Data Science and Analytics In
Big Data Essentials

This Big Data Essentials course is designed to introduce students to the fundamental concepts and technologies of big data, including data processing, storage, and analytics. Students will learn about the challenges of big data, the Hadoop ecosystem, and popular big data tools such as Apache Spark, Hive, and Pig.

REQUEST FREE INFORMATION

Overview

Big Data Essentials

Course Learning Outcomes (CLOs) and SLOs

Course Learning Outcomes (CLOs) typically include:

  • Understanding Big Data: Gain an understanding of what constitutes Big Data, including its characteristics, challenges, and opportunities.
  • Big Data Technologies: Familiarize with the key technologies and frameworks used in Big Data ecosystems, such as Hadoop, Spark, and NoSQL databases.
  • Data Management: Learn strategies and techniques for storing, managing, and processing large volumes of data efficiently.
  • Data Analysis and Visualization: Develop skills in analyzing and visualizing Big Data to extract meaningful insights and support decision-making.
    Student Learning Outcomes (SLOs) are specific goals for students, such as:
  • Technology Proficiency: Demonstrate proficiency in using Big Data technologies and tools for data storage, processing, and analysis.
  • Problem Solving: Apply Big Data techniques to solve real-world problems and challenges related to data scalability, processing speed, and complexity.
  • Data Security and Ethics: Understand the importance of data security and privacy issues in Big Data applications, and adhere to ethical guidelines.
  • Project Implementation: Successfully implement a Big Data project, from data acquisition and storage to analysis and visualization, showcasing practical skills and knowledge.

    • 100% International

    Study at your own pace from anywhere in the world

    Recommended by 96% of our graduates

    According to our latest alumni survey

    50,000+ students

    enrolled in Germany’s largest university

    Study contents

    Contents

    Big Data Essentials Short Course Outline


    1. Introduction to Big Data

  • Definition and characteristics of big data
  • Importance and applications across various industries

    2. Big Data Technologies and Tools

  • Overview of Hadoop ecosystem (HDFS, MapReduce, YARN)
  • Introduction to Apache Spark and its advantages
  • Comparison with traditional data processing methods

    3. Data Storage and Management

  • Distributed file systems (HDFS, Amazon S3, Google Cloud Storage)
  • NoSQL databases (MongoDB, Cassandra) vs. traditional SQL databases
  • Data warehousing concepts and technologies

    4. Data Processing and Analysis

  • Batch vs. real-time data processing
  • Introduction to data pipelines and ETL (Extract, Transform, Load) processes
  • Machine learning and advanced analytics on big data

    5. Data Governance and Security

  • Challenges in big data governance
  • Privacy concerns and regulations (GDPR, CCPA)
  • Security best practices for big data environments

    6. Case Studies and Applications

  • Real-world examples of big data implementations
  • Use cases across industries (e.g., healthcare, finance, e-commerce)
  • Lessons learned and success stories

    7. Future Trends in Big Data

  • Emerging technologies (e.g., blockchain, edge computing)
  • The role of AI and machine learning in big data analytics
  • Predictions and outlook for the future of big data

    8. Hands-on Labs and Projects

  • Practical exercises using Hadoop and Spark
  • Building data pipelines and performing data analysis
  • Project-based learning to reinforce concepts

    9. Conclusion and Certification

  • Recap of key concepts and takeaways
  • Assessment and certification for completion
  • Resources for further learning and development

    Target Audience

  • Professionals looking to understand the fundamentals of big data
  • Data analysts, engineers, and scientists interested in expanding their skills
  • Managers and decision-makers seeking insights into leveraging big data for business growth
    This outline provides a structured approach to cover essential aspects of big data, ensuring participants gain both theoretical knowledge and practical skills necessary to work effectively in a big data environment.

    • Admission

    Admission Criteria

    Education: Bachelor's degree in a related field such as Computer Science, Information Technology, Data Science, or Mathematics. Some programs may accept students with a non-technical background, but they may require additional coursework or certifications. Work Experience: Typically, 1-3 years of experience in a related field such as data analysis, data science, or IT. Prior experience with big data technologies such as Hadoop, Spark, or NoSQL databases is desirable. Skills: Programming skills in languages such as Python, Java, or R. Familiarity with data analytics and visualization tools such as Tableau, Power BI, or D3.js. Understanding of data structures, algorithms, and statistical concepts. Knowledge of big data technologies such as Hadoop, Spark, NoSQL databases, and cloud computing platforms. Certifications: Some programs may require or recommend certifications such as: Certified Data Scientist (CDS) or Certified Analytics Professional (CAP) from the International Institute for Analytics (IIA). Certified Big Data Developer (CBDD) or Certified Big Data Engineer (CBDE) from the Data Science Council of America (DSCA). Admission Test Scores: GRE or GMAT scores may be required for some programs. TOEFL or IELTS scores may be required for international students. Recommendations: Typically, 1-2 letters of recommendation from academic or professional references. Personal Statement: A written statement outlining your motivation for pursuing a career in big data and your goals for the program. Portfolio: Some programs may require a portfolio of your previous work or projects that demonstrate your skills in data analysis and visualization. Interviews: Some programs may include an interview as part of the admission process to assess your communication and problem-solving skills.

    Careers

    Find your Career now

    Data Scientist: Responsible for extracting insights and knowledge from large datasets using various techniques such as machine learning, statistical modeling, and data visualization. Business Intelligence Developer: Designs and develops business intelligence solutions to analyze and visualize data, providing insights to help organizations make informed business decisions. Data Engineer: Develops and maintains the infrastructure and systems that store, process, and retrieve large datasets, ensuring data quality, security, and scalability. Data Analyst: Analyzes and interprets data to identify trends, patterns, and correlations, providing insights to help organizations make informed business decisions. Data Architect: Designs and implements the overall data architecture, ensuring data consistency, scalability, and security across an organization. Machine Learning Engineer: Develops and deploys machine learning models to solve complex problems, such as predictive modeling, natural language processing, and computer vision. Data Quality Specialist: Ensures the quality of data by identifying errors, inconsistencies, and inaccuracies, and developing processes to correct them. Business Analyst: Works with stakeholders to understand business needs and develops data-driven solutions to solve problems and improve business operations. Predictive Modeling Specialist: Develops predictive models using statistical and machine learning techniques to forecast future outcomes and optimize business decisions. Data Visualization Specialist: Creates interactive dashboards, reports, and visualizations to help organizations understand complex data insights. Cloud Data Engineer: Designs and develops cloud-based data architectures, including storage, processing, and analytics solutions. Data Governance Specialist: Develops policies, procedures, and standards for data management, ensuring compliance with regulatory requirements. Machine Learning Researcher: Conducts research in machine learning algorithms and applications to develop new solutions for various industries. Big Data Developer: Develops software applications that can handle large datasets using big data technologies such as Hadoop, Spark, and NoSQL databases. Data Security Specialist: Ensures the security of sensitive data by implementing encryption, access controls, and monitoring systems.

    Student reviews

    Coming Soon.

    Tuition fees

    Big Data Essentials (Duration 4 Weeks)

    Managers, decision-makers, data technicians, and data enthusiasts alike benefit from knowing how various systems and technologies are used in big data projects.

    350 $

    200 $ / Total Cost
    Inquiry Add to Cart

    All our study programmes include the following benefits

    • Teaching and study material
    • Marking of your end-of-module exams
    • Monthly live and recorded tutorials
    • Use of the online campus
    • Individual study coaching
    • Online exams
    • Career coaching
    • Learn English for free

    Our global recognition

    IU is recognised by WES Canada and U.S., which means your degree can be converted to points in the local system for purposes of immigration, work, or studies.

    As the first EU institution in UNESCO's Global Education Coalition, IU is committed to ensuring accessible quality education to students in crisis worldwide through free online micro-credentials.

    Our company partners

    For over 20 years, IU has established partnerships with leading global companies. This offers you the chance to gain firsthand experience through internships and projects and allow us to adapt our learning content to the ever-evolving needs of the labour market. You'll benefit from an education designed to bridge the gap between theory and real-world practice, ensuring your readiness for your future career.

    Recognition

    Recognition of previous achievements

    Have you already completed a training course, studied at a university or gained work experience? Have you completed a course or a learning path through EPIBM LinkedIn Learning, and earned a certificate? Then you have the opportunity to get your previous achievements recognised, and complete your studies at EPIBM sooner.

    Save time:

    Skip individual modules or whole semesters!
    Even before you apply for a study programme, we’ll gladly check whether we can take your previous achievements into account: 100% online, no strings attached. Simply fill in our recognition application form, which you can find under the content section of each study programme's webpage, and upload it via our upload section. You can also e-mail it to us, or send it via post.
    Send an email to [email protected] to find out which previous achievements you can get recognised. You can get your previous achievements recognised during your studies. Recognition files

    F.A.Q

    Frequently Asked Questions

    Feugiat pretium nibh ipsum consequat. Tempus iaculis urna id volutpat lacus laoreet non curabitur gravida. Venenatis lectus magna fringilla urna porttitor rhoncus dolor purus non.

    Dolor sit amet consectetur adipiscing elit pellentesque habitant morbi. Id interdum velit laoreet id donec ultrices. Fringilla phasellus faucibus scelerisque eleifend donec pretium. Est pellentesque elit ullamcorper dignissim. Mauris ultrices eros in cursus turpis massa tincidunt dui.

    Eleifend mi in nulla posuere sollicitudin aliquam ultrices sagittis orci. Faucibus pulvinar elementum integer enim. Sem nulla pharetra diam sit amet nisl suscipit. Rutrum tellus pellentesque eu tincidunt. Lectus urna duis convallis convallis tellus. Urna molestie at elementum eu facilisis sed odio morbi quis

    Dolor sit amet consectetur adipiscing elit pellentesque habitant morbi. Id interdum velit laoreet id donec ultrices. Fringilla phasellus faucibus scelerisque eleifend donec pretium. Est pellentesque elit ullamcorper dignissim. Mauris ultrices eros in cursus turpis massa tincidunt dui.

    Molestie a iaculis at erat pellentesque adipiscing commodo. Dignissim suspendisse in est ante in. Nunc vel risus commodo viverra maecenas accumsan. Sit amet nisl suscipit adipiscing bibendum est. Purus gravida quis blandit turpis cursus in

    Laoreet sit amet cursus sit amet dictum sit amet justo. Mauris vitae ultricies leo integer malesuada nunc vel. Tincidunt eget nullam non nisi est sit amet. Turpis nunc eget lorem dolor sed. Ut venenatis tellus in metus vulputate eu scelerisque. Pellentesque diam volutpat commodo sed egestas egestas fringilla phasellus faucibus. Nibh tellus molestie nunc non blandit massa enim nec.

    You might also be interested in these study programmes

    ✓

    We value and respect your privacy. By continuing, you agree to EPIBM data protection policy.

    Accredited and certified