Computational Thinking and Big Data Training

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Building ETL and Data Pipelines with Bash, Airflow, and Kafka focuses on designing and automating data workflows. It covers data extraction, transformation, and loading using Bash scripts, Apache Airflow for orchestration, and Kafka for real-time data streaming.

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What is Computational Thinking and Big Data Training about?

Computational thinking is a critical skill for breaking down complex problems into structured, logical solutions, often supported by technology. When combined with Big Data, it empowers learners to design algorithms, manage data pipelines, and extract insights from massive datasets. This course introduces the principles of computational thinking (abstraction, decomposition, pattern recognition, and algorithm design) and connects them with Big Data tools and frameworks. Through case studies and practical examples, learners will understand how computational approaches are applied to data-intensive challenges in fields like healthcare, business, and engineering.

What are the objectives of Computational Thinking and Big Data Training ?

Understand the core principles of computational thinking.
Apply problem decomposition and algorithm design to real-world problems.
Explore the role of Big Data in solving large-scale challenges.
Gain exposure to Big Data tools like Hadoop, Spark, and cloud platforms.
Recognize industry applications of computational thinking with Big Data.

Who is Computational Thinking and Big Data Training for?

Students and beginners entering data science or computer science.
Professionals seeking to strengthen problem-solving and analytical skills.
Data analysts and engineers working with large datasets.
Educators teaching computational methods and data concepts.
Anyone interested in bridging critical thinking with technology.

What are the prerequisites for Computational Thinking and Big Data Training?

Prerequisites:

Basic understanding of mathematics and logic.
Familiarity with programming concepts (Python or R preferred).
Introductory knowledge of data analytics or databases.
Interest in problem-solving and data-driven innovation.
Curiosity about Big Data tools and applications.

Learning Path:

Introduction to Computational Thinking and Its Principles
Problem Decomposition, Abstraction, and Algorithm Design
Overview of Big Data Concepts and the 5 Vs
Big Data Tools and Frameworks (Hadoop, Spark, Cloud Platforms)
Applying Computational Thinking to Big Data Case Studies

Related Courses:

Introduction to Big Data
Data Science Research Methods (Python or R Edition)
Apache Spark Fundamentals
Data Engineering on Google Cloud Platform

Available Training Modes

Live Online Training

3 Days

Course Outline Expand All

Expand All

Module 1- Data in R

Introduction to RStudio

Dataframes

Subjects and variables

Categorical data

Quantitative data

Module 2- Visualizing relationships

Scatter plots

Visualising relationships using ggplot

Describing relationships between different types of variables

Interpreting plots

Module 3- Manipulating and joining data

Filter

Select

Mutate

Group by

Join

Module 4- Transforming data and dimension reduction

Why transform data?

Principal Component Analysis (PCA)

Summarising the advancement of physics using TF-IDF

Module 5- Summarising data

Population, parameters, samples, and statistics

Point estimates

Interval estimates

Counting k-mers in genomes

Module 6- Introduction to Java

The coding environment

Object-oriented programming

Module 7- Graphs

Social networks as graphs

Graph basics

Graph algorithms

Graphs in Java

Module 8- Probability

Probability space

Random variables and expectations

Natural logarithm

Module 9- Hashing

Hashing basics

Advanced hashing

Hashing in practice

Who is the instructor for this training?

The trainer for this Computational Thinking and Big Data Training has extensive experience in this domain, including years of experience training & mentoring professionals.

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