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Introduction to Big Data

This 3-day course focuses on the Data Science training and helps the students learn about the planning and implementation of a big data strategy for a business. Besides the big data fundamentals, other important topics that you’ll learn include storing, managing, and analyzing unstructured data, choosing the right big data stores, processing large data sets, and more.

Virtual

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Intermediate

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3 Days

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This 3-day course focuses on the Data Science training and helps the students learn about the planning and implementation of a big data strategy for a business. Besides the big data fundamentals, other important topics that you’ll learn include storing, managing, and analyzing unstructured data, choosing the right big data stores, processing large data sets, and more.

Course Information

About this course:

Introduction to Big Data is an intermediate level, Data Science training based course that allows students to learn how to leverage big data analysis tools and techniques to facilitate a better business decision-making. Furthermore, students also acquire hands-on knowledge on storing data in order to regulate efficient processing and analysis, and acquire the expertise to store, manage, process, and analyze large amounts of unstructured data and develop a relevant data lake.

Course Objectives:

  • Store, manage, and analyze the unstructured data sets
  • Choose the right big data stores covering disparate data sets
  • Process large data sets through Hadoop to acquire value
  • Query large data sets in almost real time through Pig and Hive
  • Craft and execute a big data strategy for a business

Prerequisite:

  • A sound expertise of the Microsoft Windows platform

Outline

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SubjectsBig Data
Lab AccessNo
Learning StyleVirtual Classroom
Learning TypeCourse
DifficultyIntermediate
Course Duration3 Days
LanguageEnglish

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Course Expert:

Author

Tom Robertson
(Data Science Enthusiast)

Tom Robertson is a Python-based data scientist with domain knowledge in behavioral neuroscience research and non-profit development. He is currently employeed as Data Science Instructor at QuickStart Technologies. Tom is inspired by the belief that data science can improve quality of life by providing our researchers, doctors and policy-makers with new, data-driven insights.

Tom has expertise on Python, SQL, and Spark. He has worked on several libraries including but not limited to Scikit-learn, Pandas, NumPy, Matplotlib, Seaborn, SciPy, NLTK, Keras, and Tensorflow.

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