Data Science: A Definition
Data Science has recently emerged as a distinct field, which has caused confusion among newcomers who want to pursue a career in the field. Earlier, it was commonly used interchangeably with Computer Science and Statistics, but it is now recognized as a separate discipline. However, since there is no agreed-upon definition of Data Science, some people consider it a mere trendy phrase, without giving it sufficient importance.
There is a broad agreement that Data Science combines techniques and principles from multiple areas, including Mathematics, Statistics, Information Science, and Computer Science. Although Computer Science and Information Science have many commonalities, there are also some significant differences. According to Jim Gray, a renowned Computer Scientist and recipient of the Works Award, Data Science represents the fourth paradigm of science, succeeding theory, experimentation, and computing.
In this article, we present an overview of Data Science, including the career opportunities it offers, its connections with related fields like Information Science and Computer Science, the essential skills required to study Data Science, and how Data Science is applied in real-world scenarios.
Essential Prerequisites for Data Science
Since the prerequisites for various roles in Data Science differ, listed below are some fundamental requirements that are necessary to begin a career in Data Science. You may also read our blog post about rear-end programmers.
- Proficiency in statistics is crucial for anyone seeking a career in Data Science. Since data science relies heavily on statistical analysis, it is a vital discipline. Therefore, those who want to pursue data science roles should possess a wide range of statistical expertise, such as Bayes theorem and probability theorem.
- Comprehending Artificial Intelligence (AI), Machine Learning (ML), and Neural Networks is crucial to achieve success in a career in Data Science. AI is an all-encompassing term for ML, Deep Learning (DL), and Neural Networks, which are utilized to program computers to mimic human behaviour. ML concentrates on building algorithms that enable machines to learn from experience and is divided into three main categories: Supervised Learning, Unsupervised Learning, and Reinforcement Learning. Neural Networks are a segment of AI, ML and DL that utilize multiple layers of neurons to process data and are comparable to biological neural networks.
- For specific roles, familiarity with high-level programming languages such as R and Python and their relevant libraries is required. Unlike in computer science, where developers must be proficient in a wide range of languages, expertise in these high-level languages is critical for data visualization and statistical analysis. So, it is expected to have a working knowledge of these higher-level programming languages in this context.
The Data Science Process for Resolving Issues:
Although using Data Science to tackle problems is a recent development, leveraging historical data to make predictions is not a new concept. Here are the essential steps for addressing such issues using Data Science.
- Initiation and Preparation
- During this phase of a Data Science project, which is typically known as the ‘problem statement’ or ‘business need’ stage, the project’s goals and objectives are precisely defined, whether it is to find a solution to a problem or meet a requirement. This stage is critical because it enables project managers to devise a comprehensive plan for the project, including its estimated cost, duration, and resource allocation.
- Data Collection
- The first step in Data Science projects is arguably the most crucial. However, in the case of original research projects and similar sophisticated initiatives, structured and cleansed databases may not always be readily accessible. As a result, other methods, such as data scraping and surveys, are necessary to obtain the required information.
- Data Cleaning
- Data cleaning is a critical step in every Data Science project, which involves detecting and eliminating irrelevant information from a database. Though time-consuming, this process is necessary to ensure precise and dependable predictions.
- Exploring the Data
- Data cleaning is a vital aspect of this Data Science project, which can yield useful insights. By analysing the data, we can identify interesting patterns and trends, allowing us to draw meaningful conclusions from the data.
- Data Modelling
- In this phase of the Data Science project, a model, like a machine learning algorithm, is developed to tackle the problem. A portion of the data is used to train and evaluate the model. To accomplish this, a technique known as ‘Data Splicing’ is employed to extract the pieces. To execute this, the complete dataset is partitioned into two sections: one for training the model and the other for verifying and validating the model. Both of these sets are referred to as ‘datasets’, with the ‘testing data’ being the more prevalent of the two.
- Refinement and Improvement
- The last phases of a Data Science project involve optimization and deployment. This is the final stage before release and mostly involves refinement.
Common Roles in Data Science
Even though a comprehensive definition of Data Science is yet to be widely acknowledged, there has been a significant surge in the number of people employed in the field. This trend is due to the growth of Big Data and related fields, as well as an increased understanding of data, allowing for more comprehensive exploration of Data Science’s potential applications. Some popular roles in Data Science are:
1. Data Researchers
Data Scientists are responsible for more than just data analysis. They must also collect data and use it in various ways. To achieve this, modern Data Scientists combine data analysis with data mining, computer science, statistical techniques, and machine learning.
2. Quantitative Analysts
Data Analysts are frequently regarded as an entry point into the Data Science field. Therefore, they have a lower skill requirement than Data Scientists, particularly in programming and algorithm creation. Data Analysts utilize their programming skills to collect, arrange, and analyze data for valuable insights.
3. Big Data Engineers
Unlike the other mentioned professions, data engineers have no obligation to carry out any data analysis. Their primary focus is on the design and functionality of the data pipeline. As a result, they must plan, build, and manage the information systems responsible for collecting, storing, and retrieving data for the aforementioned applications.
4. Data System Architects
The comparison between engineers and architects is relevant, given the names of these two professions. Consequently, data engineering is primarily responsible for execution, while data architects are accountable for design. As a result, there is a significant overlap between the two, and in smaller companies, one person may perform both roles.
Data Science: An Overview of Related Disciplines
It is crucial to distinguish between data science, computer science, and information science and identify where they intersect. Grasping the nuances between these fields is essential to understand the potential pros and cons of each, contingent upon the nature of the task being performed.
It is logical to examine the connection between Statistics and Data Science. Some individuals believe that Data Science is a subfield of Statistics and simply another name for it. A critique is that Data Science prioritizes digital information more than Statistics does. Although both fields deal with numerical data, Statistics emphasizes describing the data whereas Data Science concentrates more on predicting and acting upon it.
Here are some of the key characteristics of data science:
- Data Science investigates information in any format, ranging from structured to semi-structured and unstructured data.
- Data scientists are accountable for collecting and evaluating data in diverse formats to resolve problems and provide solutions through software.
- Data Scientists are chiefly focused on Data Mining, which entails recognizing patterns within data, and Data Transformation, which involves modifying the data structure.
Here are some of the distinguishing features of computer science:
- The subject that examines how computers and their related systems operate is computer science.
- Experts in this domain investigate the internal mechanics of computers and other computational devices.
- Computer scientists may have different career goals, depending on their specific field of work. Some examples may involve UX/UI design, web/app design, and cybersecurity.
These are some of the key characteristics of information science:
- Information Science, initially defined as the oldest among the three, is presently acknowledged as a problem-solving methodology that encompasses the necessities of all stakeholders and employs relevant data and technology.
- Information Science offers various job opportunities, including Information Scientists, Systems Analysts, and Information Professionals.
Glossary of Terms Used in Data Science
Data Science is an interdisciplinary domain encompassing several academic disciplines, resulting in an extensive collection of terms that those interested in the field should be acquainted with. This blog presents an overview of several common as well as a few lesser-known terms used in Data Science. Some of these are listed below:
The Five Cs of Analytics: Data Science (DS) can optimise a business in many ways by effectively integrating products, pricing, marketing, location, and workforce to achieve the ultimate outcome.
Backpropagation: In the domain of data science, backpropagation can be employed when the actual output of a neural network varies significantly from the anticipated output. The technique involves assessing the error at the network’s output, feeding it back, and adjusting the weights to decrease the error. This method is especially helpful when working with neural networks.
Bagging: Merging forecasts from multiple models into a single estimation is called “bagging” or “bootstrap averaging.”
The Bayesian Theorem: The Probability Theorem can be employed to compute conditional probability. The equation P(A|B)={P(B|A). P(A)}/P(B) denotes the fraction of all A events that are B on the left-hand side, while the right-hand side represents the probability of B. This theorem is extensively utilized in the domain of data science.
Information Extraction: Statistical techniques are implemented to evaluate extensive datasets, including both structured and unstructured data, to identify crucial insights. These methods are essential in the field of data science and can be applied to multiple domains.
Dplyr: The R package dplyr facilitates data manipulation. It can be used with both local and remote datasets for data cleansing and modification purposes.
Flume: Flume software allows for the real-time transfer of data logs to Hadoop. Several Flume agents can be configured to operate simultaneously, facilitating the processing of vast amounts of data.
GGplot2: GGplot2 is another R package that aids in data visualization. This tool primarily focuses on plot creation.
Hadoop: Hadoop is a cost-free, open-source system that processes large datasets in parallel.
Hive: Hive, an extension of Apache Hadoop, is a framework utilized for managing structured data. Hive offers a SQL-like interface to summarize, query, and analyze data, and you can learn more about it on the Apache Hive page.
Keras: Keras is a neural network library that operates with the Python programming language. This library was formulated using Python and can be used with TensorFlow and Theano to facilitate the application and upkeep of neural networks.
Conclusion
Based on the preceding discussion, we have now comprehended the scope of Data Science and its possible applications. It is apparent how Data Science functions, the tools it employs, the advantages it provides, and the educational credentials required to pursue this field.
