Big Data Data Science Certification Master Course

If you are dreaming to get into the world of Big Data and Data Science, then this Big Data Data Science Certification Master Course is for you. With our advanced teaching methodologies by expert trainers, you will gain proficiency in Big Data and Data Science. Also, other major subjects that you’ll be dedicated to working on are Hadoop Dev, Apache Spark, Scala, Artificial Intelligence, Tableau, Deep Learning, AWS, R, Python for Data Science, etc.

ENROLL NOW

Big Data Data Science Course Online – Overview

Big Data and Data Science are the most preferred and high-end technologies in this high-tech digital world. Due to their amazing career opportunities and high earning potential, Data Science and Big Data have turned out to be the best career options for most graduates, software developers, and IT professionals. Furthermore, with this Big Data Data Science Master Course at Hatigen, you will acquire an in-depth knowledge of designing and developing applications in the real world. Thus, if you are dreaming to become Data Science and Big Data architect, then join this course and enhance your skills in statistical computing, Hadoop, deep learning in artificial intelligence, etc.

Big Data Data Science Course Online – Key Features

  • Trusted content
  • Re-learn for free anytime in a year
  • Rigorous assignments and assessments
  • Learn at your own pace
  • Mandatory feedback sessions
  • Mock-interviews
  • Hands-on real-time experience
  • Free mentorship
  • Live chat for instant solutions
  • Job-ready employees post-training
  • End-to-end training
  • Download the certificate after the course

Big Data Data Science Training Course – Benefits

The global market of big data analytics is expected to grow by USD 549.73 billion in 2028 with a CAGR of 13.2% during the period 2021 to 2028.

Designation
Annual Salary
Hiring Companies
Job Wise Benefits
Designation
Big Data Analyst

UK
Hiring Companies
Designation
Data Scientist

UK
Hiring Companies

Big Data Data Science Course – Training Options

Self-Paced Learning

£ 1200.00

  • 1-year access to the Blockchain course content
  • 1 capstone project
  • Multiple assessments
  • Continuous feedback sessions
  • Access to the class recordings
  • Assistance and support
  • Download certification
  • Free mentorship

Online Boot Camp

£ 1000.00

  • Everything in Self-paced learning +
  • On-spot doubt clarification
  • Interactive training sessions
  • Sessions on the capstone project
  • Live, online classroom training
  • Mock-interviews

Corporate Training

Customized to your team's needs

  • 1-year access to the Blockchain course content
  • 1 capstone project
  • Multiple assessments
  • Continuous feedback sessions
  • Class recordings
  • Assistance and support
  • Certification after the course

Big Data Data Science Masters Training Program – Curriculum

Eligibility

Big Data Data Science Masters Course is well-suited for engineering graduates aiming to make a career in Data Science and Big Data fields. It is the ideal course for intermediate-level professionals who want to advance their skills, switch their career paths, and get high-paying jobs.

Pre-requisites

Before you start with this Big Data Data Science Training Program, you should have a basic understanding of statistical mathematics at the college level. Also, students should be familiar with the basic program languages such as R or Python.

Course Content

  • Hadoop Installation and Setup
  • Introduction to Big Data Hadoop and Understanding HDFS and MapReduce
  • Deep Dive in MapReduce
  • Introduction to Hive
  • Advanced Hive and Impala
  • Introduction to Pig
  • Flume, Sqoop, and HBase
  • Writing Spark Applications Using Scala
  • Use Case Bobsrockets Package
  • Following topics will be available only in self-paced mode:
  • Hadoop Administration – Multi-node Cluster Setup using Amazon EC2
  • Hadoop Administration – Cluster Configuration
  • Hadoop Administration – Maintenance, Monitoring, and Troubleshooting
  • ETL Connectivity with Hadoop Ecosystem (Self-paced)
  • Hadoop Application Testing
  • Roles and Responsibilities of Hadoop Testing Professional
  • Framework called MRUnit for Testing of MapReduce Programs
  • Unit Testing
  • Test Execution
  • Test Plan Strategy and Writing Test Cases for Testing Hadoop Application
  • Scala Course Content
  • Introduction to Scala
  • Pattern Matching
  • Executing the Scala Code
  • Classes Concept in Scala
  • Case Classes and Pattern Matching
  • Concepts of Traits with Examples
  • Scala–Java Interoperability
  • Scala Collections
  • Mutable Collections vs Immutable Collections
  • Use Case Bobsrockets Package
  • Spark Course Content
  • Introduction to Spark
  • Spark Basics
  • Working with RDDs in Spark
  • Aggregating Data with Pair RDDs
  • Writing and Deploying Spark Applications
  • Parallel Processing
  • Spark RDD Persistence
  • Spark MLlib
  • Integrating Apache Flume and Apache Kafka
  • Spark Streaming
  • Improving Spark Performance
  • Spark SQL and Data Frames
  • Scheduling/Partitioning
  • Introduction to Data Science with R
  • Data Exploration
  • Data Manipulation
  • Data Visualization
  • Introduction to Statistics
  • Machine Learning
  • Logistic Regression
  • Decision Trees and Random Forest
  • Unsupervised Learning
  • Association Rule Mining and Recommendation Engines
  • Self-paced Course Content
  • Introduction to Artificial Intelligence
  • Time Series Analysis
  • Support Vector Machine (SVM)
  • Naïve Bayes
  • Text Mining
  • Introduction to Data Science using Python
  • Python basic constructs
  • Maths for DS-Statistics & Probability
  • OOPs in Python (Self paced)
  • NumPy for mathematical computing
  • SciPy for scientific computing
  • Data manipulation
  • Data visualization with Matplotlib
  • Machine Learning using Python
  • Supervised learning
  • Unsupervised Learning
  • Python integration with Spark (Self paced)
  • Dimensionality Reduction
  • Time Series Forecasting
  • Introduction to Data Visualization and The Power of Tableau
  • Architecture of Tableau
  • Charts and Graphs
  • Working with Metadata and Data Blending
  • Advanced Data Manipulations
  • Working with Filters
  • Organizing Data and Visual Analytics
  • Working with Mapping
  • Working with Calculations and Expressions
  • Working with Parameters
  • Dashboards and Stories
  • Tableau Prep
  • Integration of Tableau with R
  • Splunk Development Concepts
  • Basic Searching
  • Using Fields in Searches
  • Saving and Scheduling Searches
  • Creating Alerts
  • Scheduled Reports
  • Tags and Event Types
  • Creating and Using Macros
  • Workflow
  • Splunk Search Commands
  • Transforming Commands
  • Reporting Commands
  • Mapping and Single Value Commands
  • Splunk Reports and Visualizations
  • Analyzing, Calculating and Formatting Results
  • Correlating Events
  • Enriching Data with Lookups
  • Creating Reports and Dashboards
  • Getting Started with Parsing
  • Using Pivot
  • Common Information Model (CIM) Add-On
  • Splunk Administration Topics
  • Overview of Splunk
  • Splunk Installation
  • Splunk Installation in Linux
  • Splunk Installation in Linux
  • Introduction to Splunk App
  • Splunk Indexes and Users
  • Splunk Configuration Files
  • Splunk Deployment Management
  • Splunk Deployment Management
  • User Roles and Authentication
  • Splunk Administration Environment
  • Basic Production Environment
  • Splunk Search Engine
  • Various Splunk Input Methods
  • Splunk User and Index Management
  • Machine Data Parsing
  • Search Scaling and Monitoring
  • Splunk Cluster Implementation
  • Introduction to Deep Learning and Neural Networks
  • Multi-layered Neural Networks
  • Artificial Neural Networks and Various Methods
  • Deep Learning Libraries
  • Keras API
  • TFLearn API for TensorFlow
  • Deep Neural Networks (DNNs)
  • Convolutional Neural Networks (CNNs)
  • Rrecurrent Neural Networks(RNNs)
  • GPU in Deep Learning
  • Autoencoders and Restricted Boltzmann Machine (RBM)
  • Deep Learning Applications
  • Chatbots
  • 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
  • Introduction to Microsoft Azure
  • Introduction to ARM & Azure Storage
  • Introduction to Azure storage
  • Azure Virtual Machines
  • Azure App and Container services
  • Azure Networking – I
  • Azure Networking – II
  • Authentication and Authorization in Azure using RBAC
  • Microsoft Azure Active Directory
  • Azure Monitoring
  • Introduction to Cloud Computing & AWS
  • Elastic Compute and Storage Volumes
  • Load Balancing, Autoscaling, and DNS
  • Virtual Private Cloud Storage – Simple
  • Storage Service (S3) Databases and In-memory
  • Datastores Management and Application Services
  • Access Management and Monitoring Services
  • Automation and Configuration management
  • AWS Migration
  • Self-paced
  • Architecting AWS – Whitepaper
  • DevOps on AWS
  • Amazon FSx and Global Accelerator
  • AWS Architect Interview Questions
  • HBase Overview
  • Architecture of NoSQL
  • HBase Data Modeling
  • HBase Cluster Components
  • HBase API and Advanced Operations
  • Integration of Hive with HBase
  • File Loading with Both Load Utilities
  • Advantages and Usage of Cassandra
  • CAP Theorem and No SQL DataBase
  • Cassandra Fundamentals, Data model, Installation, and Setup
  • Cassandra Configuration
  • Summarization, Node Tool Commands, Cluster, Indexes, Cassandra & MapReduce, Installing Ops-center
  • Multi-cluster Setup
  • Thrift/Avro/Json/Hector Client
  • Datastax Installation Part, Secondary index
  • Advance Modeling
  • Deploying IDE for Cassandra applications
  • Cassandra Administration
  • Cassandra API and Summarization and Thrift
  • Introduction to Couchbase
  • Single-node Implementation C
  • Couchbase Web Console
  • Couchbase Multi-node Cluster
  • Couchbase Command-line Interface
  • Introduction to Machine Learning
  • Supervised Learning and Linear Regression
  • Classification and Logistic Regression
  • Decision Tree and Random Forest
  • Naïve Bayes and Support Vector Machine (Self-paced)
  • Unsupervised Learning
  • Natural Language Processing and Text Mining
  • Introduction to Deep Learning
  • Time-series Analysis
  • Fundamentals of Search Engine and Apache Lucene
  • Analyzers in Lucene
  • Exploring Apache Lucene
  • Apache Lucene Demonstration
  • Apache Lucene advanced
  • Advanced Topics of Apache Lucene (Practical)
  • Apache Solr
  • Apache Solr Indexing
  • Solr Indexing continued
  • Apache Solr Searching
  • Deep Dive into Apache Solr
  • Apache Solr continued
  • Extended Features
  • Multicore
  • Administration & SolrCloud
  • Fundamentals of Search Engine and Apache Lucene
  • Analyzers in Lucene
  • Exploring Apache Lucene
  • Apache Lucene Demonstration
  • Apache Lucene advanced
  • Advanced Topics of Apache Lucene (Practical)
  • Apache Solr
  • Apache Solr Indexing
  • Solr Indexing continued
  • Apache Solr Searching
  • Deep Dive into Apache Solr
  • Apache Solr continued
  • Extended Features
  • Multicore
  • Administration & SolrCloud
  • Introduction to Linux
  • File Management Files and Processes
  • Introduction to Shell Scripting
  • Conditional, Looping Statements, and Functions
  • Text Processing
  • Scheduling Tasks
  • Advanced Shell Scripting
  • Database Connectivity
  • Linux Networking
  • Introduction to Linux
  • File Management Files and Processes
  • Introduction to Shell Scripting
  • Conditional, Looping Statements, and Functions
  • Text Processing
  • Scheduling Tasks
  • Advanced Shell Scripting
  • Database Connectivity
  • Linux Networking
  • What is Kafka – An Introduction
  • Multi-broker Kafka Implementation
  • Multi-node Cluster Setup
  • Integrate Flume with Kafka
  • Kafka API Producers & Consumers
  • Introduction to SQL
  • Database Normalization and Entity Relationship Model
  • SQL Operators
  • Working with SQL - Join, Tables, and Variables
  • Deep Dive into SQL Functions
  • Working with Subqueries
  • SQL Views, Functions, and Stored Procedures
  • Deep Dive into User-defined Functions
  • SQL Optimization and Performance
  • Advanced Topics
  • Managing Database Concurrency
  • Programming Databases using Transact - SQL
  • Microsoft Courses - Study Material

Big Data Data Science Course – FAQs

One who studies data science needs to analyze a large amount of data in order to derive meaningful insights that are essential for business development. So, with the requirement to use and analyze massive data, Hadoop acts as a common platform and thus, is essential for data science.

If you have done your engineering and want to pursue your career in big data, then you can perfectly go and achieve your dream career. If you think that companies hire only experienced professionals for big data jobs, then it is completely a myth. As a fresher, you can learn the basics of programming and join this Big Data Data Science Course and upskill your knowledge in this highly demanded field.

Data science is known to experience tremendous global market growth from the period of 2021 to 2026. Thus, as there are tremendous opportunities available in this advanced technology, data science is a good career option for both freshers as well as experienced professionals.

Yes, it is very important to learn coding skills before you get into the world of big data. As a big data analyst, you need to code to perform statistical analysis with the available massive data sets. So, before you join this Big Data Data Science Masters Course, you can invest your time in learning programming languages such as R, Python, C++, or Java.

Reviews