What is the difference between Data science and Big Data?
What is Data Science?
We hear more and more about Data Science, it is the fashionable term in companies, on the web and in schools. What is this discipline?
Data science is nothing more than a multidisciplinary field whose goal is to use (digital) data to solve real life problems or to bring a certain value called “Product Data”.
Data science is the extraction of knowledge from data sets. It employs techniques and theories drawn from several other broader areas of mathematics, primarily statistics, information theory and information technology, including signal processing, probabilistic models, machine learning, statistical learning, computer programming, data engineering, pattern recognition and machine learning, visualization, predictive analytics, uncertainty modeling, data storage, data compression and computation high performance.
Wikipedia – Data Science
What is Big Data?
Traditional data processing techniques cannot effectively manage and process large and complex datasets that are too vast and dynamic. Various sources such as social media, sensors, devices, machines, and more generate structured, semi-structured, and unstructured data, which collectively make up Big Data.
There are three main characteristics that define Big Data, commonly known as the three V’s:
- Volume: Big Data involves massive amounts of data that exceed the processing capacity of conventional systems. These datasets can range from terabytes (TB) to petabytes (PB) or even larger.
- Velocity: Big Data is generated at high speed and often in real-time. Data streams continuously from various sources, requiring efficient mechanisms to capture, store, and process it promptly.
- Variety: Big Data encompasses diverse data types, including structured data (such as relational databases), semi-structured data (such as XML or JSON), and unstructured data (such as text documents, images, videos, social media posts). It may also include time-series data, geospatial data, graph data, and more.
In addition to the three V’s, two more characteristics are sometimes considered:
- Veracity: Big Data can have issues with data quality, including noise, inconsistencies, and inaccuracies. Managing and ensuring the accuracy and reliability of the data is a challenge in Big Data analytics.
- Value: Big Data has the potential to provide valuable insights and benefits when analyzed effectively. Extracting meaningful information from Big Data can lead to improved decision-making, innovation, optimization, and discovering new patterns or correlations.
To handle Big Data, specialized tools, technologies, and approaches have been developed. This includes distributed computing frameworks like Apache Hadoop and Apache Spark, NoSQL databases, data streaming platforms, machine learning algorithms, and data visualization techniques. These tools enable data scientists and analysts to extract insights, identify trends, make predictions, and derive value from the vast amounts of data that Big Data encompasses.
What is the difference between Data Science, Big Data and Data Mining?
The difference between Data Science and Big Data is immediate. Big Data is the discipline of processing and exploiting a large amount of data, while in Data Science there is no constraint on the amount of data. It therefore happens that we can use Big Data techniques in Data Science when the quantity of our data to be processed becomes very important.
The difference between Data Mining and Data Science, on the other hand, is a little less obvious to the point that some confuse the two. If there is a difference between these two terms, it comes from the fact that Data Mining is a part of Data Science. Data Mining only consists of the exploitation of data, Data Science is broader since it takes into account the acquisition of data for example.
This definition may seem vague but it comes from the fact that the discipline is broad and itself calls for several disciplines.
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Fields involved in Data Science
It is important to understand that the end goal of data science is to solve a problem in a specific domain. That said, it is essential to have a very good knowledge of the field of application before embarking on the development of a model.
It is important to note that the fields listed below do not encompass all the disciplines involved in data science. In fact, within the context presented above, data science can be approached in various ways, as long as the end goal is justified.
In general, data science involves the following disciplines:
The field of application: By field of application, we mean the sector (the environment) in which we want to create a data product or solve a problem. This could be, for example, the stock market. If we want to build a predictive model for traders based on past stock prices.
Mathematics (Statistics, Probability, Linear Algebra, Analysis, etc.):
Mathematics is heavily involved in data science. Indeed, problems are very often translated into mathematical models before being solved.
Computer science:
Computer science is the basis of data science in the sense that models are implemented with code and/or computer tools. Since the data is digital, its acquisition, storage and all processing is done using computers.
Machine learning:
Machine learning techniques are increasingly used in data science.
Algorithms:
Mastering this science is essential since all modeling is in the form of algorithms. It is important to understand concepts such as complexity.
Common sense 😉:
Which is by far what we need the most when faced with a complex problem.
Of course, being a data scientist does not imply being an expert in all these areas (although the more knowledge you have in these areas, the better). Indeed, a data science project is very often complex and composed of several stages. We can therefore find in a team people with different profiles, each being in charge of a specific step.
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Stages of a Data Science project
A data science project typically involves several stages or steps that are followed to achieve the desired outcomes. While the specific steps may vary depending on the project and organization, here are the common stages involved in a data science project:
- Problem Definition: Clearly define the problem or question that needs to be addressed. Understand the business objectives and goals to ensure alignment with the project.
- Data Acquisition: Gather relevant data from various sources, which may include databases, APIs, files, or external datasets. Ensure data quality and integrity.
- Data Preparation: Clean and preprocess the data to make it suitable for analysis. This includes handling missing values, dealing with outliers, data transformation, and feature engineering.
- Exploratory Data Analysis (EDA): Perform exploratory analysis to gain insights into the data. This involves visualizations, statistical summaries, and identifying patterns or relationships.
- Model Building: Select appropriate machine learning or statistical models based on the problem and data characteristics. Train the models using the prepared data.
- Model Evaluation: Evaluate the performance of the models using relevant metrics and techniques such as cross-validation or holdout validation. Assess the models’ accuracy, precision, recall, or any other relevant metrics.
- Model Deployment: Deploy the trained model into a production environment or integrate it into existing systems. This may involve creating APIs, web applications, or embedding the model into other software.
- Model Monitoring and Maintenance: Continuously monitor the deployed model’s performance and make necessary adjustments or updates as new data becomes available. Monitor for any drift or degradation in performance.
- Communication and Visualization: Present the findings, insights, and results to stakeholders in a clear and understandable manner. Use visualizations and storytelling techniques to effectively communicate complex information.
- Iteration and Improvement: Data science projects often involve an iterative process. Analyze the results, gather feedback, and refine the models or methodologies to improve performance and address any limitations.
Throughout the entire project, it’s important to maintain good documentation, adhere to ethical guidelines, and ensure data privacy and security. Collaboration and effective communication among team members and stakeholders are also crucial for the success of a data science project.
Sources: Cleverly Smart, DSPA
Photo credit: geralt / Pixabay
