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Big Data Management and Analytics

Last Updated on August 12, 2023 by Oluwajuwon Alvina

The following article gives detailed information on big data management and analytics university of ottawa and bdma erasmus mundus reviews.

Not just that, you will also discover related posts on big data analytics & masters programme in security and cloud computing on collegelearners.

Big Data Analytics

Are you what wandering what Big Data Management and Analytics is, what Big data management techniques and whta Erasmus Mundus is all about. You are on the right path, this article will answer most of your questions about it.

What is big data management???

Big data management is the organization, administration and governance of large volumes of both structured and unstructured data. 

The goal of big data management is to ensure a high level of data quality and accessibility for business intelligence and big data analytics applications. Corporations, government agencies and other organizations employ big data management strategies to help them contend with fast-growing pools of data, typically involving many terabytes or even petabytes of information saved in a variety of file formats. Effective big data management helps companies locate valuable information in large sets of unstructured data and semi-structured data from a variety of sources, including call detail records, system logs and social media sites.

Most big data environments go beyond relational databases and traditional data warehouse platforms to incorporate technologies that are suited to processing and storing nontransactional forms of data. The increasing focus on collecting and analyzing big data is shaping new platforms that combine the traditional data warehouse with big data systems in a logical data warehousing architecture. As part of the process, the must decide what data must be kept for compliance reasons, what data can be disposed of and what data should be kept and analyzed in order to improve current business processes or provide a business with a competitive advantage. This process requires careful data classification so that ultimately, smaller sets of data can be analyzed quickly and productively. 

Background & Objectives

The First Report of the EU Data Market Study estimates that the number of data users will reach more than 1.3 million in 2020 while the overall data market will likely reach € 111 billion under high growth conditions. The last decade was marked by the digitalisation of virtually all aspects of our daily lives. Today, public and private organisations in all sectors face an avalanche of digital data on a daily basis. While at first glance this appears to be favourable for our knowledge-based society, in many ways it is a burden. Data is neither information nor knowledge. Instead, data is of great value once it has been refined and analysed, in order to address well-formulated questions, concerning problems of interest. It is only then through data-driven innovation that the economic and social benefit can be fully realised.The Erasmus Mundus Joint Master Degree Programme in Big Data Management and Analytics (BDMA) is a unique programme that fully covers all of the data management and analytics aspects of Big Data (BD), built on top of Business Intelligence (BI) foundations, and complemented with horizontal skills. It has been jointly designed and adheres to international studies, being structured to cover all the skills BI and BD specialists require.

Erasmus Mundus Master in Big Data Management and Analytics (BDMA)

Since its inception 20 years ago, business intelligence (BI) has become a huge industrial area and a significant driver of the economy. It covers many fields of science and technology (data warehousing and mining, content analysis, business process management, etc.) and requires knowledge of information systems, the Web, decision making, software engineering, innovation and entrepreneurship. The Erasmus Mundus master’s degree in Big Data Management and Analytics (BDMA), formerly the master’s degree in Information Technologies for Business Intelligence (IT4BI), coordinated by the Université Libre de Bruxelles (ULB) and with the UPC as a participant, provides students with the comprehensive training required for them to understand, learn and acquire BI skills and develop decision-making strategies for business.

Eligibility Requirements (Criteria)

  • They have been awarded a Bachelor’s degree (i.e., the equivalent of 180 ECTS) with a major in computer science, from an accredited university. The university has to be listed in the World Higher Education Database (WHED) or be included in the following university rankings:
    1. The Times Higher
    2. Academic Ranking of World Universities
    3. QS World University Rankings
  • They must be able to demonstrate proficiency in English by means of an internationally recognised test equivalent to level B2 in the Common European Framework of Reference for Languages (CEFR). The consortium will rely on how certification bodies evaluate their own equivalences against this framework, e.g., Cambridge General English FCE, IELTS (Academic) 5.5, TOEFL (paper based) 570, TOEFL (computer based) 230, TOEFL (internet based) 72, etc.

How to Apply

First StepBefore you start, be sure you have the following documentation (all in English and stamped by some public institution certifying that they correspond to the original counterpart in your local language) at hand. You will have to upload them in PDF format and later submit them by s-mail (i.e., paper mail, postal mail, land mail, …) to the admission contact address by the date specified in the BDMA website if your application is eventually accepted (failing to submit the hard-copies by s-mail on time implies exclusion; check the application manual for further details):

  • Cover letter (stating your interest, your possibilities in the Master, and anything you consider relevant to know for the Selection Committee)
  • Passport
  • Photo (passport size)
  • CV
  • Bachelor certificate (at least 180 ECTS)
  • Official transcripts
  • Proof of English proficiency
  • Proof of contracts

Second Step: To file an application for the EM Master programme:

  1. Register an account
  2. Log in with your username and password
  3. Complete the application forms and upload the necessary attachments (you will be notified by email of the progress of your application)
  4. Make sure that your referees uploaded their recommendation letter before the application deadline
  5. Submit your application (notice that you cannot apply to more than three Erasmus Mundus programmes) before the application deadline
  6. Make sure you’ve received an acknowledgment by email confirming your application

Schorlarship

The Erasmus+ programme provides EU-funded scholarships to students who have been selected by the consortia. Two categories of scholarships are available.

The scholarship for students from Programme Countries comprises:

  • Participation costs: up to 4,500 euros per year
  • Travel costs: 1,000 euros per year
  • Contribution to subsistence costs: 1,000 euros per month for the entire duration of the BDMA programme (24 months maximum).

The scholarship for students from Partner Countries comprises:

  • Participation costs: up to 9,000 euros per year
  • Travel costs: 2.000 or 3,000 euros per year depending on whether the student is resident of a country whose location is, respectively, less than or more than 4.000 km away from Brussels, the coordinating institution of the BDMA programme. The place of residence will be verified on the basis of the provision of documents such as a residence certificate issued in accordance with the candidate’s municipality normal registration rules or a certificate from the candidate’s place of work, study or training issued by the employer or institution in question. Both documents must have been issued within 12 months before the BDMA official deadline for student selection.
  • Installation costs: 1,000 euros.
  • Contribution to subsistence costs: 1,000 euros per month for the entire duration of the BDMA programme (24 months maximum).

The participation costs covering the tuition fees are directly taken by the consortium. The remainder of the scholarship will be allocated to selected students as follows:

  • Two yearly instalments, each one covering half of the travel costs.
  • One installment of 1,000 euros covering the installation costs for students from Partner Countries.
  • 24 monthly instalments of 1,000 euros for the subsistence costs.

The scholarship will be paid by bank transfer.

All scholarship holders will receive an insurance meeting the minimum insurance requirements of the Erasmus+ programme for JMDs.

For tuition fees:

  • Partner Countries students: €9000 / year;
  • Programme Countries students: €4500 / year.

Erasmus Mundus Master in Big Data Management and Analytics (BDMA)

Barcelona School of Informatics (FIB)

Since its inception 20 years ago, business intelligence (BI) has become a huge industrial area and a significant driver of the economy. It covers many fields of science and technology (data warehousing and mining, content analysis, business process management, etc.) and requires knowledge of information systems, the Web, decision making, software engineering, innovation and entrepreneurship. The Erasmus Mundus master’s degree in Big Data Management and Analytics (BDMA) (master’s degree website), formerly the master’s degree in Information Technologies for Business Intelligence (IT4BI), coordinated by the Université Libre de Bruxelles (ULB) and with the UPC as a participant, provides students with the comprehensive training required for them to understand, learn and acquire BI skills and develop decision-making strategies for business.

Background & Objectives

The First Report of the EU Data Market Study estimates that the number of data users will reach more than 1.3 million in 2020 while the overall data market will likely reach € 111 billion under high growth conditions. The last decade was marked by the digitalisation of virtually all aspects of our daily lives. Today, public and private organisations in all sectors face an avalanche of digital data on a daily basis. While at first glance this appears to be favourable for our knowledge-based society, in many ways it is a burden. Data is neither information nor knowledge. Instead, data is of great value once it has been refined and analysed, in order to address well-formulated questions, concerning problems of interest. It is only then through data-driven innovation that the economic and social benefit can be fully realised. The Erasmus Mundus Joint Master Degree Programme in Big Data Management and Analytics (BDMA) is a unique programme that fully covers all of the data management and analytics aspects of Big Data (BD), built on top of Business Intelligence (BI) foundations, and complemented with horizontal skills. It has been jointly designed and adheres to international studies, being structured to cover all the skills BI and BD specialists require.

Big data management: 5 things you need to know | SAS

Presentation & Structure

The programme favours the integration of students into a network of specialists and researchers in BI and BD. The curriculum is jointly delivered by Université Libre de Bruxelles (ULB) in Belgium, Universitat Politècnica de Catalunya (UPC) in Spain, Technische Universiteit Eindhoven (TU/e) in the Netherlands, CentraleSupélec (CS) in France and Università degli Studi di Padova (UniPD) in Italy. Scholars from academic partners around the world and partners from leading industries in BI, private R&D companies, service companies, public research institutes, and public authorities will contribute to the programme by training students, providing computers, software, course material, job placement or internship perspectives, as well as financial support.

This consortium will prepare the graduates not only to answer today s professional challenges by a strong connection with the needs coming from the industry, but also to pursue their studies into doctorate programs, through strong connections with the researchers and innovators views.

The master is divided in four semesters of 30 ECTS each. In the first year, students acquire fundamental knowledge in BI and BD. The first semester at ULB homogenises the students’ background by introducing them to core BI competences: traditional data management, business process management, and data analytics. The second semester at UPC covers BD fundamentals: distributed management to deal with Volume, semantic management to deal with Variety, and distributed stream-based management to deal with Velocity. In this first year, students also acquire ethics awareness and business and entrepreneurship skills to deal with Value, as well as horizontal skills such as critical thinking, language, writing, and presentation skills. In the second year, students specialise in how to couple such techniques with a business goal. The specialisation in “Business Process Analytics” at TU/e offers the bridge between data mining and business processes modelling and analysis, i.e., dealing mainly with Variability and Value. The specialisation in “Decision Support and Analytics” at CS concentrates on models, algorithms, and technologies related to decision-support systems and massive data analytics, dealing mainly with Value and Veracity. Ethics and innovation courses are incorporated in the three specialisation programmes.The specialization on “Statistics & Deep Learning for Data Analytics” at UniPD aims to provide students with advanced Data-Science methods and strengthen their background in Statistics and Deep Learning. In this respect, UniPd will provide a first mandatory course on Statistical Inference, and a second mandatory course on Deep Learning, the latter with an emphasis on the analysis of human data (see the course description below). Furthermore, students with a strong interest in statistical methods can opt to further enlarge their background of Data-Science methods by also taking a course on Stochastic Model. In the last semester, devoted to the master’s thesis, students put into practice the obtained technical skills, aligned with a business and entrepreneurship vision, and with a strong background in ethics. Finally, during the whole programme, students are introduced to local culture aspects.

The tuition language is English. The programme targets students with a Bachelor of Science (or a level equivalent to 180 ETCS) with major in Computer Science, as well as an English proficiency corresponding to level B2 of the Common European Framework of Reference for Languages.

Big Data Management the Focus of Pre-RoboBusiness Workshop

General details

Duration and start dateTwo academic years, 120 ECTS credits. Starting SeptemberTimetable and deliveryMornings. Face-to-faceLanguage of instructionEnglish

Information on language use in the classroom and students’ language rights.

Location Barcelona School of Informatics (FIB)

Official degree Recorded in the Ministry of Education’s degree register

Admission

General requirements Academic requirements for admission to master’s degrees

Places 30

Pre-enrolment To enrol for an interuniversity master’s degree coordinated by a university other than the UPC, you must enrol through the coordinating university: Université Libre de Bruxelles (ULB)

Professional opportunities

Business intelligence (BI) has become an important industrial domain that encompasses many scientific and technological fields, including data warehouses, data mining, visual and content analytics and business process management. It requires competencies in information systems, Web science, decision science, software engineering, innovation and entrepreneurship. This master’s degree is designed to provide understanding, knowledge and skills in this broad range of fields. Its main aim is to train computer scientists who understand and help to develop the decision-making strategies of modern businesses.

The posts that graduates of this master’s degree often occupy and for which they are in most demand are the following:

  • Data analyst/data scientist (typically involving user modelling and personalisation)
  • CRM specialist
  • Big data decision-making systems engineer
  • Specialist in data modelling/hybrid dataflow between different platforms
  • Administrator/designer of distributed systems for cloud computing
     

Competencies

Generic competencies

Generic competencies are the skills that graduates acquire regardless of the specific course or field of study. The generic competencies established by the UPC are capacity for innovation and entrepreneurship, sustainability and social commitment, knowledge of a foreign language (preferably English), teamwork and proper use of information resources.

MSc in Big Data Management and Analytics

Why Study Big Data at Griffith College?

Designed specifically to address a growing need in the industry, the MSc in Big Data Management and Analytics at Griffith College is a 1 year programme which aims to build upon students’ knowledge of computing science and create big data specialists. Delivered on a full-time basis, as a graduate of this course, you will:

  • Obtain specialist knowledge and skills essential for a career in Big Data Management and Analytics
  • Understand the advantages of concurrent & parallel programming systems along with developing the skill to write concurrent and parallel programmes to solve real-world problems
  • Gain a practical understanding of appropriate design and implementation strategies used in the development of Big Data solutions
  • Develop the skills to implement an end-to-end Big Data storage system using the most current technology available
  • Build on the foundations of analysis theories to explore the range of contemporary practices surrounding the ever-developing field of big data analysis
  • Fantastic job prospects with a 100% employment record from a sample of 35 graduates
How Data Management is Crucial for Small Businesses

Course Highlights

  • Emerging discipline with huge job opportunities
  • Develop highly sought after skills
  • Fully aligned with industry needs
  • Access to innovative tools and technologies
  • A dedicated experienced lecturing team

Intake Dates

The next intake for this course will be:

  • February 2022*
  • September 2022*

*subject to sufficient numbers

What our students say

I found the course to be both challenging and rewarding as it gave me the opportunity to acquire a valuable accreditation whilst enhancing my computer expertise. The modules on the course are extremely interesting and beneficial, knowledge of which will prove useful in the IT marketplace.

Related Courses

  • MSc in Network and Information Security
  • MSc in Cloud Computing
  • MSc in Interactive Digital Media

big data management

Big data management is the organization, administration and governance of large volumes of both structured and unstructured data.

The goal of big data management is to ensure a high level of data quality and accessibility for business intelligence and big data analytics applications. Corporations, government agencies and other organizations employ big data management strategies to help them contend with fast-growing pools of data, typically involving many terabytes or even petabytes stored in a variety of file formats. Effective big data management particularly helps companies locate valuable information in large sets of unstructured and semistructured data from various sources, including call detail records, system logs, sensors, images and social media sites.

Most big data environments go beyond relational databases and traditional data warehouse platforms to incorporate technologies that are suited to processing and storing nontransactional forms of data. The increasing focus on collecting and analyzing big data is shaping new data platforms and architectures that often combine data warehouses with big data systems.

As part of the big data management process, companies must decide what data must be kept for compliance reasons, what data can be disposed of and what data should be analyzed in order to improve current business processes or provide a competitive advantage. This process requires careful data classification so that, ultimately, smaller sets of data can be analyzed quickly and productively.

Big data management and analytics | Grad cert | Missouri S&T | Missouri  Online

Top challenges in managing big data

Big data is usually complex — in addition to its volume and variety, it often includes streaming data and other types of data that are created and updated at a high velocity. As a result, processing and managing big data are complicated tasks. For data management teams, the biggest challenges faced on big data deployments include the following:

  • Dealing with the large amounts of data. Sets of big data don’t necessarily need to be large, but they commonly are — and in many cases, they’re massive. Also, data frequently is spread across different processing platforms and storage repositories. The scale of the data volumes that typically are involved makes it difficult to manage all of the data effectively.
  • Fixing data quality problems. Big data environments often include raw data that hasn’t been cleansed yet, including data from different source systems that may not be entered or formatted consistently. That makes data quality management a challenge for teams, which need to identify and fix data errors, variances, duplicate entries and other issues in data sets.
  • Integrating different data sets. Similar to the challenge of managing data quality, the data integration process with big data is complicated by the need to pull together data from various sources for analytics uses. In addition, traditional extract, transform and load (ETL) integration approaches often aren’t suited to big data because of its variety and processing velocity.
  • Preparing data for analytics applications. Data preparation for advanced analytics can be a lengthy process, and big data makes it even more challenging. Raw data sets often must be consolidated, filtered, organized and validated on the fly for individual applications. The distributed nature of big data systems also complicates efforts to gather the required data.
  • Ensuring that big data systems can scale as needed. Big data workloads require a lot of processing and storage resources. That can strain the performance of big data systems if they aren’t designed to deliver the required processing capacity. It’s a balancing act, though: Deploying systems with excess capacity adds unnecessary costs for businesses.
  • Governing sets of big data. Without sufficient data governance oversight, data from different sources might not be harmonized, and sensitive data might be collected and used improperly. But governing big data environments creates new challenges because of the unstructured and semistructured data they contain, plus the frequent inclusion of external data sources.

Best practices for big data management

Done well, big data management sets the stage for successful analytics initiatives that can help drive better business decision-making and strategic planning in organizations. Here’s a list of best practices to adopt in big data programs to put them on the right track:

  • Develop a detailed strategy and roadmap upfront. Organizations should start by creating a strategic plan for big data that defines business goals, assesses data requirements and maps out applications and system deployments. The strategy should also include a review of data management processes and skills to identify any gaps that need to be filled.
  • Design and implement a solid architecture. A well-designed big data architecture includes various layers of systems and tools that support data management activities, from ingestion, processing and storage to data quality, integration and preparation work.
  • Stay focused on business goals and needs. Data management teams must work closely with data scientists, other analysts and business users to make sure that big data environments meet business needs for information to enable more data-driven decisions.
  • Eliminate disconnected data silos. To avoid data integration problems and ensure that relevant data is accessible for analysis, a big data architecture should be designed without siloed systems. It also offers the opportunity to connect existing data silos as source systems so they can be combined with other data sets.
  • Be flexible on managing data. Data scientists commonly need to customize how they manipulate data for machine learning, predictive analytics and other types of big data analytics applications — and in some cases, they want to analyze full sets of raw data. That makes an iterative approach to data management and preparation essential.
  • Put strong access and governance controls in place. While governing big data is a challenge, it’s a must, along with robust user access controls and data security protections. That’s partly to help organizations comply with data privacy laws regulating the collection and use of personal data, but well-governed data can also lead to higher-quality and more accurate analytics.

Big data management tools and capabilities

There’s a wide variety of platforms and tools for managing big data, with both open source and commercial versions available for many of them. The list of big data technologies that can be deployed, often in combination with one another, includes distributed processing frameworks Hadoop and Spark; stream processing engines; cloud object storage services; cluster management software; NoSQL databases; data lake and data warehouse platforms; and SQL query engines.

To enable easier scalability and more flexibility on deployments, big data workloads increasingly are being run in the cloud, where businesses can set up their own systems or use managed services offerings. Prominent big data management vendors include cloud platform market leaders AWS, Google and Microsoft, plus Cloudera, Databricks and others that focus mainly on big data applications.

Mainstream data management tools are also key components for managing big data. That includes data integration software supporting multiple integration techniques, such as traditional ETL processes; an alternative ELT approach that loads data as is into big data systems so it can be transformed later as needed; and real-time integration methods, such as change data capture. Data quality tools that automate data profiling, cleansing and validation are commonly used, too.

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