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Data Science & Big Data Analytics

Comprehensive training in Data Analytics and Big Data

Practical, hands-on experience with industry projects

Learn skills that are in high demand globally

Full coverage of the Data Analytics lifecycle

40-hour intensive course

Flexible learning schedules to fit your needs

Convenient and affordable payment plans

4.6/5

2365 Enrolled

Overview

What our training includes

  • Prepares professionals to leverage Big Data and Analytics for business solutions
  • Provides foundational and advanced training in Data Science and Big Data Analytics
  • Offers hands-on experience with real-world data analytics challenges
  • Equips participants with skills to apply theoretical concepts to practical scenarios
  • Enhances data analytical knowledge and ability to use analytic tools effectively
  • Empower professionals to gain a competitive edge in the job market
  • Demonstrates expertise in handling large datasets and sophisticated analytics techniques

Learning Objectives

Upon finishing the training, you will be able to:

  • 1

    Gain a deep understanding of Data Science and Data Analytics concepts

  • 2

    Understand basic and advanced analytic methods

  • 3

    Apply appropriate analytic techniques and tools to analyse big data

  • 4

    Create statistical models, and identify insights

  • 5

    Manage a data analytics project through the entire lifecycle

  • objective-image

    Ready to get started?

  • Prerequisites

    Somethings you need before starting

    • Working knowledge of C#, MVC, HTML 5
    • A strong quantitative background with a solid understanding of basic statistics
    • Strong programming skills, particularly in languages like Python and R
    • Experience with a scripting language, such as Java, Perl, or Python (or R)
    • Many of the lab examples taught in the course use R (with an RStudio GUI), which is an open-source statistical tool and programming
    • Experience with SQL
    • Familiarity with cloud computing and big data technologies, such as Hadoop, Spark, and NoSQL databases

    Overall ratings by our students

    Upcoming sessions

    Our Trainers

    We, at Learners Point, take immense pride in our teaching methods and instructors. Our instructors are some of the best experts in their fields and employ a practical approach to learning. Many of them are globally recognised and have a diverse set of experience in their field of expertise. You are always sure to have the best in the industry as your teachers who are ready to guide you at every step and make the experience informative yet enjoyable. Apart from the focus on learning your chosen course, our instructors also encourage students to develop communication skills and interpersonal skills necessary to excel in the practical world.

    Our cutting edge teaching methods make every program an immersive and productive experience for the learners. Our learning methods are research-driven and are continuously updated to stay relevant to present times as well as the future. You will enjoy practical applications of everything learned through theory and regular mock examinations to help monitor your progress. Our courses are led by an instructor in a classroom setup and we do offer online high-quality sessions as well for individuals. We also monitor the training sessions with a progress tracker to maintain high standards of instruction & ethics.

    Our Trainers

    KHDA Certificate

    Earn a KHDA attested Course Certificate. The Knowledge and Human Development Authority (KHDA) is the educational quality assurance and regulatory authority of the Government of Dubai, United Arab Emirates.

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    Learners Point Certificate

    Earn a Course Completion Certificate, an official Learners Point credential that confirms that you have successfully completed a course with us.

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    Related courses

    Curriculum

    • Business Analytics vs Big Data vs Data Science 
    • Components of Data Science: Venn Diagram 
    • Understanding Data Science Pipeline
    • Roles And Jobs In Data Science In The Market 
    • Data Science Team Structure
    • Business/Domain knowledge 
    • Hacking/Programming Skills 
    • Statistics
    • Machine Learning
    • Reading From Existing Data from Flat Files And Database 
    • APIs
    • Web Scraping
    • Other Data-Gathering Methods 
    • Hands-On Importing Data
    • Basic Statistics 
    • Exploratory Graphs 
    • Exploratory Statistics
    • Programming Basics 
    • Python Overview
    • R Overview
    • SQL
    • Hypothesis Testing 
    • Confidence 
    • Validating
    • Decision Trees
    • Ensembles
    • K-nearest neighbors (KNN) 
    • Naive Bayes Classifiers 
    • Artificial Neural Networks
    • Introduction To Visual Analytics 
    • Interpretability
    • Actionable Insights
    • Various Graphical Representations
    • Data Science Learning Sources
    • Data Science Relevance In Your Career
    • How Should a Data Scientist’s Resume Look Like 
    • Tips On Job Applications

    Big Data Introduction

    • What is Big Data?
    • Data Analytics
    • Big Data Challenges
    • Technologies Supported By Big Data

    Hadoop Introduction

    • What is Hadoop?
    • History of Hadoop
    • Basic Concepts
    • Future of Hadoop
    • The Hadoop Distributed file system 
    • Anatomy of a Hadoop cluster 
    • Breakthroughs of Hadoop
    • Hadoop Distributions: Apache Hadoop 
    • Cloudera Hadoop
    • Hortonworks Hadoop 
    • MapR Hadoop
    • Name Node
    • Data Node
    • Secondary Name Node 
    • Job Tracker
    • Task Tracker
    • Blocks and Input Splits
    • Data Replication
    • Hadoop Rack Awareness
    • Cluster Architecture And Block Placement 
    • Accessing HDFS
    • JAVA Approach 
    • CLI Approach
    • Local Mode
    • Pseudo-Distributed Mode
    • Fully Distributed Mode
    • Pseudo Mode Installation And Configurations 
    • HDFS Basic Operation
    • Basic API Concepts
    • The Driver Class
    • The Mapper Class
    • The Reducer Class
    • The Combiner Class
    • The Partitioner Class
    • Examining a Sample MapReduce Program With Several Examples 
    • Hadoop's Streaming API
    • PIG Concepts
    • Install And Configure PIG On A Cluster 
    • PIG Vs MapReduce And SQL
    • Write Sample PIG Latin Scripts 
    • Modes Of Running PIG
    • PIG UDFs
    • HIVE Concepts
    • HIVE Architecture
    • Installing And Configuring HIVE
    • Managed Tables and External Tables
    • Joins in HIVE
    • Multiple Ways Of Inserting Data in HIVE Tables 
    • CTAS, Views, Alter Tables
    • User-Defined Functions In HIVE
    • HIVE UDF
    • SQOOP Concepts
    • SQOOP Architecture
    • Connecting To RDBMS
    • Internal Mechanism of Import/Export 
    • Import data From Oracle/MySQL to HIVE 
    • Export data to Oracle/MySQL
    • Other SQOOP Commands
    • HBase Concepts
    • ZOOKEEPER Concepts
    • HBase and Region Server Architecture 
    • File Storage Architecture
    • NoSQL vs SQL
    • Dening Schema and Basic Operations 
    • DDLs
    • DMLs
    • HBase Use Cases
    • OOZIE Concepts
    • OOZIE Architecture
    • Workflow Engine
    • Job Coordinator
    • Installing and Configuring OOZIE 
    • HPDL and XML For Creating Workflows 
    • Nodes In OOZIE
    • Action Nodes and Control Nodes
    • Accessing OOZIE Jobs Through CLI, and Web Console 
    • Develop and Run Sample Workflows in OOZIE
    • Run MapReduce Programs
    • Run HIVE Scripts/Jobs
    • FLUME Concepts
    • FLUME Architecture 
    • Installation and Configurations
    • Executing FLUME Jobs
    • Data Analytics Using Pentaho as an ETL Tool 
    • Big Data Integration with Zero Coding Required
    • MapReduce and HIVE Integration 
    • MapReduce and HBase Integration 
    • Java and HIVE Integration 
    • HIVE-HBase Integration

    Frequently asked questions

    Our Data Science and Big Data Analytics course involves using advanced techniques to analyze large and diverse datasets. This includes both structured and unstructured data. These datasets often arrive in real-time and require specialized methods to extract meaningful insights.

    The career opportunities after completing a Data Science course in Dubai are as follows:

    • Data Scientist
    • Data Analyst
    • Machine Learning Engineer
    • Artificial Intelligence Engineer
    • Market Research Analyst

    Yes, along with programming knowledge, these information are also required for professionals:

    • Working knowledge of C#, MVC, HTML 5
    • A strong quantitative background with a solid understanding of basic statistics
    • Strong programming skills, particularly in languages like Python and R
    • Experience with a scripting language, such as Java, Perl, or Python (or R)
    • Many of the lab examples taught in the course use R (with an RStudio GUI), which is an open-source statistical tool and programming
    • Experience with SQL
    • Familiarity with cloud computing and big data technologies, such as Hadoop, Spark, and NoSQL databases

    Big data analytics offers advantages to a wide range of industries, including:

    • Retail
    • Healthcare
    • Manufacturing
    • Banking and Finance
    • Supply Chain Management

    Professionals will learn the following course modules. The course modules are as follows:

    • Data Science: Areas of Study
    • Data Sources and Importing Techniques
    • Data Exploratory Analysis
    • Introduction to Applied Statistics
    • Data Science Programming and many more

    The demand for data scientists in the UAE is rising quickly. Recent data from Indeed shows a significant increase of over 50% in job postings for data scientists within the past year.

    Do you want to learn more about Learners Point Academy?

    • Learn more about courses
    • Understand about our methodology
    • Let’s talk about Corporate trainings
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