.
  • Data storage and connectivity to ingest data from a wide range sources.
  • Machine learning workflow orchestration to automate model training, deployment, and management processes.
  • Model registration and management, so you can track multiple versions of models and the data on which they were trained.
  • Metrics and monitoring for training experiments, datasets, and published services.
  • Model deployment for real-time and batch inferencing

Program Fee

Post Graduate Program in Data Science, Artificial Intelligence, Business Analytics and Data Engineering

Berlin International University Of Applied Sciences

  • Global Certifications by Microsoft in Data Science, Artificial Intelligence, Data Analytics & Data Engineering
  • Easy Loan Facilitation
Program Fee

INR. 2,50,000 + GST

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Frequently Asked Questions

What is Data Science?

Data science is a broad field that refers to the collective processes, theories, concepts, tools and technologies that enable the review, analysis and extraction of valuable knowledge and information from raw data. It is geared toward helping individuals and organizations make better decisions from stored, consumed and managed data. Data science is formerly known as datalogy.

What is the difference between supervised and unsupervised machine learning?

Supervised Machine learning: Supervised learning is the learning of the model where with input variable ( say, x) and an output variable (say, Y) and an algorithm to map the input to the output. That is, Y = f(X).

Unsupervised Machine learning: Unsupervised learning is where only the input data (say, X) is present and no corresponding output variable is there.

What is pruning in Decision Tree ?

When we remove sub-nodes of a decision node, this process is called pruning or opposite process of splitting.

What is Random Forest?

Random forest is a versatile machine learning method capable of performing both regression and classification tasks. It is also used for dimentionality reduction, treats missing values, outlier values. It is a type of ensemble learning method, where a group of weak models combine to form a powerful model.

What is deep learning?

Deep learning is a machine learning technique. It teaches a computer to filter inputs through layers to learn how to predict and classify information. Observations can be in the form of images, text, or sound.

What is artificial intelligence?

AI can be described as an area of computer science that simulates human intelligence in machines. It’s about smart algorithms making decisions based on the available data. Whether it’s Amazon’s Alexa or a self-driving car, the goal is to mimic human intelligence at lightning speed (and with a reduced rate of error).

What are intelligent agents?

An intelligent agent is an autonomous entity that leverages sensors to understand a situation and make decisions. It can also use actuators to perform both simple and complex tasks. In the beginning, it might not be so great at performing a task, but it will improve over time. The Roomba vacuum cleaner is an excellent example of this.

What’s the most popular programming language used in AI?

The open-source modular programming language Python leads the AI industry because of its simplicity and predictable coding behavior. It's popularity can be attributed to open-source libraries like Matplotlib and NumPy, efficient frameworks such as Scikit-learn, and practical version libraries like Tensorflow and VTK.

What are AI neural networks?

Neural networks in AI mathematically model how the human brain works. This approach enables the machine to think and learn as humans do. This is how smart technology today recognizes speech, objects, and more.

What’s a Turing test?

The Turing test, named after Alan Turing, is a method of testing a machine’s human-level intelligence. For example, in a human-versus-machine scenario, a judge will be tasked with identifying which terminal was occupied by a human and which was occupied by a computer based on individual performance. Whenever a computer can pass off as a human, it’s deemed intelligent. The game has since evolved, but the premise remains the same.

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