Advanced Certification in Big Data Analytics

• Introduction to NoSQL Databases
• Introduction to NoSQL and MongoDB
• MongoDB installation
• Importance of NoSQL
• CRUD operations
• Data modeling and schema design
• Data management and administration
• Data indexing and aggregation
• MongoDB security
• Working with unstructured data

Tools covered

• Distributions
• Central Limit Theorem
• hypothesis testing/ statistical significance
• Advanced hypothesis testing

• Data Analytics in Excel
  • Concepts of finance
  • Concepts of economics
  • Hands-on: Inferential statistics, descriptive statistics, simple and multivariate regression, and confidence intervals
• Data Analytics Using SQL
  • Introduction to MySQL
  • Working with MySQL and MySQL IDE: Installation and setup
  • Introduction to SQL queries: DDL queries (create and select) and DML queries (alter, insert, etc.)
  • Working with joins, group, and filter
  • Writing complex SQL queries for data retrieval and the import and export of data and database tables

Tools covered

• Introduction to Python
• Python basic constructs
• OOPs in Python
• NumPy for mathematical computing
• SciPy for scientific computing
• Data manipulation
• Data visualization with Matplotlib
• Implementing statistical algorithms using Python

Tools covered

• Hadoop installation and setup
• Introduction to Big Data and Hadoop
• Understanding HDFS and MapReduce
• Deep dive into MapReduce
  • Introduction to Hive
  • Advanced Hive and Impala
  • Introduction to Pig
  • Flume and Sqoop

Tools covered

• Scala programming
• Spark framework
• RDD in Spark
• DataFrames and Spark SQL
• Machine Learning using Spark (MLlib)

Tools covered

• Introduction to PySpark
• Who uses PySpark?
• Why Python for Spark?
• Values, Types, Variables
• Operands and Expressions
• Conditional Statements
• Loops
• Numbers
• Python files I/O Functions
• Strings and associated operations
• Sets and associated operations
• Lists and associated operations
• Tuples and associated operations
• Dictionaries and associated operations
• Functions
• Lambda Functions
• Global Variables, its Scope, and Returning Values
• Standard Libraries
• Object-Oriented Concepts
• Modules Used in Python
• The Import Statements
• Module Search Path
• Package Installation Ways
• Introduction to Spark Streaming
• Features of Spark Streaming
• Spark Streaming Workflow
• StreamingContext Initializing
• Discretized Streams (DStreams)
• Input DStreams, Receivers
• Transformations on DStreams
• DStreams Output Operations
• Describe Windowed Operators and Why it is Useful
• Stateful Operators
• Vital Windowed Operators
• Twitter Sentiment Analysis
• Streaming using Netcat server
• WordCount program using Kafka-Spark Streaming

Hands-On:

• Twitter Sentiment Analysis
• Streaming using Netcat server
• WordCount program using Kafka-Spark Streaming
• Spark-flume Integration
• Demonstrating Loops and Conditional Statements
• Tuple – related operations, properties, list, etc.
• List – operations, related properties
• Set – properties, associated operations
• Dictionary – operations, related properties
• Lambda – Features, Options, Syntax, Compared with the Functions
• Functions – Syntax, Return Values, Arguments, and Keyword Arguments
• Errors and Exceptions – Issue Types, Remediation
• Packages and Modules – Import Options, Modules, sys Path

Tools covered

• Why model tuning?
• What is model tuning?
• What are parameters
• What are Hyper-parameters
• What is Hyper-parameter tuning?
• Types of Hyper parameter tuning:
• Grid Search
• Random Search

Hands-On:

• Performing Grid Search Hyperparameter Tuning to Increase model accuracy
• Performing Random Search Hyperparameter Tuning to Increase model accuracy

• Why Ensemble Learning?
• What is Ensemble Learning?
• Model Error
• Bias
• Variance
• Reducing Model Error
• Different Types of Ensemble Learning
• Bagging
• Boosting
• Stacking

Hands-On:

• Creating a Bagging classifier to reduce model error using sklearn
• Creating a Boosting classifier to reduce model error using sklearn
• Creating a Stacking classifier to reduce model error using sklearn

• What is Model Deployment
• Model Deployment Strategy
• Steps in Model Deployment
• Create a model
• Save it
• Load in in a web server/ web api
• Make Predictions

Hands-On:

• Creating, Saving and Deploying a model using a python falsk web api

• Spark Components & its Architecture
• Spark Deployment Modes
• Spark Web UI
• Introduction to PySpark Shell
• Submitting PySpark Job
• Writing your first PySpark Job Using Jupyter Notebook
• What is Spark RDDs?
• Stopgaps in existing computing methodologies
• How RDD solve the problem?
• What are the ways to create RDD in PySpark?
• RDD persistence and caching
• General operations: Transformation, Actions, and Functions
• Concept of Key-Value pair in RDDs
• Other pair, two pair RDDs
• RDD Lineage
• RDD Persistence
• WordCount Program Using RDD Concepts
• RDD Partitioning & How it Helps Achieve Parallelization
• Passing Functions to Spark

Hands-On:

• Building and Running Spark Application
• Spark Application Web UI
• Loading data in RDDs
• Saving data through RDDs
• RDD Transformations
• RDD Actions and Functions
• RDD Partitions
• WordCount program using RDD’s in Python

Tools covered

• Need for Spark SQL
• What is Spark SQL
• Spark SQL Architecture
• SQL Context in Spark SQL
• User-Defined Functions
• Data Frames
• Interoperating with RDDs
• Loading Data through Different Sources
• Performance Tuning
• Spark-Hive Integration

Hands-On:

• Spark SQL – Creating data frames
• Loading and transforming data through different sources
• Spark-Hive Integration

Tools covered

• Introduction to Machine Learning- What, Why and Where?
• Use Case
• Types of Machine Learning Techniques
• Why use Machine Learning for Spark?
• Applications of Machine Learning (general)
• Applications of Machine Learning with Spark
• Introduction to MLlib
• Features of MLlib and MLlib Tools
• Various ML algorithms supported by MLlib
• Supervised Learning Algorithms
• Unsupervised Learning Algorithms
• ML workflow utilities

Hands-On:

• K- Means Clustering
• Linear Regression
• Logistic Regression
• Decision Tree
• Random Forest

Tools covered

• Apache Flume and Apache Kafka
• Spark Streaming
• Case Study: Spark vs Kafka and when to use them

Tools covered

• Introduction to data visualization and the power of Tableau
• Architecture of Tableau
• Working with metadata and data blending
• Creation of sets
• Working with filters
• Organizing data and visual analytics
• Working with mapping
• Working with calculations and expressions
• Working with parameters
• Charts and graphs
• Dashboards and stories
• Tableau Prep
• Integration of Tableau with Big Data tools like Hadoop and Spark

Tools covered

• Marketing, Web, and Social Media Analytics
• Fraud and Risk Analytics
• Supply Chain and Logistics Analytics
• HR Analytics