"Careers in Information Technology: Computer Vision Engineer": GoodMan, #1

​Chapter 1: Introduction to Computer Vision Engineering

1.1.  ​Defining Data Science

Data science is a multidisciplinary area that aims to extract knowledge and insights from both organized and unstructured data using scientific methods, algorithms, processes, and systems. It includes a range of methods for analyzing large, complicated datasets and finding patterns, trends, and correlations that can help make decisions and resolve issues in the real world, including data mining, machine learning, statistics, and big data analytics.

To efficiently understand data and explain insights, data scientists combine statistical acumen, subject knowledge, and programming skills. To find useful insights concealed in an organization's data, data science integrates specialized programming, advanced analytics, artificial intelligence (AI), machine learning, and math and statistics with subject matter expertise. Strategic planning and decision-making can be aided by these insights.

Data science is one of the fields with the quickest growth rates across all industries due to the increasing volume of data sources and data itself. Thus, it should come as no surprise that Harvard Business Review named the position of data scientist the sexiest job of the 21st century. Businesses are depending more and more on them to analyze data and make practical suggestions to enhance business results.

Analysts can obtain useful insights by utilizing a variety of roles, tools, and procedures that are part of the data science lifecycle. A data science project often goes through the following phases:

1.1.1. Data Collection: The lifecycle starts with gathering data, both unstructured and structured, from all pertinent sources using a range of techniques. These techniques can involve real-time data streaming from systems and devices, online scraping, and human entry. Along with unstructured data like log files, video, music, images, the Internet of Things (IoT), social media, and more, data sources might include structured data like customer data.

1.1.2. Data Processing and Storage: Depending on the kind of data that needs to be recorded, businesses must take into account various storage systems because data can have a variety of formats and structures. Teams responsible for data management aid in establishing guidelines for data organization and storage, which makes it easier to work with analytics, machine learning, and deep learning models. This phase involves employing ETL (extract, transform, load) jobs or other data integration tools to clean, deduplicate, transform, and combine the data. Prior to being loaded into a data warehouse, data lake, or other repository, this data preparation is crucial for boosting data quality.

1.1.3. Data Analysis: To investigate biases, trends, ranges, and distributions of values within the data, data scientists perform an exploratory data analysis. The creation of hypotheses for a/b testing is driven by this data analytics exploration. Additionally, it enables analysts to assess the suitability of the data for modeling purposes related to deep learning, machine learning, and/or predictive analytics. Organizations may depend on these insights for business decision-making, leading to increased scalability, depending on the accuracy of the model.

1.1.4. Communicate: Insights are finally made easier to understand for business analysts and other decision-makers through reports and other data visualizations that highlight the insights and their implications for the company. The components for creating visuals are built into data science programming languages like R or Python; alternatively, data scientists can use specialized visualization tools.

1.2.  Data Science Vs Data Scientists

Data scientists are professionals in the subject of data science, which is recognized as a discipline. Not every step of the data science lifecycle falls under the direct purview of data scientists. For instance, data engineers usually handle data pipelines, but data scientists may offer advice for the kind of data that is necessary or helpful. Although machine learning models can be created by data scientists, more software engineering expertise is needed to scale these efforts and make programs operate faster. To scale machine learning models, it is therefore typical for data scientists to collaborate with machine learning developers.

The duties of a data scientist and a data analyst frequently overlap, especially when it comes to exploratory data analysis and data visualization. A data scientist's skill set, however, is usually more extensive than that of a conventional data analyst. In contrast, data scientists use popular programming languages like R and Python to perform greater data visualization and statistical inference.

Data scientists need more skills in computer science and pure science than a standard business analyst or data analyst does to do these duties. The data scientist also needs to be knowledgeable about the particulars of the industry, such as e-commerce, healthcare, or the manufacture of automobiles.

A data scientist needs to be able to, in summary:

Create programs that handle calculations and data processing automatically.

Have adequate knowledge about the company to be able to identify business pain issues and ask important inquiries.

Describe how the findings can be applied to resolve business issues.

Work along with other members of the data science team, including application developers, IT architects, data engineers, and business and data analysts.

Utilize a variety of tools and methods to prepare and extract data, such as databases, SQL, data mining, and data integration techniques.

Predictive analytics and artificial intelligence (AI), such as deep learning, natural language processing, and machine learning models, can be used to extract insights from large data sets.

Tell tales that eloquently explain the significance of findings to stakeholders and decision-makers across all technical knowledge levels.

Due to the great demand for these abilities, many people who are just starting out in the data science field look into a range of data science programs, including degree programs, certification programs, and courses given by educational institutions.

1.3.  The Evolution of Computer Vision

Computer scientists have long harbored dreams about the potential benefits artificial intelligence (AI) could bring to humanity. Could machines be built to perceive and comprehend the world similarly to humans? The branch of AI that deals with processing visual data is called computer vision.

Artificial intelligence (AI) developments, particularly in deep learning and neural network architectures, have allowed computer vision to catch patterns and objects with the same accuracy as the human eye. The visual cognitive system in humans is intricate. Deciphering images requires the analysis of multi-dimensional data, which is significantly more sophisticated than the input of most AI machines. The uses for multi-dimensional data are just as numerous now as they were when computer vision pioneer Larry Roberts first proposed them.

After over 60 years, computer vision still seems to have a bright future ahead of it. There is a plethora of possible uses for it, some of which border on science fiction. The technology that underpin computer vision are currently starting to catch up to our expectations for its uses, which include immediate checkout, quicker medical diagnosis, self-driving cars, and more.

Computer vision is an exciting field that lies at the crossroads of artificial intelligence and computer science. It allows computers to interpret picture or video data, opening up a wide range of applications across sectors, such as facial recognition systems and autonomous vehicles.

We must first examine the development of computer vision and delve into its current practical uses, which are already enhancing people's daily lives, in order to get a true sense of what the future may bring. Thanks to developments in both hardware and algorithms, computer vision has advanced remarkably.

Here's a quick rundown:

1.3.1. Early Years: In the 1960s, computer vision began to take shape, mainly concentrating on elementary tasks such as character recognition. However, its potential was limited by low processing power and poor image quality.

1.3.2. Feature-Based Techniques: To identify edges, corners, and other visual elements, researchers created feature-based techniques in the 1970s and 1980s. Object recognition was made possible by methods such as template matching and edge detection.

1.3.3. Algorithm Development: The 1990s witnessed a notable advancement in the field of algorithms with the creation of increasingly complex algorithms like the scale-invariant feature transform (SIFT) and the histogram of oriented gradients (HOG). Tasks like object identification and image classification were enhanced by these techniques.

1.3.4. Machine Learning Era: Support vector machines (SVMs) and neural networks, two methods that revolutionized computer vision, became popular with the advent of machine learning in the 2000s. Because deep learning techniques, particularly convolutional neural networks (CNNs), can directly learn hierarchical representations from raw pixel data, they have become the de facto method.

1.3.5. Big Data and GPUs: Deep learning model training was expedited by the availability of sizable labeled datasets (like ImageNet) and potent graphics processing units (GPUs). As a result, advances in object recognition, picture segmentation, and image captioning were made.

1.3.6. Use in Real-World Applications: Computer vision has been widely used in a number of fields recently, including retail (cashier-less stores), automotive (autonomous vehicles), healthcare (medical image analysis), agriculture (crop monitoring), and security (surveillance systems).

1.3.7. Technological Advancements: The creation of specialized hardware, such as field-programmable gate arrays (FPGAs) and tensor processing units (TPUs), has increased the speed and efficiency of computer vision operations and made real-time applications possible.

1.3.8. Multimodal Learning: Current research centers on the integration of computer vision with other modalities such as audio processing and natural language processing (NLP) to enable multimodal learning. This makes it possible for systems to comprehend and engage with their surroundings more fully.

The goal of the artificial intelligence (AI) field of study computer vision is to enable computers to intercept and extract information from images and videos in a way that is comparable to human vision. It entails creating methods and algorithms to interpret the visual world and extract relevant information from visual inputs.