What is Data Science?

Data Science is an emerging, interdisciplinary field that uses scientific methods, tools, algorithms, theories, machine learning principles, and systems to review, analyze, extract, and provide significant insights based on large amounts of complex data. 

As of late, the world of technology is growing fonder of everything data related. Whether it’s data processing, data analytics, data storage, Big Data...everything seems to be all about data. Therefore, it is no wonder that we now have Data Science added into the mix. But what exactly is Data Science? Is it really a science? While the concept continues to evolve quickly and offers boundless possibilities for growth and a deeper definition, here’s our humble take on this hot topic.

Data Science is designed to improve the decision-making process, the product development process, the trend analysis process, and the forecasting process. With its use of mathematical, computational, and theoretical practices, Data Science allows the study and evaluation of data on a superior level.

Data Science is comprised of several techniques such as data mining, information science, Big Data analysis, data extraction, computer science, and data retrieval. According to Techopedia, Data Science is based on “data engineering, statistics, programming, and natural language processing, among others.” 

The field of Data Science holds promise and is experiencing increased demand which continues to rise and draw the attention of IT professionals and experts. Data Science is past the regular path of traditional analysis, data mining, and programming skills - instead, it furthers the use of these concepts to uncover powerful intelligence for any organization that invests in this field.

Data Scientists are the experts on Data Science. While Data Scientists originate from varied fields and different work experience backgrounds, they share common traits and skills that qualify them. These common denominators include expertise in  business domain, statistics and probability, computer science and software programming, and excellent communication skills. These are not the only desirable skills, but they are certainly the most common ones for a well-prepared Data Scientist.

What is Machine Learning?

Machine Learning is a data analysis technique that gives computers the ability to learn, identify patterns and make decisions autonomously. 

While the term was coined by Arthur Samuel back in 1959, the more formal and widely quoted definition of Machine Learning comes from Tom M. Mitchell who states that “a computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.”

Machine Learning is essentially a method that automates analytical processes by learning from data, identifying patterns, and making educated decisions with minimal human involvement. Machine Learning originated from the field of Artificial Intelligence with the premise of minimizing human intervention and automating as many tasks as possible.

Just like data science, machine learning is also considered a science that uses algorithms and mathematical models in computers to get them to act without being explicitly programmed.

Nowadays, we use computers with Machine Learning capabilities to help us get a job done quickly and with minimal to no human interaction. Machine Learning plays a key role in data analysis as it automates the process and reduces the time necessary to analyze massive amounts of data.

The rise of Machine Learning came from the emergence of the internet and the realization that so much data is available for analysis.

There are three areas that constitute Machine Learning: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning trains a model to use known inputs and outputs of data to predict future outcomes. Unsupervised learning finds patterns in data based solely on inputs. Reinforcement learning involves an agent that learns from cumulative reward, discovering the best course of action from experience.

Data Science and Machine Learning: How they help each other to solve problems

There is one term that is closely related to Data Science: Machine Learning. For this section, we are going to focus on Data Science vs Machine Learning comparison and their similarities.

Now, let’s get to the nitty-gritty. Data Science and Machine Learning rely on each other to make decisions, perform analytics, and make predictions. Specifically, Data Science leverages Machine Learning for pattern discovery. Machine Learning unearths hidden patterns within a dataset in order to make meaningful predictions - the most popular algorithm for pattern discovering is clustering. Clustering is used for exploratory data analysis to discover hidden patterns in data; its most common model algorithms include hierarchical clustering, self-organizing maps, subtractive clustering, and more.

Much like Data Science and Analytics, Machine Learning is widely considered a subfield of Data Science and consequently, there is an overlap between the two concepts. The true difference between the two lies in the fact that Data Science is a broader, multidisciplinary concept that doesn’t focus solely on algorithms and statistics, but deals with the entire data processing methodology, which includes data science analytics, software engineering, data engineering, predictive analytics, business analytics, and more. In essence, Data Science is responsible for bringing structure to Big Data, discovering patterns in data, and empowering decision-makers with insights.

Currently, Machine Learning models are regularly used and applied to Data Science. There are numerous Machine Learning techniques that are incredibly useful for Data Science such as deep learning, decision trees, clustering algorithms, data modeling, and more. Ultimately, Data Science uses a collection of algorithms derived from Machine Learning to develop a solution that stems from statistics, mathematics, analytics, and more.

Data Science: An entire data processing methodology

Depending on the project’s requirements, goals, and objectives, the Data Science process or methodology can vary and adapt to meet different needs.

Usually, it is comprised of the following stages:

• Data collection and storage: Straightforward, this phase collects and stores data that is required for the data science project.
• Discovery and goal identification: Smart questions must be answered to identify goals and objectives of the project.
• Ingestion and integration of data: Data must be absorbed and integrated into the project to prepare it for data science analytics operations.
• Processing and cleaning data: Data is only valuable when it is clean and ready for processing. In this stage, data is categorized and cleared of any unwanted items.
• Investigation and exploratory data analysis: In this stage, a deeper assessment of specifications, requirements, priorities, and budget is performed to fully understand the implications of each element.
• Selection of models and algorithms: Determine the models and algorithms that will draw the best results for the project. 
• Use of data science techniques and methods: Apply the techniques, tools, methods and all involved resources to the project.
• Measure and validate results: Data is prepared for testing purposes to analyze its value and define insights.
• Deliver, communicate, and present final results: Deliver final results, reports, briefings, code, and technical documents to implement a solution in a real-time production environment. Additionally, in this stage Data Scientists provide a clear picture of performance and any constraints, if available.
• Decision-making based on final results and insights: Now that all results are comprehensively provided, decision-makers can leverage the valuable insights to determine new courses of action, changes, updates, modifications, and any necessary measurements to achieve the best outcomes for the business or technology challenge.

This Data Science process or methodology is oftentimes referred to as the Data Science Lifecycle (DSLC). This method encapsulates programming skills, statistical prowess, visualization techniques, and business knowledge in order to work together with the common purpose of translating business questions into actionable answers. As we mentioned before, a Data Scientist is the Data Science evangelist who spearheads and connects the dots between the business world and the data world, so they are highly specialized and knowledgeable on the DSLC method.

The DSLC is commonly comprised of a common toolset that includes Python, Tableau, SQL, R, Apache Hadoop, NoSQL databases, GitHub, MapReduce, Cloud computing, Apache Spark, and more.

Data relies on algorithms in Machine Learning

Both Data Science and Machine Learning leverage algorithms. Similar to the principles of a cooking recipe, an algorithm is a collection of instructions for computers to fulfill a designated task. With algorithms, engineers and developers can create a lot of different applications that achieve different tasks.

Some of the most popular algorithms are:

• Naïve Bayes: It classifies every value as independent of any other value. It enables the prediction of a class or category based on a set of features by using probability.
• K Means: It categorizes unlabelled data by discovering groups within the data set. Then, it iteratively assigns each data point to a group (K - the variable that represents the number of groups). In essence, it finds groups in data based on similarities rather than predetermined labels.
• Kernel methods: Algorithms used to analyze patterns from raw data, categorized into rankings, clusters, and classifications. The most popular use of kernels is the Support Vector Machines algorithm, which we will clarify next.
• Support Vector Machines: This algorithm analyzes data used for classification and regression analysis. It filters data into categories via training examples and then creates a model to assign new values to one or more categories.
• Linear Regression: The most basic type of regression that enables the understanding of relationships between two variables.
• Logistic Regression: Used for binary classification problems where there are two possible outcomes influenced by one or more variables. It estimates the likelihood of an outcome given a set of variables.
• Random Forest: This algorithm is a compound of thousands of decision trees. Each decision tree uses graphs to model decision-making; each node represents a question about data; each branch represents possible answers to a question.
• Neural Networks: It mimics how a human brain organizes, assembles, and understands information to reach predictions. An artificial neural network passes information via an input layer, a hidden layer, and an output layer, consisting of raw features and interconnected neurons.

The selection of algorithms is critically important to mesh the needs of the business, requirements, specifications, time, and more variables of a data-related project. Usually, there are a number of factors that affect the decision of selecting algorithms, including data size, data quality, data diversity, accuracy, data points, parameters, and more.

Data Science vs Machine Learning

In this section, we describe the main actors of the Data Science vs Machine Learning duel:

• Scope: Data Science is by far the broader concept of the two as it is multidisciplinary and it actually encompasses the definition of Machine Learning within itself. In Data Science, data is used from all available areas, while Machine Learning focuses on algorithms and statistics, instead of the entire data processing methodology as Data Science does.
• Data: With Data Science, input data is to be leveraged or analyzed by humans, while the input data for Machine Learning is specifically transformed for algorithm use.
• Skills: Machine Learning specialists oftentimes have backgrounds in computer science, mathematics, statistics, physics, and more, while Data Science experts come from varied technical backgrounds that apply statistics and Machine Learning techniques to address any issue.
• Hardware: Data Science requires horizontally scalable systems to handle Big Data. In Machine Learning, GPUs are the preferred choice for intensive operations.
• Components: Since Data Science deals with the entire data processing methodology, it requires components to cover the collection and profiling of data, distributed computing, automated intelligence, dashboards, business intelligence, data engineering, deployment, and more. In Machine Learning, the components cover problem understanding, data exploration, data preparation, model selection, performance measure, and more.
• Methodologies: Machine Learning follows a more research-based methodology while Data Science is more similar to an engineering development methodology.
• Programming languages: Data Science uses SQL and SQL-like languages such as HiveQL and Spark SQL. Machine Learning primarily uses Python and R. 

Conclusion

Data Science and Machine Learning are bringing numerous industries into a more tech-savvy era. Companies are beginning to tap into the prospect of employing these two fields for Big Data and delivering value to consumers. These two fields are architecting the digital transformation by bringing value to almost every industry and organization that embarks upon this data-driven journey.

In Data Science vs Machine Learning battle both are as valuable as the insights and outcomes they deliver by processing data at all levels. Insights must be put into action to gain genuine value out of these two science fields.

The real value of both fields is that they are both targeted to extract value from information and insights from data. They play nice with each other and we expect to see a deeper collaboration between the two in today’s data-driven, tech-savvy world.

Every field has a wealth of data and questions to solve, which is why regardless of your industry, it is certain that you can benefit from using Data Science and Machine Learning, one way or another.

Why Svitla?

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