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Data Science Primer | Python Technologies, Tools & Modern Roles in the Data-Driven Enterprise
Description
Data Science Primer Course Introduction
Welcome to the Data Science Primer course, designed to be your stepping stone into the dynamic and ever-evolving realm of data science. This course is meticulously crafted to offer a broad yet comprehensive introduction, catering to a diverse audience that includes Business Analysts, Data Architects, Developers, and anyone keen on grasping the fundamental aspects of data science.
Throughout this course, you will dive into the intricacies of key topics such as the fundamentals of Big Data, the basics of Artificial Intelligence, and an array of other critical concepts. Our curriculum is structured to provide you with an immersive learning experience, equipping you with a robust foundation in the principles and practices of modern data science.
By the conclusion of this course, you will possess the foundational knowledge and skills essential for further advanced studies and practical applications in the field. Whether you are a budding Data Analyst, a seasoned Developer, or an integral member of a Modern Data Team, this course is your ideal launchpad for a successful journey in the fascinating world of data science.
Data Science Primer Course Objectives
- Understand the Basics: Grasp the fundamental concepts of Big Data and Artificial Intelligence, setting the stage for deeper learning.
- Master Key Tools and Techniques: Learn to use tools like MyBinder, JupyterLab, and TensorFlow for data analysis, machine learning model building, and deep learning applications.
- Explore Critical Algorithms: Dive into clustering algorithms, including Mahout, MLLib, SciKit, and Madlib, and understand their applications in real-world scenarios.
- Navigate Business Rule Systems: Gain insights into Business Rule Systems such as Drools, JRules, and Pegasus, crucial for business data analysis.
- Hands-On Learning: Participate in hands-on demonstrations and exercises that reinforce your learning through practical application.
- Discover the Hadoop Ecosystem: Lean into the Hadoop ecosystem, learning about its components like HDFS, YARN, and Spark, and explore different distributions like Cloudera and Hortonworks.
Prerequisites
- Attendees should have prior exposure to Enterprise Information Technology and some familiarity with Relational Databases.
Data Science Primer Outline
Exploring the Hadoop Ecosystem
- HDFS: Hadoop Distributed File System
- Resource Negotiators: YARN, Mesos, and Spark; ZooKeeper
- Hadoop Map/Reduce
- Spark
- Hadoop Ecosystem Distributions: Cloudera, Hortonworks, OpenSource
Artificial Intelligence and Business Systems
- Artificial Intelligence: Myths, Legends, and Reality
- The Math
- Statistics
- Probability
- Clustering Algorithms, Mahout, MLLib, SciKit, and Madlib
- Business Rule Systems: Drools, JRules, Pegasus
The Modern Data Team
- Agile Data Science
- NOSQL Data Architects and Administrators
- Developers
- Grid Administrators
- Business and Data Analysts
- Management
- Evolving your Team
- Growing your Infrastructure
Supervised Learning with Big Data
- Demo
- Exploratory Data Analysis (Demo – Credit Card Risk Model)
- Wrangling and Cleaning Data
- Building a Supervised Machine Learning Model with MyBinder and JupyterLab
Deep Learning with Big Data
- Exploratory Data Analysis
- Data Cleaning and Wrangling
- Build a Deep Learning Model
- Demo with TensorFlow
$895.00
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1 Day Course |