Practical Data Science & Deployment Specialist Program

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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

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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

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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 Module -

• 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

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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 Module -

• 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

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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 Module -

• 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

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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 Module -

• 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)

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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 Module -

• 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

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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 Module -

• 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

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 Module -

• 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)

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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 Module -

• 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

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