Introduction to Big Data
Virtual
Learning Style
Course
Learning Style
Intermediate
Difficulty
3 Days
Course Duration
Course Info
Certificate
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9:00 AM - 5:00 PM CST
Duration - 0 Days -
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About Individual Course:
$2,650.00
$2,650.00
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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
More Information
| Subjects | Big Data |
|---|---|
| Lab Access | No |
| Learning Style | Virtual Classroom |
| Learning Type | Course |
| Difficulty | Intermediate |
| Course Duration | 3 Days |
| Language | English |
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Course Expert:
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.
