Practical Data Science

12 Months
In Demand Job Guarantee Program in
PG Diploma in Practical Data Science & Deployment Specialist

In Collaboration with

14th Sep

Intake Date

12 Months

Total Duration

Part Time

Weekend

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Key Highlights for Practical Data Science & Deployment Specialist

100% Job guarantee program
INR 4.5 L Conditional job offer letter from Innodatatics. Inc or Pvt Ltd
10+ hours of masterclasses from leading academician and faculty from IIT and ISB
10+ Mock interviews with subject experts throughout the program

20 Industry specific real-time projects
100 hours of credible working experience
INR 2L Earn back opportunity if you don't get a job through us
INR 41,000 sponsored payback funds from NASSCOM for completion of core modules 10 Blockchain security-enabled micro-credential certificates

Program Overview for Practical Data Science & Deployment Specialist

One year program will help students and practitioners build the data pipeline by understanding business problems. First, one will understand the right data to be ingested from the right sources, and after that, performing the right preprocessing techniques will be learned.

Finally, participants can confidently face the customers and document the business problem in a manner that will help align all the business & technical stakeholders into solving the complex problems.

This program covers every aspect of the data pipeline, alongside the pre vs. post-solution building aspects. The comprehensiveness of the solution building is the best in the industry along with industry leaders and academic partners and encompasses Data Engineering, Data Science, and MLOps, including productionisation.

Overview for Practical Data Science & Deployment Specialist

Deciding the right Machine Learning algorithms to solve the problems is a combination of multi-dimensional aspects, including explainability, applicability, scalability, etc., and all these will be thoroughly understood..

Understanding customer infrastructure (cloud or on-premise) and then deploying the final solution in a usable format by end users is the fulcrum of the learning.

Monitoring will also be discussed to ensure that the machine-learning solutions meet the regulatory requirements.

Finally, to ensure that the solution built is applicable throughout the business’s success, one must monitor the model.

Learning Outcomes for Practical Data Science & Deployment Specialist

Handle Data Science, AI, IoT, and Cloud projects using a structured project management methodology.
Be able to handle what-if analysis for management decision-making.
Data Engineering tools and techniques for effective data ingestion.
Learn how to work in a collaborative environment using collaborative tools because multiple data scientists work on the same project.
Exposure to the world of automation via - AutoEDA, Auto Data Preparation, AutoML libraries.

Working knowledge of handling model pipelines, including automating the pipelines.
Deploying the models’ on all ‘3’ major cloud platforms - AWS, Azure, GCP
Learn to work on deploying the models on-premise environments.
Working on model monitoring and maintenance tools such as Evidently, etc.
Learn about meeting the regulatory requirements of the model along with building explainability.

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Program Framework for
Practical Data Science & Deployment Specialist

Chart your career with 10 core modules and 20 live projects designed for expanding job roles which includes fundamentals to advanced analytics concepts

EDA & Business Intelligence

Python for Data Science

20 Hours

Objectives

This course will enable you to understand what data science is, how it helps business draw meaningful insights from historical data, learn to deal with data, understand various data types and methods to deal with them, understand how data mining machine learning techniques are used to predict outcomes and finally how to analyse and infer strategies to help organizations to draw benefits.

Key Modules

Introduction to Python Programming
Installation of Python & Associated Packages
Graphical User Interface
Installation of Anaconda Python
Setting Up Python Environment
Data Types
Operators in Python
Data structures
Conditional Statements
Loops
Functions

Function call stack
Stackframe
Modules
File Handling
Exception Handling
Regular expressions
Oops concepts
Classes and Objects
Inheritance and Polymorphism
Multi-Threading

Database Management System (SQL)

12 Hours

Objectives

SQL for structure database and NoSQL for unstructured database. Create tables to store data and extract specific information for data analysis for SQL and understand how to leverage the capabilities of NoSQL alongside SQL.

Key Modules

What is a Database
Types of Databases
DBMS vs RDBMS
DBMS Architecture
Normalisation & Denormalization
Install PostgreSQL
Install MySQL
Data Models
DBMS Language
ACID Properties in DBMS
What is SQL
SQL Data Types
SQL commands

SQL Operators
SQL Keys
SQL Joins
GROUP BY, HAVING, ORDER BY
Subqueries with select, insert, update, delete statements?
Views in SQL
SQL Set Operations and Types
SQL functions
SQL Triggers
Introduction to NoSQL Concepts
SQL vs NoSQL
Database connection SQL to Python

CRISP-ML(Q) - Business & Data Understanding; Data Preprocessing, EDA, Feature Engineering

20 Hours

Objectives

CRISP - ML(Q) the perfect Project Management Methodology used for handling Data Mining projects. Understand the entire process flow including Business Problem definition, Data Collection, Data Cleansing, Feature Engineering, Feature Selection, Model Building, Deployment and Maintenance.

Key Modules

Dos and Don'ts as a participant
Introduction to Big Data Analytics
Data and its uses – a case study (Grocery store)
Interactive marketing using data & IoT – A case study
Course outline, road map, and takeaways from the course
Stages of Analytics - Descriptive, Predictive,Prescriptive, etc.
Cross-Industry Standard Process for Data Mining
Typecasting
Handling Duplicates
Outlier Analysis/Treatment
Zero or Near Zero Variance Features
Missing Values
Discretization / Binning / Grouping
Encoding: Dummy Variable Creation
Transformation
Scaling: Standardization / Normalization
Machine Learning project management methodology
Data Collection - Surveys and Design of Experiments
Data Types namely Continuous, Discrete, Categorical, Count, Qualitative, Quantitative and its identification and application

Further classification of data in terms of Nominal, Ordinal, Interval & Ratio types
Balanced versus Imbalanced datasets
Cross Sectional versus Time Series vs Panel / Longitudinal Data
Batch Processing vs Real Time Processing
Structured versus Unstructured vs Semi-Structured Data
Big vs Not-Big Data
Data Cleaning / Preparation - Outlier Analysis, Missing Values Imputation Techniques,
Transformations, Normalization / Standardization, Discretization
Sampling techniques for handling Balanced vs. Imbalanced Datasets
What is the Sampling Funnel and its application and its components?
Measures of Central Tendency & Dispersion
Feature Engineering on Numeric /
Non-numeric Data
Feature Extraction
Feature Selection

Business Intelligence with Power BI

24 Hours

Objectives

Visualising data to extract meaningful & actionable business insights is pivotal for success of organisations of any size, be it small or medium or large. Understanding the various ways of representing data in presentable format using a wide variety of plots and representing insights & KPIs in dashboards, is a combination of art & science. Alongside preparing reports and dashboards on-premise systems, one should also be proficient with the cloud aspect. Establishing connectivity between Power BI & Azure cloud & at the same time moving from reactive or proactive decision making requires knowledge of Machine Learning. All these are explained using enriching real-world examples.

Key Modules

What is Power BI?
Introduction
Overview of Power BI
Architecture of PowerBI
PowerBI and Plans
Installation and introduction to PowerBI
Importing data
Changing Database
Data Types in PowerBI
Basic Transformations
Managing Query Groups
Splitting Columns
Changing Data Types

Working with Dates
Removing and Reordering Columns
Conditional Columns
Custom columns
Connecting to Files in a Folder
Merge Queries
Query Dependency View
Transforming Less Structured Data
Query Parameters
Column profiling
Query Performance Analytics
M-Language

Data Science & Adv Machine Learning

Data Science Using Python

60 Hours

Objectives

Data Science Using Python is focuses on the MUST KNOW concepts of Data Analytics for students as well as professionals who are working in other domains, to embark into the world's # 1 profession - Data Science. This training is focused on providing knowledge on all the key techniques such as Statistical Analysis, most widely used Regression Analysis, Data Mining Unsupervised Learning Techniques, Machine Learning, Forecasting, Text Mining and many more. These techniques will be explained using the best data science tools in industry – Python & R.

Key Modules

Mathematical Foundations
Clustering / Segmentation
Dimension Reduction
Association Rules
Recommender Systems
Network Analytics
Text Mining and Natural Language Processing (NLP)
Machine Learning
Machine Learning Classifier Technique - Naive Bayes
Machine Learning - KNN Classifier
Confidence Interval
Hypothesis Testing - The ‘4’ Must Know Hypothesis Tests
Supervised Learning – Regression Techniques

Multiple Linear Regression - Predictive Modelling
Logistic Regression Binary Value Prediction, MLE
Lasso and Ridge Regressions
Multinomial and Ordinal Logistic Regression
Advanced Regression for Count Data
Kernel Method - SVM
Ensemble Techniques
Survival Analytics
Decision Tree
Model-Driven Algorithms
Data-Driven Algorithms
Introduction to Perceptron and Multilayer Perceptron
Building Blocks of Neural Network - ANN
Deep Learning Primer

AI & Deep Learning Using Python

60 Hours

Objectives

The certification course been designed for professionals with an aptitude for statistics and a background in programming language such Python, R, etc. Artificial Intelligence (AI) and Deep Learning training helps students in Building Applications, understanding Neural Network Architectures, Structuring Algorithms for new Al Machines and minimizing errors through Advanced Optimization Techniques.

Key Modules

Introduction to Artificial Intelligence and Deep learning
Introduction to Python Libraries
Machine Learning Primer
Mathematical Foundations
Deep Learning Primer
Perceptron Algorithm and Back
Propagation Neural Network Algorithm
Artificial Neural Network (ANN), Multilayer Perceptron (MLP)
Image Processing and Computer Vision
Convolutional Neural Networks - CNN
Object Detectors, Image Segmentation,
Model Optimization and Inference
Recurrent Neural Network - RNN

Long Short Term Memory - LSTM
Gates Recurrent Units - GRUs
Functional APIs
Sequence to Sequence Model,Transformers and BERT
ChatBots Using RASA and BERT
Speech Recognition
Reinforcement Learning and Q-learning
Autoencoders and Types of Autoencoders
Generative Adversarial Networks - GAN and Types GANs
Restricted Boltzmann Machines and Deep Belief Networks
Auto Artificial intelligence (Auto AI)
Explainable Artificial Intelligence (XAI)

End to End Model Deployment

Data Engineering Modules

24 Hours

Objectives

The Data Engineering course modules will lay out the foundation for data science and analytics. The core of Data Engineering involves understanding various techniques like data modeling, designing, constructing, and maintaining data pipelines, and deploying analytics models. As you progress, you'll learn how to design, build data pipelines, and work with big data of diverse complexity and real-time streaming data sources. You will also learn to extract and extract data from multiple sources, build data processing systems post the transformations of the data, optimize processes for big data, build data pipelines, and much more.

Key Modules

Intro to Data Engineering
Data Sources
Python Programming
Big Data Tools
Apache Spark
Apache Kafka
Introduction to Apache Airflow
Data Lakes and Data Warehouses
Data Engineering with AWS
Data Engineering with Azure
Data Engineering with GCP

Machine Learning Operations (MLOps)

40 Hours

Objectives

Machine Learning Operations a.k.a MLOps is fast gaining steam as one of the most sought-after skills in the Data Science and Artificial Intelligence domain. The MLOps course with On-premises as well as Cloud tools is a first in the industry offering to help Data Scientists and ML Engineers, deploy ML models into production with efficiency. This course focuses on the best-in-class tools and frameworks such as Kubernetes, Kubeflow, MLflow, Tensorflow Extended (TFX), and Apache Beam among others.

Key Modules

Introduction to ML workflow and the need for Pipelines
Automated ML Deployment Tools
Cloud-Based Tools
Open-Source Tools
Cloud-Based ML Deployment Tools
AWS SageMaker
Cloud-Based ML Deployment Tools
Microsoft Azure
Google Cloud Platform (GCP)
Open-Source Tools
Pros and Cons

Containerization vs Virtualization
Docker for ML
Kubernetes
Kubernetes for Machine Learning
Kubernetes Architecture
Introducing Kubeflow
Need for CI/CD pipelines for Machine Learning models
MLOps Stack with KubeflowPlan and Design a Kubeflow installation
MiniKF on Local and Cloud

Machine Learning on Cloud - Elective 1

12 Hours

Objectives

Learn to implement Machine Learning models on the cloud in the most comprehensive program. Learn the various ML algorithms to handle large data and to derive patterns and predict results. Explore how Machine Learning models are designed, deployed, configured, and managed on the various cloud platforms. You will also learn about the various benefits of Machine learning on the cloud and also draw a comparison of machine learning services on various cloud platforms like AWS, Azure, and Google Cloud.

Key Modules

Machine Learning on Cloud & AutoML
Amazon Web Services & Amazon SageMaker
AWS Machine Learning Services
Microsoft Azure Machine Learning Services
Machine Learning with Google Cloud Platform
IBM Watson Machine Learning
eXplainable AI (XAI)

Domain Analytics - Elective 2

16 Hours

Objectives

There are 20+ domain specific Analytical modules. One module can be chosen from these 20+ modules. Select an elective module and add more value to your profile

Key Modules

Cyber Security Analytics
HR Analytics
Supply Chain Analytics
Financial analytics
Customer Analytics
Marketing Analytics
Banking Analytics
Accounting Analytics
eCommerce Analytics
Life Sciences Analytics
Logistics analytics

Security analytics
Telecom analytics
Geospatial analytics
Web and Mobile Analytics
Energy and Resources Analytics
Sports Analytics
Trading Analytics
Retail Analytics
Media Analytics
Oil & Gas Data Analytics

Applied AI with POC’s

12 Projects & POC's Offline Coursework - research & reading on emerging techniques

24 Hours

Final Examination

One hour examination and viva by State University of New York (SUNY) team of experts.

1 Hour

Objectives

Design an Entity Relation Ship Diagram for Database creation for a Logistics Company.

Programming Languages and Software Tools

The 360DigiTMG's Advantage

Scholarships up-to 20%

Our program advisors shall help you save more

Resume prep sessions

Learn to customise your profile for 5 different domains

Digital Portfolio

Assistance to built digital portfolio to suit all fields data science spectrum

Mock interview session

Module level simulation of an actual job interview with industry experts

Fee Structure for Practical Data Science & Deployment Specialist Program

Total Program Fee

₹ 2,18,300

(inclusive of all taxes)

Hybrid Mode | Combination of
Classroom, Virtual and elearnings

Deadline to Apply: 7th Sep'2024

Apply Now

EMI as low as Rs 18,192/ month
Limited seats for classroom seats for selected modules
Hyderabad | Chennai | Bangalore

Fee break up with instalments

Registration Fee (Included in the fee)	Standard Instalments	Loan Amount EMI
Rs.20,000	₹ 66,100 Day 0 (No Cost EMI)	₹ 24,256 9 months (No Cost EMI)
	₹ 66,100 Day 30 (No Cost EMI)	₹ 18,192 12 months (No Cost EMI)
	₹ 66,100 Day 60 (No Cost EMI)

What is the prime advantage of joining this Practical Data Science course?

The prime advantage of a program is the ability to complete 10 courses each of 1-2 months duration in 12 months. The cost of the program is far lower than that of a university Masters Program. Also since you are specialising in multiple domains you can apply for a variety of job roles in data science at the end of the course. Your employability and knowledge levels will be higher than the competition.

eLearning Courses

Flexibility	-
Full time coding courses	Up to 400 hours
Time commitment	-
Coursework (practical)
Pre & Post support
Industry Specific Curriculum
Exposure to Domain Skills
Team studies
Life-time learning support
1-1 mentoring
Cost of Investment	-

Academic Degree

Flexibility	-
Full time coding courses	>600 hours
Time commitment	2-4 years
Coursework (practical)
Pre & Post support
Industry Specific Curriculum	Upto 50%
Exposure to Domain Skills
Team studies
Life-time learning support
1-1 mentoring
Cost of Investment	Upto INR 13 L

360DigiTMG 3D program

Flexibility	Live, classroom, self-paced learning
Full time coding courses	Up to 120 hours
Time commitment	1 year
Coursework (practical)	data analytics
Pre & Post support
Industry Specific Curriculum	100%
Exposure to Domain Skills	Upto 56 Domain Analytics elearnings
Team studies
Life-time learning support
1-1 mentoring
Cost of Investment	INR 2,18,300

	eLearning Courses	Academic Degree	360DigiTMG 3D program
Flexibility	-	-	Live, classroom, self-paced learning
Full time coding courses	Up to 400 hours	>600 hours	Up to 120 hours
Time commitment	-	2-4 years	1 year
Coursework (practical)			data analytics
Pre & Post support
Industry Specific Curriculum		Upto 50%	100%
Exposure to Domain Skills			Upto 56 Domain Analytics elearnings
Team studies
Life-time learning support
1-1 mentoring
Cost of Investment	-	Upto INR 13 L	INR 2,18,300

You Will Work on Industry Projects in collaboration with AiSPRY, USA

Learn through the "20 Most Promising Data Analytics Solution Provider of 2018".

Innodatatics provide customised and innovative solutions for the clients. The organisation is keen to support and encourage upcoming data professionals and they believe that the need for skilled data scientists in only going grow in the coming years. 360DigiTMG in collaboration with Innodatatics offers hands-on Internships to students where they can work on real world problems and solve real-world challenges.

Cloud Based Deployment (AWS)

Health monitoring for Plants using Practical Transfer Learning on AWS

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You will learn to capture the health of plants using the image of the leaf. You will learn to develop an Image processing Model using deep learning algorithms. You will learn to develop, train, deploy, the ML/AI model on the AWS cloud platform.

Cloud Based Deployment (AWS)

Hugging Face models for NLP Generative Chatbot on AWS

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You will learn to use AWS services to develop a generative chatbot. You will learn the application of NLP models on AWS cloud. You will learn to design and develop an NLP pipeline to train the model on Text corpus stored in PostgreSQL DB.

Cloud Based Deployment (AWS)

Practical Real-time Prediction and Analysis on AWS

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You will learn to train and develop end to end ML models on the data stored on the s3 bucket. You will learn to deploy it with a click of a button using the autopilot feature on AWS Sagemaker service.

AutoML Based Deployment

AutoML frameworks for Practical ML project implementation

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You will learn to implement an ML workflow with automated libraries like AutoEDA, Hyperparameter optimization techniques, and AutoML libraries You will learn to benchmark the best solution by developing multiple models to compare the performances

AutoML Based Deployment

AutoML for Predictive Maintenance

Learn more >

You will learn to use AutoML libraries to develop an ML model to perform preventive maintenance Manufacturing sector. You will learn to ingest data generated from IoT sensors.

AutoML Based Deployment

AutoML TPOT for End to End Machine Learning Application

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You will learn to automate the ML pipeline with tree-based AutoML technique Tpot. You will learn to tune the AutoML parameters with the help of Hyperparameters and export the best pipeline identified by the AutoML TPOT library for deployment.

MLOps Based Deployment

Machine Learning Algorithms from Scratch using MLOps Strategies

Learn more >

You will learn to deploy an ML pipeline With the help of open-source MLOps platforms. You will learn how to data drift and model drift with the help of data validation strategies of MLops tools.

MLOps Based Deployment

ML Pipeline using Kubeflow with End-to-End deployment and Monitoring

Learn more >

You will learn how to set up the machine learning toolkit for Kubernetes (Kubeflow) on cloud services. You will learn to develop end to end ML models with Kubeflow pipelines with deployment.

MLOps Based Deployment

Leveraging Google Cloud IoT and AI/ML services for Practical ML Applications for Live Streaming Data

Learn more >

You will learn to Ingest live streaming data generated by IoT sensors using the pub-sub system on GCP. You will learn to create a pipeline to work with the data stored in BigQuery and perform predictive analytics solutions.

MLOps Based Deployment

Distributed Computing using Spark for Network Optimization

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You will learn to work with very large-sized geospatial data with the help of big data distributed framework services. You will learn to use Apache Spark in processing the large amount of unstructured data using the GraphX library on the Hadoop Cluster.

MLOps Based Deployment

Machine Learning Models for Predictive Analytics with SparkML

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You will learn to address the high latency problem by using Apache SparkML on the distributed platform for in-memory cluster. computing to try and speed up the ML training pipeline by 100xtrain the ML models on big data will be a very time-consuming activity.

MLOps Based Deployment

Fraud Detection in Procurement on BigData using Practical tools

Learn more >

You will learn to design data pipelines to ingest the data from multiple tables. You will learn to use Big Data tools for developing Data Science solutions for the retail sector to address procurement fraud.

Cloud Based Deployment (AWS)

Health monitoring for Plants using Practical Transfer Learning on AWS

Learn more >

Cloud Based Deployment (AWS)

Hugging Face models for NLP Generative Chatbot on AWS

Learn more >

Cloud Based Deployment (AWS)

Practical Real-time Prediction and Analysis on AWS

Learn more >

AutoML Based Deployment

AutoML frameworks for Practical ML project implementation

Learn more >

AutoML Based Deployment

AutoML for Predictive Maintenance

Learn more >

You will learn to use AutoML libraries to develop an ML model to perform preventive maintenance Manufacturing sector. You will learn to ingest data generated from IoT sensors.

AutoML Based Deployment

AutoML TPOT for End to End Machine Learning Application

Learn more >

MLOps Based Deployment

Machine Learning Algorithms from Scratch using MLOps Strategies

Learn more >

You will learn to deploy an ML pipeline With the help of open-source MLOps platforms. You will learn how to data drift and model drift with the help of data validation strategies of MLops tools.

MLOps Based Deployment

ML Pipeline using Kubeflow with End-to-End deployment and Monitoring

Learn more >

You will learn how to set up the machine learning toolkit for Kubernetes (Kubeflow) on cloud services. You will learn to develop end to end ML models with Kubeflow pipelines with deployment.

MLOps Based Deployment

Leveraging Google Cloud IoT and AI/ML services for Practical ML Applications for Live Streaming Data

Learn more >

MLOps Based Deployment

Distributed Computing using Spark for Network Optimization

Learn more >

MLOps Based Deployment

Machine Learning Models for Predictive Analytics with SparkML

Learn more >

MLOps Based Deployment

Fraud Detection in Procurement on BigData using Practical tools

Learn more >

Advisory Panel and Facilitators

Bharani Kumar Depuru

Program Director

CEO & Founder - 360DigiTMG

Sharat Chandra Kumar

Data Scientist - Research in Analytics,

Big Data & Deep Learning

Lavanya Gade

Program Advisor

Innodatatics, Malaysia

Dr. Nagendra P (PhD)

Analytical Research and

Development Senior Scientist

S Swaroop

Director

360DigiTMG, USA

Bhargavi kandukuri

Senior Business Analyst

Innodatatics, Malaysia

Deepthi Gonela

Data Scientist

360DigiTMG, India

Dr. Sreedeepthi (PhD)

Global Solution Delivery Manager,

Novartis Ireland Ltd.