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Course Overviews

UMBC Training Centers’ Big Data Analytics Training Program was designed to help individuals and organizations gain the technical and managerial skills required to conduct data analytic studies; evaluate, plan, manage and complete data analytics projects; develop custom data analytic software; and administer and maintain analytic systems at scale. View upcoming Big Data Courses

General Audience

  • Introduction to Data Analytics and Big Data

    The purpose of this course is for a student to get a broad familiarity with the relevant concepts of data analytics and data science and how they are applied to a wide range of business, scientific and engineering problems. The course will also explore the unique challenges of doing data analytics at very large scales, i.e. “Big Data”. Click for more information

Data Analysts / Data Scientists (map)

  • Introduction to Machine Learning

    In recent years industry, not just academia, has found that creating powerful data models provides the next level of value past traditional business intelligence. This course focuses on state of the art machine learning techniques combined with a practical approach designed to teach you to process your data and build models using Python’s scikit-learn. In this class you will learn to load and analyze your data with Pandas (a data analysis library), build visualizations with pyplot, and create predictive models using scikit-learn. Click for more information

  • Applied Data Analytics and Machine Learning

    This course helps students develop a broad familiarity with primary tools and techniques in modern data analytics and machine learning and how they are applied to a wide range of business, scientific and engineering problems. Click for more information

  • Deep Learning with TensorFlow

    TensorFlow is an open source machine learning library from Google designed for numerical computation using data flow graphs. Nodes represent mathematical operations, while edges in the graphs represent tensors being passed between nodes. While this framework lends itself particularly well to deep learning with neural networks, any framework can be added to the graph, allowing for extreme flexibility. Click for more information

  • Introduction to Data Visualization

    We are constantly faced with a vast amount of complex information – often more than we can handle. Well-designed visual interpretations of data improve comprehension, communication, and decision making. This workshop introduces data methods and techniques that increase the understanding of complex data. The focus is on conveying ideas effectively with visually appealing charts, graphs and maps. Participants will learn to craft clear, meaningful pictures of complex statistics and publicly available data through the creation of effective graphs and charts. Click for more information

  • Introduction to SQL

    This course is designed to give users an understanding of SQL Language. The course covers SQL commands for DML, DDL, Query, and Transaction Control operations. Click for more information

  • Introduction to Statistics

    This course is an introduction to statistical methods common to engineering, science and social sciences applications. Click for more information

  • Data Analysis with SAS

    This objective of this course is to provide students with a strong foundation in fundamental concepts of statistics that are both theoretical and applied. The course will teach enough statistical theory so that students can become educated consumers of analytical methodology, with an emphasis on application of these techniques to reach sound conclusions from real-world data. Click for more information

  • Data Analysis with Excel

    This objective of this course is to provide students with a strong foundation in fundamental concepts of statistics that are both theoretical and applied. The course will teach enough statistical theory so that students can become educated consumers of analytical methodology, with an emphasis on application of these techniques to reach sound conclusions from real-world data. The material will begin with basic concepts and methods, such as probability, descriptive statistics, exploratory analysis, and inferential testing. The course progresses to more complex material, such as regression modeling. Analytical challenges unique to large and/or heterogeneous datasets will also be explored. All analytical techniques will be illustrated with examples using Microsoft Excel. Students will analyze a variety of real world sample data sets during the course. Click for more information

  • Applied Data Science and Big Data Analytics

    Business success in the information age is predicated on the ability of organizations to convert raw data coming from various sources into high-grade business information. To stay competitive, organizations have started adopting new approaches to data processing and analysis.  For example, data scientists are turning to Apache Spark for processing massive amounts of data using Spark’s distributed compute capability along with its built-in machine learning library, or switching from proprietary and costly solutions to the free R programming language. Click for more information

  • Data Analysis for Cyber & IT Professionals

    This course is offered in a number of variants, each of which focuses on data within a specific industry or domain (e.g. finance, health care, marketing, and IT/Cyber). The IT/Cyber course focuses on the analysis of data within an enterprise IT infrastructure, to be analyzed for the purposes of monitoring the health and security of operational systems and networks; to detect threats or breaches of systems and networks; penetration testing; forensic analysis; and incident response. Click for more information

  • Hortonworks HDP Analyst: Apache HBase Essentials

    This course is designed for big data analysts who want to use the HBase NoSQL database which runs on top of HDFS to provide real-time read/write access to sparse datasets. Topics include HBase architecture, services, installation and schema design. Click for more information

  • Big Data Overview

    This course provides an in-depth overview of the choices you have in processing Big Data. It introduces Big Data, the types of data you might have, approaches to working on and processing the data, and the capabilities, strengths, and weaknesses of those approaches. Click for more information

  • Introduction to Cloud Technology

    This 1-day course provides an overview of Enterprise Cloud Computing. It is aimed at a broad audience including technology managers. Cloud computing models are discussed, including public, private and hybrid clouds. Major Cloud platforms such as Amazon AWS and Microsoft Azure are analyzed. Important issues such as Compliance, Security and Legacy system migration are discussed. Click for more information

  • AWS & Cloud IT Foundations

    This course introduces students to the Cloud Computing value proposition; Cloud Computing solution models, and core Amazon Web Services (AWS) services and foundational technologies. Course attendees are provided with insights that will enable them to intelligently translate their organization’s business requirements into Cloud and AWS-based IT solutions. Click for more information

  • SQL For Data Analytics

    This course provides you with an overview of Structured Query Language (SQL) so that you can quickly begin working with and analyzing data with other data science tools. Before you can analyze data, you need to have the correct data. Many organizations store their data in structured databases and SQL is the language of choice to extract, manipulate, filter, and generally wrangle that data. Click for more information

  • Data Visualization with Tableau

    This course introduces Tableau with an emphasis on creating powerful visualizations with your data. We will connect to data sources and perform basic filtering before displaying the data. The class is a mixture of lecture and hands-on labs. Click for more information

  • Data Analytics with Excel

    The objective of this course is to provide students with a strong foundation in fundamental concepts of statistics and analytics that are both theoretical and applied. The course will teach enough statistical theory so that students can become educated consumers of analytical methodology, with an emphasis on application of these techniques to reach sound conclusions from real-world data. Click for more information

  • Data Analysis with Python, SQL and Excel

    This course takes a practical approach to understanding key methods for Data Analytics by using common tools: SQL, Excel, and Python. Participants will perform common analytics activities: Interacting with a SQL database; writing an ETL script; outputting to Excel; use Excel and Tableau for data visualization. Click for more information

  • Python for Data Science

    This course introduces the Python language to students who want to use Python as a tool for their data science initiatives. The goal is to become proficient enough with the Python language to leverage powerful Data Science packages such as Pandas and matplotlib. Click for more information

  • Data Visualization with Matplotlib & Seaborn

    Matplotlib is a data visualization library for Python. As part of the SciPy data analysis library it is widely used to create data graphics. However, Matplotlib is older than the pandas library, the most common Python library for data frame manipulation. The Matplotlib library requires some extra steps when plotting data from pandas data frames that sometimes make it more cumbersome to use. Seaborn was created to address some of those issues. Click for more information

  • Machine Learning & Data Science with Python

    In recent years industry, not just academia, has found that creating powerful data models provides the next level of value past traditional business intelligence. This course focuses on state of the art machine learning techniques combined with a practical approach designed to teach you to process your data and build models using Python’s scikit-learn. Click for more information

  • Hadoop Programming

    This training course introduces the students to Apache Hadoop and key Hadoop ecosystem projects: Pig, Hive, Sqoop, and Spark. This training course is supplemented by a variety of hands-on labs that help attendees reinforce their theoretical knowledge of the learned material and gain practical experience of working with Apache Hadoop and related Apache projects. Click for more information

  • R Programming for Data Science

    Students will gain a strong foundation in applied data analytics and programming methodology. Data scientists are in demand and companies are struggling to find the perfect combination of applicants skilled in programming, math and statistics, and data modeling and wrangling. This course focuses on gaining the programming skills in R to explore data, clean and prep data sets, and conduct statistical analysis. Click for more information

Developers (map)

  • Hortonworks HDP Developer: Java

    This advanced four-day course provides Java programmers a deep-dive into Hadoop 2.0 application development. Students will learn how to design and develop efficient and effective MapReduce applications for Hadoop 2.0 using the Hortonworks Data Platform. Students who attend this course will learn how to harness the power of Hadoop 2.0 to manipulate, analyze and perform computations on their Big Data. Click for more information

  • Hortonworks HDP Developer: Apache Pig and Hive

    This course is designed for developers who need to create applications to analyze Big Data stored in Apache Hadoop using Pig and Hive. Topics include: Hadoop, YARN, HDFS, MapReduce, data ingestion, workflow definition, using Pig and Hive to perform data analytics on Big Data and an introduction to Spark Core and Spark SQL. Click for more information

  • Hortonworks HDP Developer: Enterprise Spark I

    This course is designed as an entry point for developers who need to create applications to analyze Big Data stored in Apache Hadoop using Spark. Topics include: An overview of the Hortonworks Data Platform (HDP), including HDFS and YARN; using Spark Core APIs for interactive data exploration; Spark SQL and DataFrame operations; Spark Streaming and DStream operations; data visualization, reporting, and collaboration; performance monitoring and tuning; building and deploying Spark applications; and an introduction to the Spark Machine Learning Library. Click for more information

  • Hortonworks HDP Analyst: Data Science

    This course provides instruction on the processes and practice of data science, including machine learning and natural language processing. Included are: tools and programming languages (Python, IPython, Mahout, Pig, NumPy, pandas, SciPy, Scikitlearn), the Natural Language Toolkit (NLTK), and Spark MLlib. Click for more information

  • Big Data Analytics for Data Engineers

    This is an intensive, hands-on introduction to Data Engineering as it relates to Big Data applications. Participants will leverage numerous programming languages to gain hands-on experience with toolsets required in Big Data applications. Click for more information

  • Data Science with Apache Spark

    Success of many organizations depends on their ability to derive business insights from massive amount of raw data coming from various sources. Apache Spark offers many engineering improvements over the traditional MapReduce programming model as implemented in Hadoop by providing multi-pass in-memory processing of data which boosts the overall performance of your ETL and machine-learning algorithms. Click for more information

  • Advanced Topics in Artificial Intelligence

    Building from the learning obtained in prior courses in AI/ML, this course will review advanced tools, methods and trends in AI/ML. The course is a mix of lecture, demonstration and hands-on exercises. The course typically features guest lecturers from research, academia and industry. Advanced computational models, open source projects and cloud services will be considered. Case studies from recent publications and conferences will also be analyzed. An optional Capstone project can also be included for corporate and government cohort groups. Click for more information

Administrators (map)

  • Hortonworks HDP Operations: Administration Foundations

    This course is intended for systems administrators who will be responsible for the design, installation, configuration, and management of the Hortonworks Data Platform (HDP). The course provides in-depth knowledge and experience in using Apache Ambari as the operational management platform for HDP. This course presumes no prior knowledge or experience with Hadoop. Click for more information

  • Hortonworks HDP Operations: Hadoop Administration 2

    This course is designed for experienced administrators who manage Hortonworks Data Platform (HDP) 2.3 clusters with Ambari. It covers upgrades, configuration, application management, and other common tasks. Click for more information

  • Hortonworks HDP Operations: Security

    This course is designed for experienced administrators who will be implementing secure Hadoop clusters using authentication, authorization, auditing and data protection strategies and tools. Click for more information

  • Big Data and Data Analytics for Managers and Business Users

    The purpose of this course is for a student to get a broad familiarity with the relevant concepts of data analytics and data science and how they are applied to a wide range of business, scientific and engineering problems. The course will also explore the unique challenges of doing data analytics at very large scales, i.e. “Big Data”. Click for more information

  • Artificial Intelligence for Managers

    Artificial Intelligence is everywhere and today’s business managers need to understand and embrace it. AI can help businesses reengineer their processes for higher revenue, higher customer satisfaction and lower cost. This course teaches the fundamentals of artificial intelligence and how machine learning is a crucial component of the AI infrastructure. Participants will learn how machine learning differs from traditional rule based software and explore via examples and live demonstration how AI and ML can be applied to business applications. Click for more information

Architects

  • Data Science for Solution Architects

    Business success of organizations in the information age largely depends on their ability to cost-effectively convert massive amounts of raw data coming from various sources into high-grade business information. In many organizations, Solution Architects are called upon to provide the much needed “data-to-information” conversion solutions. This class aims at helping Solution Architects and other IT practitioners understand the value proposition, methodology and techniques of the emerging Data Science discipline that is positioned to tackle many of the challenges posed by the modern data-driven business. The class also introduces the students to a number of existing production-ready technologies and capabilities that enable enterprises to build cost-efficient Big Data processing solutions. Click for more information

  • Big Data on AWS

    Big Data on AWS introduces you to cloud-based big data solutions such as Amazon Elastic MapReduce (EMR), Amazon Redshift, Amazon Kinesis and the rest of the AWS big data platform. In this course, we show you how to use Amazon EMR to process data using the broad ecosystem of Hadoop tools like Hive and Hue. We also teach you how to create big data environments, work with Amazon DynamoDB, Amazon Redshift, and Amazon Kinesis, and leverage best practices to design big data environments for security and cost-effectiveness. Click for more information

  • Data Warehousing on AWS

    Data Warehousing on AWS introduces you to concepts, strategies, and best practices for designing a cloud-based data warehousing solution using Amazon Redshift, the petabyte-scale data warehouse in AWS. This course demonstrates how to collect, store, and prepare data for the data warehouse by using other AWS services such as Amazon DynamoDB, Amazon EMR, Amazon Kinesis Firehose, and Amazon S3. Additionally, this course demonstrates how to use business intelligence tools to perform analysis on your data. Click for more information

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