What is data science?

At MakoLab, we have been developing and honing our AI-engineering skills for years and we offer a range of services rooted in data.

Data science is a set of methods and practices designed to collect data and transform it into conclusions. What kind of data, though? Well, that depends on the business. Stock prices, IoT sensor data, data from sonars or from sales of services or products... and that’s just the tip of the iceberg. The methods and practices include conducting statistical analyses of data, data processing and data visualisation, applying a user experience approach.

Several major components of data science can be identified: 

• data acquisition: preparing data for further processing and interpretation. The data are transformed into digital numeric values which can then be processed by a computer. Data acquisition includes registering, sampling and quantising data;
• data cleaning: detecting, correcting and/or removing corrupt or inaccurate records from a database. Data cleaning identifies incomplete, incorrect, inaccurate or irrelevant data and then replaces or modifies them. Incomplete and inaccurate data may also be deleted;
• data transformation: this process, which involves changing data from one format or structure to another, is fundamental to data integration and data management;
• data visualisation: this process involves graphical representation of data using visual elements like charts, graphs, maps and tables. It is essential to presenting data simply and accessibly for a company’s non-technical staff and is highly useful during conversations with clients.

Data science and AI are commonly equated with each other. In fact, though, they are two distinct fields that are related and complementary. As mentioned earlier, data science is interdisciplinary. It uses statistical, mathematical and computer science techniques to analyse large collections of data in order to detect patterns and draw conclusions. In contrast, AI is focused on creating systems capable of performing tasks that normally require human intelligence, such as processing natural language, compiling summaries, making mock-ups and quickly generating images.

What links data science and AI? The element that binds them is machine learning. ML is a subtype of AI, employing algorithms to learn from data. In data science, it is used for creating predictive and analytical models on the basis of data collections. Thanks to machine learning, artificial intelligence has better capabilities for analysing and interpreting data, leading to smarter, more accurate applications.

Data science is a field that uses a scientific approach to extracting meanings and conclusions by means of various data analysis tools. ML is regarded as a subtype not only of AI, but also of data science. That would mean that every ML model is also a data science model and every ML algorithm is also an AI algorithm. However, not all AI solutions require the use of ML, nor does every data science project employ it. 

Data scientists often have a broad view of data, which can include a business model, domain and data collection process. Machine learning, though, is primarily focused on the computational processing of raw data. As such, although data science and machine learning are linked, they are not the same. Data science is a wider field that incorporates machine learning techniques, but is not limited to them.

We used machine learning in our PoC, designed for financial institutions, insurance companies and claims experts, with the aim of optimizing claims detection and analysis processes and identifying potential frauds.

Working with data often means working on large data sets, which is why cloud-based tools that can scale with the growth of data are often used. Solutions of this kind also facilitate teamwork and integration with other tools, enabling advanced data analytics to be performed faster.

Below, we present several trends that are set to be significant in the field of data science.

One anticipated hot trend in 2024 is the integration of business intelligence (BI) and AI. Integration with AI will certainly increase the analytical capabilities of BI tools by automating repetitive tasks. Detecting patterns in data will be quicker, as will identifying those that might otherwise have gone unnoticed. Combining BI and AI will also speed up teamwork.

The industrialisation of data science means moving to a more automated process. Tools such as data science platforms, machine learning operations (MLOps), feature stores and automated machine learning (AutoML) are being used in the transformation. MLOps automates life-cycle models, feature stores manage the input data for models and AutoML simplifies the creation and tuning of models.

Blockchain technology increases data security and transparency by verifying source authenticity, maintaining data integrity and creating tamper-resistant audit trails. It reduces the risk of data breaches by eliminating single failure points, which protects confidential information from unauthorised access and data manipulation.

Finance, health care and supply chain management are just a few of the sectors that are making the most of these benefits.

Data science has a crucial role to play, combining statistics, mathematics, computer science and AI to analyse data and draw meaningful conclusions from them. Integration with AI via machine learning makes it possible to create accurate predictive models. Trends like integrating BI and AI using MLOps and AutoML and the industrialisation of data science are signalling that the future of data science lies in automation. Blockchain, on the other hand, elevates security and data integrity. 

At MakoLab, we never stop developing our data science skill sets and employing AI technology to enhance them.

What is data science and why is it important for businesses?

Data science is an interdisciplinary field that combines statistics, mathematics, computer science, and AI to extract meaningful insights from data. For businesses, it is crucial as it transforms raw data into actionable strategies, helping in pattern detection and informed decision-making. By leveraging data science, businesses can optimise operations, enhance customer experiences, and drive innovation. Check our data services page.

How does data science integrate with AI to benefit businesses?

Data science and AI are distinct yet interconnected through machine learning. This integration enhances analytical capabilities, enabling predictive modelling and automation of repetitive tasks. Businesses benefit by achieving better data interpretation, more accurate predictions, and faster decision-making processes. This synergy is essential for developing smarter applications and efficient workflows.

What role does blockchain technology play in data science?

Blockchain technology ensures data security and integrity, a vital aspect for industries like finance and healthcare. It safeguards data against unauthorized access and manipulation by creating tamper-resistant audit trails and eliminating single points of failure. This enhances trust and transparency in data handling processes, crucial for maintaining confidentiality and compliance. 

What are some emerging trends in data science that businesses should be aware of?

Key trends include the integration of Business Intelligence (BI) with AI for enhanced analytics and the industrialisation of data science through MLOps and AutoML for automation and efficiency. These trends point towards a future where data analysis becomes more streamlined, with faster pattern detection and improved teamwork capabilities.

1. IBM, What is data science?
2. Nisha Talagala, AI And Data Science - What Is The Difference? , Forbes
3. Wikipedia, Data acquisition 
4. Wikipedia, Data cleansing 
5. TIBCO, What is data transformation?
6. Tableau, What Is Data Visualization? Definition, Examples, And Learning Resources 
7. Power BI, Key trends in data analytics in 2024 
8. Gartner, Gartner Identifies the Top Trends in Data and Analytics for 2024 
9. Randy Bean, Thomas H. Davenport, Five Key Trends in AI and Data Science for 2024 
10. Simplilearn, Top Data Science Trends Reshaping the Industry in 2024
11. DataSpace Academy, Top 8 Data Science Trends For 2024 

Translated from the Polish by Caryl Swift