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Master in Data and Cloud Technologies (MDC) | Technogeeks

    Technology Master Program in data & technologies Syllabus

    Technology Master Program in Data science, Cloud Computing, Bigdata, Machine Learning, Deep learning & NLP

    In the master’s program, we cover several components related to development, cloud and data related technologies. We start from any of these three mentioned sections to begin the course and let candidates feel comfortable with any of these three fields first before candidate work on any industry standard based problem statements.

    Python Programming Section - A

    Module 01 – Introduction To Python

    • What is Python and brief history
    • Why Python and who use Python
    • Discussion on Python 2 and 3
    • Unique features of Python Discussion on various IDE’s
    • Demonstration of practical use cases
    • Python use cases using data analysis

    Module 02 – Setting up and Installations

    • Installing Python
    • Setting up Python environment for development Installation of Jupyter Notebook
    • How to access our course material using
    • Jupyter Write your first program in python
    • Python code deployment and execution on cloud using Google Colab

    Module 03 – Python Object And Data Structures Operations

    • Introduction to Python objects Python built-in functions
    • Number objects and operations
    • Formatting with strings
    • List objects and operations
    • Tuple objects and operations
    • Dictionary objects and operations Sets and Boolean
    • Object and data structures assessment test

    Module 04 – Python Statements

    • Introduction to Python statements
    • If, elif and else statements
    • Comparison operators
    • Chained comparison operators
    • What are loops
    • For loops
    • While loops
    • Useful operators
    • List comprehensions
    • Statement assessment test
    • Game challenge

    Module 05 - UDF Functions And Methods
    • Methods
    • What are various types of functions
    • Creating and calling user defined functions
    • Function practice exercises
    • Lambda Expressions
    • Map and Filter
    • Nested statements and scope
    • Args and kwargs
    • Functions and methods assignment

    Module 06 - File And Exception Handling

    • Process files using python
    • Read/write and append file object
    • File functions
    • File pointer and operations
    • Introduction to error handling
    • Try, except and finally

    Module 07 – Python Modules And Packages Python Inbuilt Modules
    • Python inbuilt modules
    • Creating UDM-User-defined modules Passing command-line arguments
    • Writing packages
    • Define PYTHONPATH
    • __name__ and __main__ in python

    Module 08 – Object-Oriented Programming (OOP) in Python
    • Object oriented features
    • Implement object oriented with Python
    • Creating classes and objects
    • Creating class attributes
    • Creating methods in a class
    • Inheritance
    • Polymorphism
    • Special methods for class
    • Assignment - Creating a python script to replicate deposits and withdrawals in a bank with appropriate classes and UDFs

    Module 9 – Advanced Python Modules
    • Collections module
    • Datetime
    • Python debugger
    • Timing your code
    • Regular expressions
    • StringIO
    • Python decorators
    • Python generators

    Module 10 – Package Installation

    • Install packages on python
    • Introduction to pip, easy install

    SQL Section – B

    MODULE 01 – Introduction to SQL Language

    • Introduction to SQL
    • Need of SQL
    • Introduction to RDBMS
    • Need of SQL for RDBMS
    • Real life examples where SQL is used

    Module 02 – Data Definition Language (DDL)

    • Introduction to DDL
    • DDL Create clause
    • DDL Drop clause
    • DDL alter clause
    • Data types
    • How to create a table
    • How to alter table
    • How to drop table

    Module 03 – Data Manipulation Language (DML)

    • Introduction To DML
    • Insert Clause
    • Update Clause
    • Delete Clause
    • How To Work On Bulk Insert, Update, Delete

    Module 04 – Data Retrieval Language (DRL)

    • Select Clause
    • Select Clause Multiple Variants With Keywords And Clauses
    • Real-life Queries Example

    Module 05 – Transaction Control Language (TCL)

    • Need of TCL
    • Commit
    • Rollback
    • Best Practices

    Module 06 – CRUD operations

    • Scenarios based approach to perform CRUD operations
    • Need of CRUD operations in projects

    Module 07 – Python And SQL Integration

    • Introduction to Flask
    • Decorators
    • SQL and Python Integration
    • REST API
    • Postman

    Module 08 – SQL And Python Based Project Use Cases

    • Use cases explanation
    • Problem understanding
    • Tools and frameworks require to solve problem statement
    • Development and unit testing
    • Q & A

    Data Analytics Section – C

    Module 01 – Data Analysis With Python

    • Introduction to data analysis
    • Why Data analysis?
    • Data analysis and Artificial bridge
    • Introduction to Data Analysis libraries
    • Data analysis introduction assignment challenge

    Module 02 – Data Analysis Using Numpy

    • Introduction to Numpy arrays
    • Creating and applying functions
    • Numpy Indexing and selection
    • Numpy Operations
    • Exercise and assignment challenge

    Module 03 – Pandas And Advanced Analysis

    • Pandas series
    • Introduction to DataFrames Missing data
    • Groupby
    • Merging, joining and Concatenating Operations
    • Data Input and Output
    • Pandas in-depth coding exercises

    Data Visualisation Section – D

    Module 01 – Data Visualization With Python

    Matplotlib Library

    • Plotting using Matplotlib
    • Plotting Numpy arrays
    • Plotting using object-oriented approach
    • Subplots using matplotlib
    • Matplotlib attributes and functions
    • Matplotlib exercises

    Seaborn Visualization Library

    • Categorical Plot using Seaborn
    • Distributional plots using Seaborn
    • Matrix plots
    • Grids
    • Seaborn exercises

    Data Visualization Using Tableau

    • Need of Tableau
    • Comparison between tableau and Programming based data visualization
    • Types of data sources supported by Tableau for report development
    • How to build charts in Tableau
    • How to build report and Dashboard in Tableau
    • Data visualization using Tableau features

    Data Science (Machine Learning, NLP, Deep Learning) Section – E

    Module 01- Machine Learning Algorithms

    Linear Regression with Python

    • Introduction to Regression
    • Exercise on Linear Regression using Sci-kit Learn Library Project on Linear regression using USA_HOUSING data
    • Evaluation of Linear regression using python visualizations
    • Practice project for Linear regression using advertisement data set to predict appropriate advertisements for users

    Logistic Regression

    • Introduction to Logistic Regression
    • Data set preprocessing using python libraries
    • Data Prediction using Logistic Regression

    Module 03- Machine Learning Algorithm KNN

    • K- Nearest neighbors using Python
    • Exercise on K- Nearest neighbors using Sci-kit Learn Library
    • Project on Logistic regression using Dogs and horses’ dataset getting the correct number of clusters
    • Evaluation of model using confusion matrix and classification report Standard scaling problem
    • Practice project on KNN algorithm

    Module 04 - Decision tree and Random forest with python

    • Intuition behind Decision trees
    • Implementation of decision tree using a real time dataset Ensemble learning
    • Decision tree and random forest for regression
    • Decision tree and random forest for classification
    • Evaluation of the decision tree and random forest using different methods
    • Practice project on decision tree and random forest using social network data to predict if someone will purchase an item or not

    Module 05 - Support Vector Machine(SVM)

    • Linearly separable data
    • Non-linearly separable data
    • SVM project with telecom dataset to predict the users portability

    Module 06 - K-means clustering

    • Clustering in unsupervised learning
    • K-means clustering intuition
    • Implementation of K-means with Python using Mall customer’s data to implement clusters on the basis of spending and income
    • Hierarchical clustering intuition
    • Implementation of Hierarchical clustering with python

    Module 07 - Apriori algorithm

    • Apriori theory and explanation Market basket analysis
    • Implementation of Apriori
    • Evaluation of association learning

    Module 08 - Natural Language Processing (NLP)

    • Introduction to Natural Language processing
    • NLTK Python library
    • Exercise on NLTK
    • Natural data mining
    • Data processing using stemming
    • Data processing using stop words
    • Significance of pattern matching

    MODULE 09- Deep Learning

    • Neural Network and Deep Learning
    • What is TensorFlow
    • TensorFlow examples
    • TensorFlow Exercise
    • What is Keras
    • Keras exercise
    • Pipeline implementation using Keras

    MODULE 10- Project Implementation

    • Project Implementation using Python and Data Science libraries

    Bigdata Hadoop Section – F

    Module 01 - Introduction To Hadoop

    • Hadoop- Demo
    • What is Bigdata
    • When data becomes Bigdata
    • 3V’s of Bigdata
    • Introduction to Hadoop Ecosystem
    • Why Hadoop? If Existing Tools and Technologies are there in the market for decades?
    • How Hadoop is getting two categories Projects- New projects on Hadoop
    • Clients want POC and migration of Existing tools and Technologies on Hadoop
    • Clients want POC and migration of Existing tools and Technologies on Hadoop Technology
    • How Open Source tool (HADOOP) is capable to run jobs in lesser time which take longer time in other tools in the market.
    • Hadoop Processing Framework (Map Reduce) / YARN
    • Alternates of Map Reduce
    • Why NoSQL is in more demand nowadays
    • Distributed warehouse for DFS
    • Most demanding tools which can run on the top of Hadoop Ecosystem for specific requirements in specific scenarios
    • Data import/Export tools

    Module 2 - Hadoop Setup Installation And HDFS Basics

    • Hadoop installation
    • Introduction to Hadoop FS and Processing Environment’s UIs
    • How to read and write files
    • Basic Unix commands for Hadoop
    • Hadoop’s FS shell
    • Hadoop’s releases
    • Hadoop’s daemons

    Module 03 - Hive Basic, Hive Advanced

    • Hive Introduction
    • Hive Advanced
    • Partitioning
    • Bucketing
    • External Tables
    • Complex Use cases in Hive
    • Hive Advanced Assignment
    • Real-time scenarios of Hive

    Module 04 - Data Ingestion Using Sqoop

    • Need of Sqoop
    • Data ingestion from RDBMS in HDFS using Sqoop
    • Data ingestion from RDBM in Hive table using Sqoop
    • Different types of ingestion techniques

    Module 05 - Spark And Python

    • Introduction to Spark
    • Introduction to Python
    • Pyspark concepts
    • Advantages of Spark over Hadoop
    • Is Spark a replacement for Hadoop?
    • How Spark is Faster than Hadoop
    • Spark RDD
    • Spark Transformation and Actions
    • Spark SQL
    • Datasets and Data Frames
    • Real-time scenarios examples of Spark where we prefer Spark over Hadoop
    • How Spark is capable to process complex data sets in lesser time
    • In-Memory Processing Framework for Analytics
    • Data Science on the top of Hadoop

    Module 06 - NoSQL using HBase

    • Introduction to NOSQL
    • Need of NOSQL
    • SQL vs NOSQL
    • CAP Theorem vs ACID properties
    • HBase commands hands on
    • Hadoop and HBase integration

    Module - 07 Project on BigData Hadoop


    Cloud Computing using AWS Section – G

    Module 01 - Introduction To Cloud Computing

    • Introduction to Cloud Computing
    • Advantages of Cloud Computing
    • Cloud Services & deployment models Cloud service providers
    • What is AWS?
    • AWS Account
    • AWS services
    • AWS Regions and AZ's
    • AWS suite Starting off with AWS
    • Billing Dashboard & Cost Explorer Setting up Billing Alarm $ Budget

    Module 02 - Simple Storage Service - S3

    • Basics of Storage System
    • Storage Services provided by AWS
    • Difference Between Object storage and Block Storage
    • Introduction to Simple Storage Service - S3
    • Components of S3
    • Important Properties of S3 bucket

    Module 03- Elastic Compute Cloud - EC2

    • Basics of Virtual Servers
    • Components of a Virtual Server
    • Introduction to Elastic Cloud Compute - EC2 Use cases and important features of EC2
    • Introduction to AMI - Its Uses
    • Introduction to Instance and its types
    • Security Groups - Creation & Management Key Pair - Why & How
    • Launching & Connecting to Window Instance Launching & Connecting to Linux Instance
    • Setting up a web server on linux Instance - Hosting a website Elastic IP Address Placement Group
    • Instance Pricing Model Tenancy Models

    Module 04 - Virtual Private Cloud (VPC)

    • Basics of Networking
    • IP Address and CIDR Block
    • Concept of Virtual Cloud
    • Introduction to Virtual Private Cloud -VPC Subnet and Route Tables
    • Internet Gateway and NAT
    • Creating and managing a NAT Instance
    • Access Control List - ACL
    • VPC Peering

    MODULE 05 - Relational Database Service (RDS)

    • Introduction to Database - Its Components Database Services provided by AWS
    • Introduction to RDS Components of RDS
    • DB engines provided by RDS
    • Snapshots and Back-up in RDS Read Replicas in RDS
    • Creating and connecting to a RDS database RDS Security
    • Pricing in RDS

    Module 06 - Simple Notification Service (SNS)

  • Introduction to Simple Notification Service - SNS How SNS Works?
  • Important Components of SNS
  • Creating and Managing Topics in SNS Adding Subscriber in SNS
  • Managing SNS Policy

Module 07 - Cloudwatch

  • Important Components of CloudWatch
  • Creating and Managing metrics in CloudWatch
  • Creating and Managing Events in CloudWatch
  • Creating and Managing Dashboards in Cloudwatch
  • Creating and Managing Alarms in CloudWatch
  • Creating and Managing Logs in Cloudwatch

Module 08 - Cloudtrail

  • Introduction to Cloudtrail
  • Creating and Managing trails
  • Setting up trail for Root login Notification

Milestone Project Section – H

  • Milestone project based on hybrid technologies
  • POC to evaluate individual performance
  • Code development
  • Code submission in github repository

Interview Preparation Section – I

  • GD – Group Discussion
  • Resume Building
  • Mock PI – Mock Personal Interview
  • Feedback

To obtain the Master’s Program Certification, you have to fulfil the following criteria:

  • Complete the Master’s program training course (online/classroom) syllabus.
  • Completion of all assignments & final project.
  • When you successfully complete the course, you will get a Master’s program completion certificate with a unique identification number from Technogeeks.

  • Pay only after Attending one FREE TRIAL OF RECORDED LESSON.
  • No prerequisite.
  • Course designed for non-IT as well as IT professionals.
  • Flexible batch switch is available.
  • Classroom & Online Training - Can switch from online training to classroom training with nominal fee.
  • Placement calls guaranteed till you get placed.
  • Working professional as instructor.
  • Proof of concept (POC) to demonstrate or self-evaluate the concept or theory taught by the instructor.
  • Hands-on Experience with Real-Time Projects.
  • Resume Building & Mock Interviews.
  • Evaluation after each Topic completion.
  •  

Best Blended Syllabus for Master’s Program in Data & Cloud Technologies in Pune by a Placement-Oriented IT Training Institute

KYC - Know Your (Master’s Program) Course

  • 1 month internship with Technogeeks
  • 300+ hrs of Assignment and Live training-based program
  • 50+ Modules
  • Capstone Projects : Real-world projects from industry experts.
  • Master’s Program Certificate with unique verification ID.
  • Certification in multiple technologies including Python Programming, SQL, Data analytics & visualization, Data Science & A.I., Bigdata Hadoop, Cloud Computing
  • The project is introduced to meet the growing demand for multi-skilled IT professionals.

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