Learning about the building blocks of data science can be confusing. Data mining, big data, machine learning, artificial intelligence, predictive analytics, business intelligence; you dive into one of these topics but consider what you think data science is.
Data Science is the field of scientific research that uses statistical techniques and computer systems to analyze data. At its core, Data Science relies on Mathematics, Statistics, and Computer Science to analyze big data sets through the many algorithms and frameworks generally used in those fields.
Spot the difference!
The building blocks of data science are different from the building blocks of computer science. It involves advanced statistics and mathematical modeling techniques in fields such as machine learning, pattern recognition, data mining, visualization, and predictive analytics.
If you have ever wanted to get a glimpse into the world of data science, this blog is for you. We will lay down the groundwork for what data science is, some of the most important steps in a data science project, and show some real-life examples of it.
Data science is defined by using data to create knowledge that empowers action. The basic building blocks are data, algorithms, and visualizations.
It is a multidisciplinary field that combines the core concepts of computer science, statistics, and knowledge discovery. A career in data science can be extremely rewarding. It has many applications for enhancing business and industry and making advancements to other fields like health or biology.
Data Science demands mastery in statistics, machine learning, and data engineering.
Data science is a rapidly growing field with a lot of subfields. In my professional experience, I have found great demand for data scientists who have mastered all three of the building blocks of data science: statistics, machine learning, and data engineering. Without all three strong skills, you will find difficulty landing a competitive job in your desired location.
Big Data is changing the world, and big data analytics transform businesses into data-driven organizations. But to get there, one must first work on mastering several different skills and disciplines for which there is no single point of entry. Aspiring data scientists need to master diverse disciplines that integrate statistics, database systems, software engineering, and machine learning.
How to get started with Data Science?
Data science as an industry has gained popularity over the last few years. This is due to many factors, including a large amount of data available to us, technological advances like high-speed internet and big data processing software, and more people wanting a career in this industry.
Data science is an interesting occupation, especially when there are tons of raw data being generated by different online sources and companies. That said, data science is quite an intimidating career path which is why many people are hesitant to get started.
The life cycle of Data Science
Data science is an emerging field that attacks complex problems by bearing the power of statistical analysis, machine learning, and high-performance computing. So now that we have gone through the building blocks of data science, what can we do with them? We will explore this in detail in the upcoming chapters.
Capture
Data science can be broken down into a five-step process. The first step, capture, is gathering data from various sources. Data is always distributed across various business applications and systems; it is never in one place. New data can also be entered into a system, and this process can either be manual or automated. Another way to collect data is by sourcing it through data devices. The rise of the Internet of Things (IoT) has made it significantly easier to collect data through data devices. Finally, data can also be extracted from various sources such as web servers, databases, logs, and online repositories through data extraction.
Maintain
This step deals with what happens to the data once it is sourced — the process of data warehousing stores data collected from various sources. The next phase involves removing inaccurate, unreliable, duplicative, and missing data from the database. Next, the remaining data is staged and processed for interpretation by machine-learning algorithms. Finally, the data is transferred efficiently to other locations using a framework.
Process
Once the data is free of errors, it is processed to find trends and future patterns. Data mining identifies trends and groups data into clusters based on similar traits. A descriptive diagram shows the relationships between different data types, which are then summarized.
Analyze
After the data has been classified and modeled, it can be analyzed. Regression, text mining, and qualitative analysis help predict trends in data by analyzing the data.
Communicate
To gain utility from their data, companies must display their information physically or visually. This allows them to identify trends, patterns, and outliers in data. Actionable insights are produced when companies use these insights to produce reports and visual representations of the results of their analysis.
What is a successful data science strategy?
Even though our guide, “What are the building blocks of data science,” is rapidly gaining popularity among businesses and IT leaders, many companies have a hard time implementing and executing their data science strategies.
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Takeaway notes
Data science is a hot topic where we can hear almost every day new ideas and techniques for analyzing data. In this article, we wanted to show you the building blocks of data science and which type of skills you will need to obtain.
It is an area that continues to grow and develop in its own right, accumulating many different elements. It’s a discipline based on mathematics and core principles, but it also relies heavily on engineering to develop programs and applications. On top of that, it requires a special kind of creativity: the ability to find patterns in data and make predictions from it. Data science will always be a very broad subject with various specific areas within it that still need to be defined.
