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Data Management - Data Analytics with Tensorflow

Essay by   •  April 20, 2019  •  Term Paper  •  1,504 Words (7 Pages)  •  58 Views

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Data Management - Data Analytics with TensorFlow

Data Management is the practice of organizing and maintaining data processes to meet ongoing information lifecycle needs. The use of data management and analytics is on the rise as more and more companies realize the enormous impact this can have on their businesses. TensorFlow is one of the best data analytics frameworks and has been adopted by several companies.

This 3-day course provides students with a strong foundation in data management and data analytics using TensorFlow. After completing this course, students will be able to plan for data management, prepare science data to share, install and configure TensorFlow

A well-designed data management provides a solid foundation for bringing executives, technology professionals, data scientists, and managers into line with one another so the organization’s data can be used to give organization a real competitive edge.

  • Data engineers that need a tool for building distributed workflows of complex data transformations.

TensorFlow is arguably one of the best deep learning frameworks and has been adopted by several giants such as Airbus, Twitter, IBM, and others mainly due to its highly flexible and modular system architecture. Of course, considering it was developed at Google, engineers there are constantly updating it and adding more features. Don’t expect TensorFlow to lose steam anytime soon.

TensorFlow is an incredibly agile tool that will continue to drive forward advancement in machine learning and artificial intelligence. 

Tensorflow is an open source software library for numerical computation using data flow graphs that enables machine learning practitioners to do more data-intensive computing. It provides a robust implementation of some widely used deep learning algorithms and has flexible architecture.

VALUE OF DATA MANAGEMENT

Data Realities • Data Management Role • Scientists • Data Stewards • Managers

PLANNING FOR DATA MANAGEMENT

What is a Data Management Plan (DMP)? • Why Prepare a DMP? • Where should you Begin • Revisit and Continuously Improve • Components of a DMP • Information about Data & Format

BEST PRACTICES FOR PREPARING SCIENCE DATA TO SHARE

Importance of Well-Managed Data • Example: National Tamarisk Habitat • Example: Python Maps • Importance of Well-Managed Data • Benefits of Good Data Management Practices • Fundamental Practice • Example of Poor Data Practice for Collaboration and Sharing

FROM DATA TO DECISIONS – GETTING STARTED WITH TENSORFLOW

From data to decision: Titanic example • General overview of TensorFlow • Installing and configuring TensorFlow • TensorFlow computational graph • TensorFlow programming model • TensorFlow data model • Visualizing through TensorBoard • Getting started with TensorFlow: linear regression and beyond

PUTTING DATA IN PLACE – SUPERVISED LEARNING FOR PREDICTIVE ANALYTICS

Supervised learning for predictive analytics • Linear regression for predictive analytics: revisited • Logistic regression for predictive analytics • Random forests for predictive analytics • SVMs for predictive analytics • A comparative analysis

CLUSTERING YOUR DATA – UNSUPERVISED LEARNING FOR PREDICTIVE ANALYTICS

Unsupervised learning and clustering • Using K-means for predicting neighborhood • Using K-means for clustering audio files • Using unsupervised k-nearest neighborhood (kNN) for predicting nearest neighbors

USING REINFORCEMENT LEARNING FOR PREDICTIVE ANALYTICS

Reinforcement learning • Reinforcement learning for predictive analytics • Notation, policy, and utility in RL • Developing a multi-armed bandit's predictive model • Developing a stock price predictive model


Data management is the practice of organizing and maintaining data processes to meet ongoing information lifecycle needs. Emphasis on data management began with the electronics era of data processing, but data management methods have roots in accounting, statistics, logistical planning and other disciplines that predate the emergence of corporate computing in the mid-20th century.

 Demonstrate your broad skill sets in SQL administration, building enterprise-scale data solutions, and leveraging business intelligence data – both on-premises and in cloud environments.

A postgraduate specialisation in data management and analytics will provide you with a strong foundation in big data analytics and data science. It will help you build skills and to work with state-of-the-art tools to analyse large amounts of data of a variety of types (big data) to uncover hidden patterns, unknown correlations and other useful information. Foundational technologies such as database management and business intelligence used for both operational (OLTP) and decision-support (OLAP) purposes will also be covered. You will have the opportunity to engage in areas of study including knowledge discovery and data mining, advanced data models, statistical natural language processing, knowledge management systems and computational statistical methods.

A well-designed data strategy and roadmap provide a solid foundation for bringing executives, technology professionals, data scientists and managers into line with one another so that your organization’s data can be used to give you a real competitive edge. Our Data Strategy & Roadmap Design services provide you with fast-moving insights into the true potential of your data.

Demonstrate your broad skill sets in SQL administration, building enterprise-scale data solutions, and leveraging business intelligence data – both on-premises and in cloud environments.

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