Navigating the Data Jungle in the Age of Algorithms -Habibur Rahman Nirob

What is data? There are a huge number of people who have no idea about that. A specific purpose collects, processes, and stores a set of non-objective instructions, known as data. Usually, a variable stores these purposeless instructions as numbers, text, images, audio, video, or other digital forms. We refer to any value of a variable as data. Data is usually messy. Sometimes messy or very messy.

First, let’s understand the difference between data science and big data. Both fields deal with data and require specialized skills. Both aim at extracting insights and knowledge from data to make decisions. Both have a wide range of applications. If implemented correctly, both can significantly improve stakeholders’ earnings and operational efficiency.

Data science is now a discipline of study. Big data is a specialized technique for collecting, maintaining, and processing large amounts of data. Data science deals with the collection, processing, analysis, and use of data in various activities. It’s more of a conceptual thing. Big data deals with extracting important and valuable information from large amounts of data. Data science is a field of study such as computer science, applied statistics, statistics, or applied mathematics. Big data, on the other hand, is a technique for tracking and discovering trends in complex data sets.

The goal of data science is to develop data-driven strategies or products in commercial terms. The aim of big data is to make data more meaningful and usable by extracting only the most important information from the existing traditional aspects. Programs mainly used in data science include SAS, R, Python, Julia, etc., but tools used in big data are Hadoop, Spark, Flink, etc.

Data science is a superset of big data, as it includes many techniques, including data scraping, cleaning, visualization, statistics, etc. Big data is a subset of data science, as are data mining activities in a data science pipeline. Data science generates work based on scientific principles. On the other hand, businesses primarily use big data for customer satisfaction and business purposes.
Now let’s know: What is the relationship between machine learning and data science? Many people do not have a clear idea about this. We can assert that a significant number of individuals possess no knowledge whatsoever about this field. Machine learning is a subset of data science that focuses on developing algorithms and models for machines (computers) to learn from data and make predictions or decisions. On the other hand, data science encompasses a broad range of activities that invariably involve extracting insights from data using various techniques, including machine learning. Machine learning and data science are closely related fields, but they have distinct differences in terms of their objectives, methods, and focus areas.

The primary goal of machine learning is to develop algorithms and models that enable machines to learn from data, make predictions or decisions, and improve their performance over time. It focuses on building intelligent systems capable of automatically acquiring knowledge and adapting to new information. Data science, on the other hand, aims to extract insights and knowledge from data using various techniques, including statistical analysis, data visualization, and data mining. Its main objective is to derive meaningful and actionable insights from data to solve complex problems, make informed decisions, and drive business value.

Machine learning employs algorithms and statistical models to automatically learn patterns and relationships from data. It involves training models on labelled datasets and incrementally reinforcing techniques for making predictions or taking actions based on new, unseen data. Machine learning algorithms require significant amounts of labelled data for training and rely heavily on pattern recognition and statistical analysis.

Data science encompasses a wide range of techniques and methods for analyzing data. It involves data collection, cleaning, purification, and transformation; exploratory data analysis; the application of statistical methods; and the creation of visualizations. Data scientists also use machine learning algorithms as part of their toolkit, but their focus is not just on building models. They explore data from different angles, identify trends, correlations, and anomalies, and gain insights to solve specific problems or answer specific questions.

Machine learning primarily focuses on the development and implementation of algorithms and models that help machines learn from data and make predictions or decisions. It easily performs tasks like classification, regression, clustering, recommendation systems, and natural language processing. We emphasize the creation of intelligent systems capable of performing specific tasks without the need for subsequent explicit programming.

Data science encompasses a wide range of activities. In a nutshell, it involves understanding the business problem, identifying relevant data sources, collecting and cleaning data, exploring and visualizing data, performing statistical analysis, and building predictive models or algorithms. Data scientists work across different domains and often work with domain experts to gain insights and solve complex problems using data.

Deep learning is a subfield of machine learning that focuses on the development and application of artificial neural networks, known as deep neural networks. There is a real history of developing deep neural networks inspired by the structure and function of the human brain, particularly by exploiting the interconnections of neurons. Multiple layers in deep learning design neural networks, enabling them to learn and represent complex patterns and relationships in data.

These layers, called hidden layers, enable the network to extract categorical features from the input data. The deeper the network, the more abstract and high-level features it can learn. Large amounts of labelled data typically train deep learning algorithms. During the training process, the network iteratively adjusts its internal parameters to minimize the difference between its predicted output and the actual output. This process, known as backpropagation, involves pushing the error backwards through the network and updating the connection weights between neurons.

One of the key strengths of deep learning is its ability to automatically learn representations from raw data without the need for manual feature engineering. It has achieved remarkable success in various domains, including computer vision, natural language processing, and text mining. Image classification, object detection, machine translation, sentiment analysis, and other fields apply deep learning models like Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). These models can exhibit state-of-the-art performance and even exceed human-level accuracy in certain domains.

Overall, deep learning has revolutionized the field of artificial intelligence, and by harnessing the power of neural networks and massive amounts of data known as big data, data scientists are making unimaginable gains to solve complex problems. We can confidently predict that sets of technological tools like these will rule over the world in the future.