Course description

You will learn the following interesting topics in this course

  1. Big Data Technology Fields


  2. Types of Big Data Technologies


  3. Big Data Technologies in Data Storage


  4. Big Data Technologies in Data Analytics


  5. Big Data Technologies in Data Mining


  6. Big Data Technologies in Data Visualization


You will learn the following interesting topics in this course.

  1. Big Data Technology Fields


  2. Types of Big Data Technologies


  3. Big Data Technologies in Data Storage


  4. Big Data Technologies in Data Analytics


  5. Big Data Technologies in Data Mining


  6. Big Data Technologies in Data Visualization


You will learn the following interesting topics in this course.

  1. Big Data Technology Fields


  2. Types of Big Data Technologies


  3. Big Data Technologies in Data Storage


  4. Big Data Technologies in Data Analytics


  5. Big Data Technologies in Data Mining


  6. Big Data Technologies in Data Visualization


You will learn the following interesting topics in this course.

  1. Big Data Technology Fields


  2. Types of Big Data Technologies


  3. Big Data Technologies in Data Storage


  4. Big Data Technologies in Data Analytics


  5. Big Data Technologies in Data Mining


  6. Big Data Technologies in Data Visualization

You will learn the following interesting topics in this course.

  1. Big Data Technology Fields


  2. Types of Big Data Technologies


  3. Big Data Technologies in Data Storage


  4. Big Data Technologies in Data Analytics


  5. Big Data Technologies in Data Mining


  6. Big Data Technologies in Data Visualization

You will learn the following interesting topics in this course.

  1. Big Data Technology Fields


  2. Types of Big Data Technologies


  3. Big Data Technologies in Data Storage


  4. Big Data Technologies in Data Analytics


  5. Big Data Technologies in Data Mining


  6. Big Data Technologies in Data Visualization

What will i learn?

  • Various Big Data Technology Fields
  • Big Data Technologies in Data Storage
  • Big Data Technologies in Data Visualization
  • Big Data Technologies in Data Mining

Requirements

  • 1. Educational Background High School Diploma or Equivalent: Most courses require at least a high school diploma or equivalent. Bachelor's Degree (Preferred but not always required): Some institutions may prefer candidates who have a bachelor's degree in a related field such as Computer Science, Engineering, Information Technology, or Mathematics, but this is not always a strict requirement.
  • 2. Prerequisite Knowledge and Skills While prior experience is not always mandatory, it can help if you are familiar with some basic concepts in data science, analytics, and programming: Basic programming knowledge: Familiarity with programming languages like Python or R is typically recommended. These languages are widely used in data analytics, mining, and visualization. Basic understanding of databases: Some knowledge of databases (e.g., SQL or NoSQL) will be useful, as the course covers topics like data storage and data retrieval. Basic statistics or math: Understanding fundamental concepts in statistics and mathematics is important for data analysis and mining. Some courses may recommend familiarity with concepts such as mean, median, regression, probability, and statistical tests.
  • 3. Software and Tools Access to a computer and internet: Since the course may be delivered online, you will need a computer with internet access. Software tools: Many of the tools covered in the course, such as Python, Apache Spark, and Hadoop, are open-source and free to use. For data visualization, tools like Tableau or Power BI may offer free versions for students or learners. Cloud computing access (optional): Some courses may provide access to cloud environments (e.g., AWS, Google Cloud Platform, Azure) for processing large datasets. Make sure you have the technical ability to access or set up cloud environments if required.
  • 4. Language Proficiency English proficiency: If the course is delivered in English, a basic understanding of English is necessary, especially for reading materials, understanding lectures, and completing assignments. Some institutions might require proof of English proficiency for non-native speakers, such as through TOEFL or IELTS scores.

Frequently asked question

This course provides in-depth knowledge of key big data technologies and their applications, covering: Data Analytics: Techniques for extracting insights from data. Data Storage: Managing and storing large datasets efficiently using distributed systems. Data Mining: Techniques for discovering patterns and trends in large datasets. Data Visualization: Using visualization tools to present complex data in an accessible format. The course covers the full spectrum of big data technologies and methodologies, from raw data collection to actionable insights.

This course is ideal for: >>Aspiring Data Scientists and Data Analysts who want to gain hands-on experience with the latest tools and techniques in big data analytics. >>IT professionals looking to expand their knowledge of big data technologies like Hadoop, Spark, NoSQL, and data visualization tools. >>Business Analysts or professionals who want to enhance their data-driven decision-making skills. >>Students or individuals interested in entering the field of big data, data mining, or data visualization. >>Tech professionals who want to specialize in handling and analyzing large datasets for industries like healthcare, finance, e-commerce, or marketing.

>>Basic understanding of data: Familiarity with data concepts and analysis is recommended. If you're new to data science, you may want to take a beginner course in data analysis or statistics. >>Basic programming knowledge: Knowledge of programming languages like Python or R is helpful, especially for data mining and analytics tasks. >>Familiarity with databases: Understanding basic database concepts (SQL, NoSQL) can be useful, as the course will cover topics related to data storage and querying large datasets. >>No advanced experience required: While prior knowledge can help, the course is designed to be accessible to those with fundamental tech and data analysis skills.

By the end of the course, you will: >>Understand big data concepts and technologies, including the infrastructure and tools required for handling large-scale datasets. >>Learn about data storage solutions (e.g., relational databases, NoSQL databases, Hadoop Distributed File System) and how to manage and store large amounts of data efficiently. >>Master data mining techniques like clustering, classification, regression, and association rule mining to uncover hidden patterns in large datasets. >>Gain proficiency with popular big data processing frameworks like Hadoop, Apache Spark, and MapReduce. >>Learn how to use data visualization tools (e.g., Tableau, Power BI, or D3.js) to present complex data insights in an interactive and understandable way. >>Explore the integration of data analytics and machine learning for predictive analysis in real-world applications.

₹499

₹1399

Lectures

7

Quizzes

1

Skill level

Beginner

Expiry period

Lifetime

Certificate

Yes

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