Well, let's start this course with an introduction to what big data is. What do we mean when we talk about large amounts of data and what are the ways to manage them correctly? Digitalization in today's world generates vast amounts of data across nearly every field.

Topics in this video:

• Introduction to Big Data

• Characteristics of Big Data

Topics in this video:

• Characteristics of Big Data

• Proper Big Data Management

• Value of Big Data

• Veracity of Big Data
  
  

In this video, I talk about:

• Big Data value

• Big Data veracity

and the importance for the companies to ensure data accuracy, validity, and integrity.

In this video, we analyze the various issues that can arise when using large amounts of data. The process of collecting and storing data is crucial for organizations to manage and use large, diverse datasets effectively.

Topics included:

• Data Collection and Storage Challenges

• Data Quality and Integrity Challenges

• Privacy and Security Concerns

In this video we will see what big data analytics is and what are the key elements of a big data analytics system. We'll see that at its core, big data analytics is the process of identifying trends, patterns, and correlations within massive datasets to enable data-driven decision-making.

Topics of this video:

• Key Elements of Big Data Analytics

• How Does Big Data Analytics Work

• Benefits and Challenges of Big Data Analytics

Don't forget to download the attached pdf on how to install Hadoop on Windows, Linux and Mac and how to install MapReduce on Linux or MacOS

As organizations handle increasing amounts of data, big data analytics platforms have become essential for turning raw information into actionable insights. In this video, we'll talk about the key components of big data analytics platforms.

Covered topics:

• Big Data Analytics Platform

• Key Components of Big Data Analytics Platforms

• Main Characteristics of Big Data Analytics Platforms

In this video, we’ll see a real-world example of using Big Data Analytics for enhancing fraud detection capabilities. The company is SecurePay Solutions and its goal in this case is to enhance fraud detection capabilities to minimize financial losses and protect customer data.

In this second case history, we’ll see another real-world example of using Big Data Analytics. In this case the goal of the company is optimizing investment strategies and portfolio management through enhanced data-driven insights. The Company is AlphaWealth Advisors, they create financial strategies that are tailored to individual needs and goals.

Hi, in this video, we talk about a useful open-source framework called Hadoop. It is used for storing and processing large datasets distributed across clusters of computers. You'll learn some key features and components of Hadoop like Distributed File System (HDFS).

Topic in this video:

• Big Data Processing with Hadoop

• Distributed File System

• MapReduce

In this video, we will see some examples of Adoption of Big Data Analytics in Finance. In particular, we will examine companies in the financial sector. We start with FinTrust Bank. The objective of the company is to improve credit scoring accuracy and manage lending risk effectively.

In banking and insurance industries, customer segmentation plays a vital role in delivering personalized services and identifying cross-selling and upselling opportunities. Properly categorizing clients according to demographics, behavior, preferences, and profitability enables institutions to optimize resources and maximize returns. There are a lot of methods used for customer segmentation.