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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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 is an innovator first, and then a Data Scientist & Software Architect. He has integrated expertise in business, product, technology and management. Tom has been involved in creating category defining new products in AI and big data for different industries, which generated more than hundred million revenue cumulatively, and served more than 10 million users.
As a Data Scientist and Software Architect Tom has extensive experience in data science, engineering, architecture and software development. To date Tom has accumulated over a decade of experience in R, Python & Linux Shell programming.
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.
