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.

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.

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    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

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    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

    All our study programmes include the following benefits

    • Teaching and study material
    • Marking of your end-of-module exams
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    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.

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    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

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