• Candidates with Computer Science degree or equivalent experience and pursuing their first IT role with the focus on Data Engineering and Data Science.

• Understanding of how computers work
• One or more years technical experience
• Programming experience with Python & SQL would be a plus.

Course Outline:

Agile Scrum Methodology

• Scrum Introduction
• Scrum Team
• Scrum Artifacts
• Sprint Increment
• Spring planning
• Backlog
• Retrospective
• Project description and Case Study
• Practice exam and Knowledge check
• Certification (optional)

SQL

• SQL Fundamentals
• Writing SQL Queries
• Working Tables and Indexes
• Predefined SQL functions
• Connecting Python to SQL
• Certification (optional)

Data Engineering Principles

• Data engineering to prepare data for downstream needs
• Build pipelines for batch processing and streaming processing
• Understanding different types of data
• Using PyTorch for Artificial Neural Network – Classification

Python Programming – Fundamentals

• Set Up
• Set up development environment – Jupyter notebooks
• Using python shell
• Executing python script
• Understanding python strings
• Print statements in python
• Data Structures in python
• Integers
• Lists
• Dictionaries
• Tuple
• Sets
• File
• Mutable and Immutable structures
• Selection and Looping Constructs
• If/else/elif statements

• Boolean type
• “in” membership
• For loop
• While Loop
• List and Dictionary Comprehension
• Functions
• Defining functions
• Variable scope – Local and Global
• Arguments
• Polymorphisms
• Modules
• Creating modules
• Importing Modules
• Different types of imports
• Dir and help
• Examining some built-in modules
• Classes & Exceptions
• Object Oriented Programming Introduction
• Classes and Objects
• Polymorphism – Function and Operator Overloading
• Inheritance

Big Data Overview

• History and background of Big Data and Hadoop
• 5 V’s of Big Data
• Big Data Distributions in Industry
• Big Data Ecosystem before Apache Spark
• Big Data Ecosystem after Apache Spark
• Comparison of MapReduce Vs Apache Spark
• Big Data Ecosystem after Apache Spark
• Spark Clusters

Getting started with Apache Spark

• Understanding Apache Spark Components and Libraries
• Introduction to Pyspark
• Explore using Pyspark in Databricks Cloud Environment
• Pyspark code examples
• Working with Jupyter Notebook

Working with Spark SQL

• Getting started with Spark SQL
• Spark Context and Spark Session
• Performing basic data transformations with Spark SQL CLI
• Managing Tables with Spark SQL
• Spark SQL functions

Apache Spark Data Structures – RDD

• Understanding fundamental data structure in Spark – RDD
• Understanding Linage and Lazy Evaluation with RDD
• Performing RDD transformations
• Performing RDD actions
• RDD persistence and caching

Apache Spark Data Structures – Data frames

• Understanding another data structure in Spark – Data frames
• Reading different file formats into Data frame
• Creating and Inferring Data frame schema
• Basic transformations on Data frames
• Basic actions on Data frames
• Apply functions such as filtering, group by, etc. on Data frame
• Aggregations, Sum, Mean, on Data frame
• Preparing data transformation pipelines

Apache Spark Data Structures – Data frames (Advanced)

• Apply functions such as filtering, group by, etc. on Data frame
• Aggregations, Sum, Mean, on Data frame
• Handling corrupt records
• Preparing data transformation pipelines
• Working with Data frame Joins
• Understanding Data frame Do’s and Don’ts
• Spark Lifecycle and Spark UI

Machine Learning Overview

• History and Background of AI and ML
• Compare AI vs ML vs DL
• Describe Supervised and Unsupervised learning techniques and usages
• Machine Learning patterns
• Classification
• Clustering
• Regression
• Gartner Hype Cycle for Emerging Technologies
• Machine Learning offerings in Industry
• Discuss Machine Learning use cases in different domains
• Understand the Data Science process to apply to ML use cases
• Understand the relation between Data Analysis and Data Science
• Identify the different roles needed for successful ML project
• Prepare machine learning data using pipelines – Data manipulation

Spark Machine Learning Library

• Prepare machine learning data using pipelines – Data manipulation
• Building Classification models with Spark Machine Learning Library
• Building Regression models with Spark Machine Learning Library
• Clustering with Spark ML library
• Understanding model performance and metrics
• Data pipeline and model persistence

Spark Streaming Library

• Streaming data and its challenges
• Understanding Spark Structured Streaming
• Working with Spark Streaming output modes
• Aggregations on streaming data

NoSQL

• Relational v/s Non-Relational Databases
• What are NoSQL databases?
• Types of NoSQL databases

Document Datastore: MongoDB

• MongoDB Introduction
• Understanding Basics and CRUD operations
• Structuring Documents
• Create Operations
• Read Operations on Collections
• Updating Documents
• Deleting Documents
• Working with Indexes
• Working with different data types
• Using MongoDB Compass to explore data visually
• Integrating Apache Spark with MongoDB

DevOps Toolkit

• DevOps Overview
• Containers with Docker
• Orchestrating containers with Kubernetes
• Understanding Continuous Integration
• Understanding Continuous Delivery and Deployment

Cloud Computing Foundations (AWS or GCP)

• Cloud Computing Overview
• Security with Google’s Cloud Infrastructure
• Understanding resource hierarchy
• IAM – Identity and Access Management
• Different IAM Roles
• Connecting to Google Cloud Platform
• Understanding different compute options
• Working with different Relational and NoSQL databases on GCP
• GCP Data Warehouse: Big Query

Project & Use Case

• Project Overview
• Complete projects to get experience and practice
• Industry Use Case Studies

Certification

• Certification Overview
• Identify the right certification for you
• Tips to prepare for certification

Training material provided:

Yes (Digital format)
Hands-on Lab: Instructions will be provided to install Anaconda and PyTorch on student’s machines.