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Data Analyst positions require familiarity with the collection of data, manipulation, cleaning and generating insight from data. During the training program, candidates with be trained to think in terms of gathering data insight and manipulating data to provide dashboards, reports and analysis for making decisions.

Professional Diploma in Big Data Analyst

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Professional Diploma for Graph and Big Data Analytics – Graph analytics is one the key upcoming trends identified by Garner for 2019 for Data and Analytics and is currently gaining traction in many industries and supported by the largest companies in the world. In this course, you will learn Python from basics to a level sufficient for you to develop applications for Big Data and Analytics. This knowledge will provide you the basis of working on Graph databases, Big Data platforms, Machine Learning and Artificial Intelligence initiatives in organizations embarking on implementing solutions based the latest technology trends.

Fundamentals

Python for Data Analysis

Intermediate

Big Data and Graph Databases

Advanced

Machine Learning and Artificial Intelligence

Proposed modules

The candidates will be exposed to the latest tools being used in the industry such as Hadoop and its components, Python for data manipulation, Graph Databases to store data and its relationships and machine learning and AI concepts that they can apply in the future job positions. They will also be provided with exercises that allow critical thinking to generate insight from the data.

  1. 1
    Fundamentals - Python for Data Analysis
    • History of Programming
    • Programming basics
    • Why program with Python
    • Introduction to Python
    • Python – First Python program
    • Python – Understanding Data Types
    • Python – Working with Strings
    • Python – Understanding variables
    • Python – Math Operators and Boolean logic
    • Python – Understanding Lists
    • Python – List Methods
    • Python – Understanding Tuples
    • Python – Understanding Dictionaries
    • Python – modules
    • Python – modules (creating modules)
    • Python – Conditional Statements
    • Python – While Loops
    • Python – For Loops
    • Python – More loops
    • Python – Functions
    • Python – Using *args and **kwargs
    • Python – Classes and Objects
    • Python – Understanding Classes and instance variables
    • Python – Understanding Inheritance
    • Python – Applying Polymorphism to classes
    • Python – debugging
    • Python – interactive console
    • Python Best practices
  2. 2
    Intermediate - Big Data and Graph Databases
    • Introduction to Big Data and Hadoop
    • Hadoop Architecture Distributed Storage (HDFS) and Yarn
    • Data Ingestion into Big Data Systems and ETL
    • Distributed Processing MapReduce and Pig
    • Apache Hive
    • HBase NoSQL database
    • Basics of functional programming and SCALA
    • Apache Spark
    • Spark Core Processing RDD
    • Spark SQL Processing DataFrames
    • SparkMLLib modelling BigData with Spark
    • Stream Processing Frameworks and Spark Streaming
    • Spark GraphX
    • Alternate Graph engines
    • Neo4j Introduction
    • Neo4j Cypher
    • Neo4j Graph Analytics
  3. 3
    Advanced - Machine Learning and Artificial Intelligence
    • Introduction to Artificial Intelligence
    • Python in Artificial Intelligence
    • Data Visualization
    • Data Analytics
    • Data Exploration
    • Supervised Learning
    • Supervised Learning Classification
    • Unsupervised Learning
    • Developing a Machine Learning model
    • Deploying a machine learning solution

Participants will be evaluated based on project work and multiple-choice questions.

  • Python
    • Application Development Project
    • 50 multiple choice and true/false questions. Closed book.

    Duration – 2 hours.
    Passing Rate – 65%

  • Big Data
    • Big Data Development Project
    • 50 multiple choice and true/false questions. Closed book.

    Duration – 4 hours.
    Passing Rate – 65%

  • Artificial Intelligence
    • AI Model Development Project
    • 50 multiple choice and true/false questions. Closed book.

    Duration – 4 hours.
    Passing Rate – 65%

Yes I’m Interested

    We focus on accreditation and certification of training programs, recognise continuous professional capacity enhancement by offering certified designated credentialing and consistently share strategic insights, new wave technologies and latest industrial development.

    ADDRESS

    Convergence Certification and Skill Development Council
    CCSD COUNCIL SRO
    Belehradska 858/23 Prague 2, 120 00
    Czech Republic

    Munies Pillai - Secretary General

    [email protected]
    +6012 3242 885
    +65 8747 8735

    PHONE

    +420 257 325 117
    Working Hours - 9:00AM - 5:00PM (CET)

    EMAIL

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