Introduction to Data Analysis and Data Science
The use of data within different sectors has multiple objectives that range from process optimization to protecting financial information, among many others. In corporations, it can help find the right patterns to keep their operations competitive. The above results from applying different disciplines, and today we will talk about two: Data Analysis and Data Science.
In this article, both disciplines will be addressed, so that you have an introductory vision of this topic and thus know how to obtain strategies that improve the performance of operations and support decision-making.
Introduction to Data Analysis and Science
Data analysis and Data science are two fields of Data processing with similar points of contact and techniques, as well as their differential factors. Let’s start with the definitions.
Data analysis: This is one of the methodologies in the field of Data processing, and as its name indicates, it refers to the analysis of a recorded history. The professional profile that carries it out is an analyst through Business intelligence and other tools.
This professional focuses on applying tools to find patterns that help make business decisions.
The analysts help to visualize the existing scenarios and obtain diagnoses or business perspectives, logistics chain and financial systems, among others.
Data science: The science approach covers a broader field since it does not only analyze to find certain insights about what is happening but develops algorithms, models and uses more complex tools.
These activities are carried out by a scientist and compared to the analyst, the scientist approaches problems from a different perspective; to obtain objective knowledge. Data scientist skills are related to development, research, and innovation. New algorithms or models are created that can optimize different activities that a company requires according to its goals.
The objectives pursued by both fields can complement each other but, due to their different scopes, their divergences must be highlighted:
The objective of the analysis is to reach conclusions without necessarily generating any development. Just as it would be to monitor and analyze a series of numbers regarding a purchase or trends, to generate later a report that helps define a sales strategy, for example.
The objective of science is to obtain significant information from a bunch of data, with the use of algorithms, processes and scientific methodologies, in addition to creating models that help to solve problems or improve systems.
Analysts’ and scientists’ skills have also been different because of the goals that each one has.
An analyst has the following skills:
- Basic knowledge of programming methods focused on analysis with tools such as Python or R.
- The analysis uses interactive software for data visualization, such as Tableau or Power BI.
- They use software with a large storage capacity, such as Hadoop, which works by open source.
- Data Analysis can collaborate with investors and managers of a company to show valuable information, such as market trends; thus, decisions makers determine the next step.
Otherwise, the scientist has a profile that meets the following criteria:
- Advanced programming knowledge, using Python, R, Julia and databases. In addition to that, they had a broad command of statics, mathematics, and algorithms.
- Machine Learning is one of the main tools to create models that help obtain information.
- An approach towards developing solutions for the problems encountered, making predictions or else establishing automated processes. They work inside the operation, to say something.
- For example, solid skills in tools such as Data Mining are highly used to obtain much deeper information.
Although both fields can complement each other and have significant differences, Data processing professionals currently have skills that can be combined and result in comprehensive processes.
Methodologies that are applied in Data Analysis and Science
As part of this introduction, it is relevant to talk about the processes or methodologies applied in the analysis or the scientific process. However, setting a standard for their uses would be arbitrary, since each expert performs the most appropriate procedure according to their knowledge and the project needs.
Let us talk about different methodologies for a more significant number of results. To avoid discussing them in detail, since it’s not the point of this introduction, we’ll mention them to have a general idea of their basic operation.
Data Analysis
7 steps solving problems that consist of stages ordered in sequence; definition of problems; construction of a probability tree; description of a storyline; or rather, the ideal solution to the problem; plan development; collect or analyze data from different sources; synthesis and finally, communicate the results.
Problem trees and hypotheses are a method that aims to reduce small problems to solve large ones, which helps to establish priorities and create a much more manageable approach.
Data Science
Data science has also undergone a broad evolution, and its journey, as well as the experience of various experts, have highlighted 3 factors to consider establishing a methodology:
- Models that help organizations obtain better results, something that a single model employed may not obtain.
- Teamwork to carry out this task, since it is logical that, if models with more tasks are needed, more experts are required to collaborate to achieve the objective.
- Agile approaches because a stringent process will not be able to consider unexpected variables or technological conditions.
Once this clarification is made, let’s go with the methodologies:
CRISP-DM is another methodology with 6 stages which are: Business understanding, Data understanding, Data preparation and Modeling, also known as testing of data, that is available for evaluation and deployment. This methodology can be more comprehensive in business information and established a series of clear steps.
Domino is another approach, one that combines CRISP-DM and Agile methodologies. This helps to have little room for collaboration.
There are more methodologies and study frameworks than those mentioned above, which respond to different characteristics. If you need to know which is the best for a given case, it is recommended to go with an expert in Data processing, Analyst or Scientist. In this way, the panorama will be much more straightforward.
Science and Analysis applications
In this introduction to Data Analysis and Science, it’s important to address definitions and methods and now, it’s time to learn about some applications areas.
1. Search Engine Whenever we want to search on the internet, we use a Search Engine, which works through algorithms that carry out a series of steps with the information of each user. This explanation is general, since the operation of the algorithms can be complex and is updated continuously.
2. Finance In recent years the field of finance has constantly been changing since analysis technology contributes to lowering the level of insecurity, recurrence of fraud, or investment risk. Analysis and science have played an important role in detecting risk or possible losses on this last topic; Thus, it impacts companies because it allows them to optimize stock, maximize the lifetime of their business relationships or resources, and make sustainable decisions.
3. Ecommerce The most famous e-commerce companies, such as Amazon, Flipkart and Mercado Libre, have Data analysis to ensure the personalization of the purchase journey and the definition of the target with algorithms. Therefore, everything that involves discovering purchasing patterns, preferences, or trends are closely related to the treatment of each buyer’s information.
4. Industry
Industry is the field that has been able to have a remarkable transformation. It is possible that you have a general knowledge of the well-known Smart Factories, and that the entry of data science into this world has brought with optimizations in different areas.
It begins with promoting intelligent production models based on intelligent prediction technology, machine learning or artificial intelligence, among many other tools. This has resulted in increased productivity and speed.
The role of this discipline within the industry also addresses the reduction of costs or energy invested, creation of better equipment or components, risk assessment, development of specialized software, and avoiding waste or loss among many other advantages with which different companies already work.
Within the industrial sector, we can highlight specific applications such as:
- Predictive or real-time analysis of data on the performance and quality of products or processes. This includes failure forecasting, detecting problems before they occur, and saving resources.
- Price optimization to find the cost-benefit ratio between customer and production.
- Automating industries are one of the first steps to being part of an intelligent production system.
- Supply chain optimization is a complex task due to all the factors involved. However, it is possible to use the appropriate analysis model.
- Product design and development, since it seeks to manufacture according to customer requirements. One more step is the optimization of products that already exist.
There are many more applications in industries and if you want to implement them in a company, you need to identify the objective and degree of depth and determine the ideal procedure. So, how to discover what a company needs?
This introduction to Data Science and Data Analysis aims to give you a glimpse of the possibilities. Still, it’s imperative to remember the complexity that comes with it. The recommendation is the approach of experts, such as the Autmix specialists.
Autmix works hand in hand with clients to achieve maximum performance in each operation with the objective of transforming the information in the strategic asset for their success. Learn more about this Autmix brand, and let’s start with a consulting session.
