Data Science – Today, data science is integral to various industries. This is because the large amount of data generated is a hot topic of discussion and debate in IT circles. Over the years, the popularity of data science has grown, and many companies have started to create specific data science techniques to grow their business.
Sinaumed’s, in this sinaumedia article, we will learn the basics of data science. Let’s also know what to do to become a data scientist in the future!
Definition of Data Science
Data science, aka data science, is a focus of study that deals with large volumes of data using modern techniques to find invisible patterns, gain meaningful information, and make business decisions with that information.
Complex machine learning algorithms are used in data science to build predictive models. Meanwhile, the data used for analysis can come from various sources and appear in multiple formats.
Data science includes a topic of discussion that is broad and with intense subjectivity. By definition, data science is not a science that stands alone. It combines various fields, especially mathematics, computer science, business strategy, and statistics.
Three components are involved in data science: organizing, packaging, and delivering data or The OPD of Data. Collecting data is the process of storing data and data management.
Meanwhile, data packaging is manipulating and merging various raw data that will be presented later. Furthermore, delivering data ensures that messages in data have been accessed by those who need them.
In 2011, research resulted in a prediction that the world would produce more data in 2020. Now, what does Sinaumed’s think?
With this drastic increase in data flow, new tools that can be used to use raw data properly will emerge. The scope of data science is tools, techniques, and technology that will help us deal with the increasing flow of data.
It is an interdisciplinary blend of data derivation, algorithm development, and technology to solve complex analytical problems.
Do you understand what data science is? Next, we also need to understand the general tools used in data science. They are none other than Big Data, Machine Learning, Data Mining, Deep Learning, and Artificial Intelligence.
The following is a discussion of each:
1. Big Data
Big Data is the first tool we have to cover. A new data scientist can help predict products that will be sold, anticipate when and why customers change operators, understand how good their customers are driving, unit deployment, and so on for product companies, telecommunications, and car insurance, only if Big Data is used.
2. Machine Learning
Machine Learning is the second tool. This tool is interdisciplinary and uses statistics, computer science, and Artificial Intelligence techniques. The main component of Machine Learning is an algorithm that can automatically learn from experience to improve its performance. In various fields, the algorithm itself is used.
3. Data Mining
Data Mining, as the following tool discussed, is the application of a unique algorithm to extract patterns from a data set. Data Mining is closely related to Machine Learning in removing informative dents stored in data sets.
4. Deep Learning
Next, there are Deep Learning tools. Not just “study deep or seriously,” as we can do when we are about to take an exam; this new term often used and discussed refers to applying Deep Neural Network technology, a neural network architecture, and hidden layers to solve problems.
5. Artificial Intelligence
The last data science tool is Artificial Intelligence. The term commonly referred to as AI is a field of computer science that emphasizes the creation of intelligent machines that can react and work like humans. Meanwhile, AI has a core component in computer programming for specific characteristics, such as reasoning, knowledge, perception, problem-solving, learning, planning, etc.
Data Science Life Cycle
Now that we’ve finally identified the tools commonly used in data science, let’s focus on the life cycle of data science itself. This cycle consists of five stages, each with its own task.
Data Acquisition, Data Entry, Signal Receiving, and Data Extraction. This stage involves collecting raw data, both structured and unstructured.
Data Warehousing, Cleaning, Data Staging, Data Processing and Architecture. This stage involves taking the raw data and putting it in a form we can use.
Data Mining, Clustering/Classification, Data Modeling, and Data Summarization. The data scientist will take the prepared data and examine patterns, ranges, and generally to determine the usefulness of the data in predictive analysis.
Exploratory/Confirmatory, Predictive Analysis, Regression, Text Mining and Qualitative Analysis. This is the essence of the data science life cycle. This stage involves applying various analyses to the existing data.
Data Reporting, Data Visualization, Business Intelligence, and Decision Making. In this step, the analyst will prepare the analysis in an easy-to-read form. Examples include charts, graphs, and reports.
Requirements in Data Science
In this sub-chapter, we will study some technical terms or concepts that must be known before learning data science. Let’s see!
1. Machine Learning
As with Machine Learning in data science tools, machine learning is the backbone of a data scientist. The reason is that he must have a strong understanding of this field and basic knowledge of statistics.
We can make calculations and predictions quickly and precisely based on what we already know about data, thanks to mathematical models. Modeling itself is also part of Machine Learning and involves identifying the most suitable algorithm to solve the existing problem and training the model itself.
The essence of data science is statistics. It will help us to extract more knowledge and get more meaningful results with robust statistics.
Some level of programming is required to run a successful data science project. Generally, the programming language is Python, and it is trendy because it is easy to learn and supports a lot of data science and machine learning literature.
It is necessary to understand how databases work, how management functions, and how to extract data from databases to become a capable data scientist.
Example of Application of Data Science
We need to know more about data science, especially after understanding the tools, cycles, and various requirements to learn it. Multiple fields, such as social, journalism, finance, and others, use or apply data science. Examples of its application can also be seen in the Natural Language and Machine Learning processing in news articles to identify zoning reforms.
The Metropolitan Urban Community and Housing Policy Center also has data scientists who want to estimate the impact of zoning reforms on the housing supply in the United States (US) metropolitan area. In this case, the data scientist uses data from around 2000 local news sources to identify local reforms because it is impossible for them to get historical data from thousands of municipalities in the metro area.
They can also flag articles that mention major reforms and add relevant metadata similar to whether the report says parking, height limits, or other characteristics with the application of Natural Language and Machine Learning processing.
Data scientists also collect data by type using this method. Copying court records to inform criminal background check policies is another example of a data science application. Data scientists from the Center for Judicial Policy wanted to estimate the number of people with likely criminal records in Washington, DC.
They also worked with the Researcher team to collect data through online searches of the Washington Superior Court. The data scientist then uses the data to create statistics on the criminal background of the community in the area.
What Do Data Scientists Do?
We already know what data science is and must wonder what this job role looks like. here’s the answer. A data scientist analyzes business data to extract meaningful insights. In other words, a data scientist solves a business problem through a series of steps, including:
- Before tackling data collection and analysis, they define the problem by asking the right questions and gaining insight.
- They then determine the correct set of variables and data set.
- They collect structured and unstructured data from many different sources, such as company and public data.
- Once the data is collected, they process the raw data and convert it into a format suitable for analysis. This involves cleaning and validating data to ensure uniformity, completeness, and accuracy.
- After the data is rendered into a usable form, it is fed into an analytical system: Machine Learning algorithms or statistical models. This is where data scientists analyze and identify patterns and trends.
- When the data has been fully rendered, they interpret it to find opportunities and solutions.
- They complete tasks by preparing results and insights to share with appropriate stakeholders and communicating them.
Data Science Oversight
Who oversees the data science process?
1. Business Manager
The business manager is the person in charge of overseeing data science training methods. Their primary responsibility is collaborating with the data science team to characterize problems and define analytical methods.
A data scientist can oversee a marketing, finance, or sales department and report to an executive in charge. They aim to ensure projects are completed on time by collaborating closely with data scientists and IT managers.
2. IT Manager
Next, the IT manager. If members have been in the organization for long, responsibilities will be more critical than others. They are primarily responsible for developing the infrastructure and architecture to enable data science activities.
Data science teams are continuously monitored and appropriately resourced to ensure they operate efficiently and securely. They may also be responsible for creating and maintaining the IT environment for the data science team.
3. Data Science Manager
The data science manager is the last part. They primarily track and oversee the work procedures of all team members. They also manage and track the day-to-day activities of the three data science teams. They are team builders who can combine project planning and monitoring with team growth.
The Importance of Learning Data Science
Data is essential for any industry to make decisions; moreover, in the all-digital era, alias all-technology is like now. Even so, an analytical process is needed, such as collecting, tidying up, and analyzing it to make the data helpful information.
Of course, this process is not easy because a large and growing amount of data must be collected and analyzed. That is the reason for the growing demand for jobs in data science, especially by start-up companies. Don’t be surprised if the government also needs data science to regulate accurate policies.
Learning data science is also a high-value investment to deal with trends in this digital era. We will be able to analyze data anywhere and have good problem-solving skills and skills in other fields of science with our data science competencies.
For example, a profession engaged in the data sector must continually adapt to the constantly changing trends of data scientists. By studying it, we are expected to be able to keep up with the current changes.
If Sinaumed is interested in pursuing a career in this field, such as becoming a data analyst, data scientist, or data engineer, several things must be prepared to become a reliable data expert. One of them is programming, which is the main thing in managing data.
In addition, we are also required to be able to use specific software, such as Microsoft Excel, SPSS, and so on, to perform data analysis. Not only using software, Sinaumed must also understand programming languages such as R and Python, which will be a plus if Sinaumed wants to have a career in this field.
Because data science is closely related to data (as mentioned above), understanding statistics and mathematics must also be well mastered. This is because the results of successfully processed data will be presented to other parties, not all whom understand data science terms.
For this reason, these skills will be beneficial in this regard, including good communication and visualization so that ordinary people can more easily understand our presentations. Conclusion
How does it feel to learn the basics needed in data science? Of course, we get new insight into one of the popular professions in this era and understand that this field is not easy and cannot be underestimated.
Even so, no single profession deserves to be looked down upon because they have their place in society and play a role in their respective fields.
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