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A process of inspecting, cleansing, transforming and modeling data with the goal of discovering useful information, informing conclusion and supporting decision-making.The journey of making millions of raw data become valuable is the job of a data scientist.

Python Programming master class

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Fundamentals

Python Programmin Masterclass

Intermediate

Big Data and Analytics

Advanced

Artificial Intelligence for Data Analytics

Proposed modules
  1. 1
    Python Programmin Masterclass

    Day 1
    • History of Programming
    • Programming basics
    • Why program with Python
    Day 2
    • Introduction to Python
    • Python – First Python program
    • Python – Understanding Data Types
    • Python – Working with Strings
    Day 3
    • Python – Understanding variables
    • Python – Math Operators and Boolean logic
    Day 4
    • Python – Understanding Lists
    • Python – List Methods
    Day 5
    • Python – Understanding Tuples
    • Python – Understanding Dictionaries
    • Python – modules
    Day 6
    • Python – modules (creating modules)
    • Python – Conditional Statements
    • Python – While Loops
    Day 7
    • Python – For Loops
    • Python – More loops
    • Python – Functions
    Day 8
    • Python – Using *args and **kwargs
    • Python – Classes and Objects
    • Python – Understanding Classes and instance variables
    Day 9
    • Python – Understanding Inheritance
    • Python – Applying Polymorphism to classes
    • Python – debugging
    Day 10
    • Python – interactive console
    • Python Best practices

  2. 2
    Big Data and Analytics

    Day 1
    • 1.Introduction to Big Data and Hadoop
    • 2.Hadoop Architecture Distributed Storage (HDFS) and Yarn
    • 3.Data Ingestion into Big Data Systems and ETL
    Day 2
    • 4.Distributed Processing MapReduce and Pig
    • 5.Apache Hive
    • 6.HBase NoSQL database
    Day 3
    • 7.Basics of functional programming and SCALA
    • 8.Apache Spark
    • 9.Spark Core Processing RDD
    Day 4
    • 10.Spark SQL Processing DataFrames
    • 11.SparkMLLib modelling BigData with Spark
    • 12.Stream Processing Frameworks and Spark Streaming
    Day 5
    • 13.Spark GraphX
    • 14.Alternate Graph engines
    • 15.Neo4j Introduction
    Day 6
    • 16.Neo4j Cypher
    • 17.Neo4j Graph Analytics

  3. 3
    Machine Learning and Artificial Intelligence for Data Analytics

    Day 1
    • Introduction to Artificial Intelligence
    • Python in Artificial Intelligence

    Day 2
    • Data Visualization
    • Data Analytics

    Day 3
    • Data Exploration

    Day 4
    • Supervised Learning
    • Supervised Learning Classification
    • Unsupervised Learning

    Day 5
    • Developing a Machine Learning model

    Day 6
    • 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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