Data is all around us. Every large company is currently pouring significant resources into gathering as much data as possible about their customers and operations, allowing them to analyze it and extract useful patterns.
Data production has also been growing rapidly, partly due to the strong presence of Big Data, which is no surprise when you consider that a whopping 2.5 billion gigabytes of data are generated every day. And that number keeps growing!
With that in mind, it should be no surprise that data scientists are in high demand. And with annual salaries of $120K and upwards, the demand for experienced data scientists keeps rising.
So, why data science? Well, whether you work with data or not, we are all living in the age of big data, so it’s helpful to understand how different companies collect and process data.
In this article, we will dive into the details of data science, including how it’s used, the data science lifecycle, applications, benefits, and more.
So, What Is Data Science?
How do we create a data science meaning? In simple terms, data science includes collecting, organizing, storing, and analyzing data in large volumes to identify patterns that would otherwise go unnoticed by ordinary humans.
Some students get into the field with a data science certification program like the one at Turing College. That helps them explore the fundamentals with the help of industry experts as they build out their own portfolios.
Great, we now have a data science definition, and we understand the purpose of data science, but we should also point out that data science combines multiple disciplines, meaning people from various backgrounds can find a place in the sector.
When working with data science information, a good data scientist must maintain a reasonable balance between knowledge and skills in statistics, programming, databases, and artificial intelligence.
The field is spread across multiple areas, with data mining, data scraping, and advanced visualization techniques being prevalent.
With all that said, what is a data scientist, who is a data scientist, and what do data scientists do? Well, a data scientist is someone that carries out the activities we’ve described above. They can also bring domain-specific expertise, allowing them to organize data more reliably and understand the implications of working with different sources.
How Is Data Science Used?
Data science primarily revolves around four analytical approaches; in many cases, a combination of these will be utilized. Let’s look at these data science concepts.
- Descriptive analysis: Attempts to provide a comprehensive overview of the relationship between certain data points and is commonly focused on intuitive visualization to allow everyone involved to gain a deeper understanding.
- Diagnostic analysis: Investigates the reasons behind trends identified via descriptive analysis. A company might see its sales peaking at certain periods of the year with the help of descriptive analysis, but diagnostic analysis helps to understand why it happens.
- Predictive analysis: Attempts to leverage historical data to predict future trends. For example, a company might analyze past financial records in an attempt to predict its most lucrative months over the next year.
- Prescriptive analysis: This is the final stage in the analytical chain. It occurs after we’ve found the reason behind certain developments and created a model of what the future may look like. We can then use prescriptive analysis to develop a viable long-term strategy aligned with those predictions.
The Data Science Life Cycle
The data science life cycle can be broken down into five stages. Let’s take a look at each of these data science components.
[Stage 1] Capture:
Data can be captured in various ways, including using existing data sets that naturally accumulate over time. Other methods include web scraping from sites and services with automated tools, manual data entry, or purchasing data sets from third parties.
[Stage 2] Maintain:
Long-term data storage can be challenging due to the ever-evolving nature of storage media. Plus, data sets are seldom perfect and must be cleaned to be useful for analytical purposes. The maintenance step often involves processing data to prepare it for different analytical approaches and transferring large data sets between storage locations.
[Stage 3] Process:
This involves several stages: data mining, clustering, modeling, and summarization. Data mining attempts to identify useful patterns in data, while clustering organizes data sets into relevant categories. Modeling involves creating representations of relationships between data sets, and summarization attempts to narrow the contents of a data set to a brief description.
[Stage 4] Analyze:
This is accomplished via several techniques, with exploratory and confirmatory analysis being the main two. Exploratory analysis seeks to identify points of interest in a data set, and confirmatory analysis confirms those theories.
Other methods include regression analysis in the context of external variables to enable forecasting, text mining to identify meaningful patterns in text, and qualitative analysis of data that cannot be directly mapped to numerical values.
[Stage 5] Communicate:
There are various visualization and presentation techniques available for different types of data. Business intelligence (BI) is sometimes used interchangeably with data science, but data science is just a single element of BI.
Benefits of Data Science
Data science can enable companies to identify patterns they were previously unaware of, allowing them to target new and untapped market segments.
Companies can also innovate their current solutions with reasonable expectations about the impact on future operations rather than shooting in the dark.
Data science can also help with real-time operations. By constantly fine-tuning parameters, companies can improve performance on the go, even in a chaotic environment.
For example, a company could investigate sales patterns and cross-reference data with customer support (CS) logs, revealing a link between CS response times and purchase likelihood.
Applications of Data Science
Banks actively use data science in fraud prevention. With large transactional volumes, it's impossible for the average bank to monitor everything manually. Data science applications enable banks to understand complex customer activities and identify potentially malicious activity.
Medical researchers frequently implement an application of data science to analyze large data sets and find new approaches to treating difficult conditions. Hospitals may also use data science to improve patient handling in real-time.
Companies can squeeze out more performance from logistical operations with data science, whether that’s shifting larger quantities of supplies, optimizing transport schedules, or minimizing the occurrence of traffic jams and accidents.
You see these everywhere nowadays, with sites like Amazon frequently suggesting products relevant to your interests and YouTube recommending videos that interest you.
Data science is at the heart of modern image recognition technology, with various industrial applications. For example, warehouses can use image recognition for product sorting, factories use it for early fault detection, and security services use it to identify people from video footage.
This is a constant game of cat and mouse between researchers and malicious actors, and artificial intelligence (AI) has played a major role in shifting the balance. Automated analysis enables security service providers to operate more confidently while relieving employees of menial tasks like monitoring security feeds.
The gaming industry has been utilizing data science to study the behavior of players and improve their experience, but also for generating content and streamlining production pipelines.
With the amount of data a modern search engine has to process, data science is the only option to ensure results are delivered fast and accurately. Google, Microsoft, and all other major search providers heavily use data science.
Some of us have grown accustomed to modern digital assistants and use them daily. Those solutions are heavily driven by AI in the backend, especially regarding speech recognition and synthesis.
By studying customer habits and behavioral patterns, companies can deliver targeted advertising with a higher chance of success as users are more likely to engage with ads directly relevant to their interests.
Planning flight routes can be tricky with increasing aircraft numbers in the sky. With the help of data science, this can be optimized to ensure no wasted resources.
AR and VR are still taking off in the consumer market, but AI is already playing a major role in their growth. Image recognition plays a major role here, as does geographical analysis and advanced user interaction.
Data Science in Action
Multiple companies have been experimenting with data science solutions to improve customer retention. Kellton is one of the pioneers in this field, offering multiple tools to assist companies, including predictive search and chatbots.
Companies are actively using data science to improve routes, use less fuel and other resources, and ultimately make logistics more optimized. BlueCargo recently announced a project that aims to decrease downtime during container ship transit through terminals, leveraging data science to optimize routes.
Data science accelerates drug research, streamlines patient care, and improves facilities’ predictive abilities. A good example here is the Shanghai Changjiang Science and Technology Development, which has developed an AI platform for assessing medical records to identify patients at increased risk of suffering a stroke.
Tesla is constantly hiring data scientists, and it’s far from the only company keeping an eye on the sector. Autonomous vehicles are the perfect candidate for utilizing the power of advanced analytics as they generate a lot of data, some of which has to be processed in real-time.
Law enforcement heavily uses data science to analyze criminal patterns and even predict crimes ahead of time. Independent organizations have been attempting to use data science to improve law enforcement, including a startup that wants to introduce AI-driven automatic analysis of officer body cam footage.
Data Science Tools
Data science uses various tools, including programming languages, databases, and purpose-built suites for capturing and analyzing data.
While several programming languages are available to data scientists, Python and R are at the top of the leaderboard.
Python, in particular, enjoys popularity due to its unique combination of powerful libraries and an intuitive approach to programming. Several Python libraries like Scikit-learn and pandas have established a prominent place for themselves in this field.
SQL is another important language skill, as while you won’t need to go in-depth, basic knowledge of building efficient queries can go a long way toward working as a data scientist.
Databases are one of the primary tools for storing data in data science, as they can enable researchers to find links between points in large data sets while organizing data sensibly.
Comprehensive software suites include various tools in one complete package. A great example is SAS (Statistical Analysis System), which provides users tools for working with data, including IoT (Internet of Things) analytics, dedicated BI solutions, and dozens of others for capturing and analyzing data.
Jupyter is a particularly popular choice here as its virtual notebook allows data scientists to build analytical solutions from building blocks. It also has testing and documentation features for creating programs on the go.
Amazon’s AWS also features a complete package of analytics tools, including scalable data lakes, support for multiple types of analysis, data migration services, and API access to deep learning features.
Unsurprisingly, Google also offers a comprehensive solution set for data science, including tools for data discovery and integration, warehousing, preprocessing, and toolkits for building custom AI solutions.
How to Learn Data Science
If you’re interested in learning data science, you must spread your efforts across several main fields. At the minimum, you’ll need a solid understanding of programming and math, especially statistics.
Working with databases and machine learning is also unavoidable, and, of course, you need to understand the general data science life cycle.
But what is a data scientist exactly, and what kind of experience should one bring to the table? Let’s examine the main skills you’ll need as a data scientist.
You don’t need to be an advanced programmer to be a data scientist, but you need an understanding of basic concepts like loops, file I/O, and simple data structures. Of course, brushing up on your programming skills can definitely help down the road.
Most of your programming work revolves around using libraries as building blocks for your own solutions. But without the ability to understand the underlying code, it will be difficult to make modifications, especially without compromising the system's performance.
What is data science without statistics? While you may be able to build basic solutions without understanding the underlying concepts, a background in stats will go a long to help you learn why you’re using certain approaches in the first place. It can also help you identify better solutions.
At the bare minimum, you’ll want to dig into probability theory and descriptive statistics. Some additional concepts you should explore include covariance and correlation, statistical significance, mean, variance, standard deviation, and p-values.
You should know how data is stored in a database and how to optimize access. This may not matter much when you’re working with small data sets but becomes more important as the scope of your projects increases.
Start with the basics by learning how a database works (especially relational databases), CRUD operations, and basic queries. You should know how to retrieve simple records and how to cross-reference multiple tables.
Machine learning is a broad field, and it’s impossible to keep up with it unless you’re actively involved at the core. That said, you should still have a general idea of what machine learning is, how to deploy and customize your own solutions, and what you can do to improve them.
Start by learning about the three main types of machine learning: supervised, unsupervised, and reinforcement. You can dig into each in more detail by exploring regression, classification, and clustering.
Knowing how to represent data via mathematical models helps maintain a structured approach to your work. This does not require in-depth research, but you should know how to find your way around research papers and online discussions.
You should also have a solid understanding of linear algebra and calculus. While you may not do a lot of hands-on work with mathematical models, understanding them in detail will be useful for adjusting models for your own needs.
Business Intelligence vs Data Science
At its core, data science is a subset of business intelligence (BI), but let’s take a closer look at the differences between the two.
Business intelligence focuses on historical trends and analyzing the present state of a company’s operations.Data science, however, is usually more focused on predictive analysis, meaning it’s interested in the company's future direction.
Data science uses structured and unstructured data, while BI relies on structured data. The analytical methods used in BI focus on descriptive and static analysis, while data science focuses on exploratory analysis.
Most of the skills used in data science are also relevant for BI, but BI also requires a strong approach to visualization and presentation, along with advanced communication skills.
But what is data science in a business context? Well, it is focused on working with business data and identifying patterns that can benefit the company’s growth.
Focuses on descriptive analysis
Focuses on predictive and prescriptive analysis
Solutions developed for specific problems
General solutions for dealing with various data-related problems
Can be utilized by general business people
Requires experience as a data scientist to use
Focused on analyzing historic trends and present problems
Explores predictions for the future of the company and identifies solutions for potential problems
Strong emphasis on intuitive visualization and presentation with interactive dashboards and reports
Focused on statistical models and hypothesis testing
Used to develop decisions for the company’s future actions
Used for strategic analysis and planning
Cloud Computing vs Data Science
Cloud computing is an auxiliary tool that can support data science. While data science focuses on specific methods for capturing, storing, and analyzing data, cloud computing is concerned with providing geographically independent access to data and processing tools.
Modern data science solutions tend to rely heavily on cloud computing, as they often involve working with large data sets. This means they need tools that allow for easy scaling and distribution, and cloud computing is ideal for this.
Cloud computing can also allow teams to utilize solutions without having to manually deploy them. Researchers can spin up new virtual computing instances without needing to reconfigure a system, and everything can be updated automatically without user intervention.
So, what is data science? Well, if you’ve made it this far, you know that data science is a broad field with numerous applications. But at its core, data science involves collecting, organizing, storing, and analyzing data to uncover hidden patterns in data.
With that said, it feels like we’re barely scratching the surface of what’s possible with data science. And while it’s unlikely that data science’s objective will change anytime soon, the underlying tools and solutions are constantly evolving.
And as the field of data science continues to grow, learning about data science will likely become more complicated in the future.
If you’re interested in data science, consider developing programming, databases, machine learning, and statistics skills. It also helps to learn about cloud computing, data science applications, and the link between data science and business intelligence. And as they say, the best time to start learning is right now!
Frequently Asked Questions
1. What’s the Difference Between Data Science, Artificial Intelligence, and Machine Learning?
Data science focuses on collecting, storing, and analyzing data, and data scientists write tools for processing data and utilize statistical models to gain deeper insights. Machine learning (ML) uses statistical models to automate analysis via statistical models.
It’s common for some to confuse machine learning with artificial intelligence (AI), but ML is a subset of AI. The main goal of AI is for machines to “understand” requirements and identify their own solutions to different problems.
2. Define Data Science in Simple Words
Data science is a collection of practices for capturing, storing and analyzing data for various purposes. It’s useful for extracting patterns from data sets and identifying new ways to use that data.
3. What Does a Data Scientist Do?
A data scientist is responsible for collecting and cleaning data, ensuring that it’s easily accessible for all researchers involved in the project. They identify insights within large and complex data sets with the ultimate goal of assisting business decision-making and enhancing growth. They also use ML and AI tools to analyze that and find hidden patterns.
4. Explain Data Science with some Examples
Data science can be used to analyze customer spending habits, allowing a store to optimize restocking and product placement for maximum engagement. It can also be used to find links between medical treatments, leading to the discovery of new treatment approaches.
The Pre Cancer Genome Atlas is a massive data science project that aims to build a database of factors on the development of lung cancer, with the aim of allowing physicians to diagnose before symptoms have manifested.
5. Can I Learn Data Science on My Own?
Yes, learning data science on your own is perfectly doable. If you don’t have a good foundation in programming and statistics, you will need to get these skills up to speed first. You can also look for online resources like courses and boot camps to help bridge any knowledge gaps.