What is data science?

To find insights concealed in an organization's data, data science integrates specialized programming, advanced analytics, artificial intelligence (AI), machine learning, and math and statistics with subject expertise. Strategic planning and decision-making can be aided by these insights.

Data science is one of the fields with the quickest growth rates across all industries due to the increasing volume of data sources and data itself. Thus, it should come as no surprise that Harvard Business Review (link located outside of IBM) named the position of data scientist the "sexiest job of the 21st century." Businesses are depending more and more on them to analyze data and make practical suggestions to enhance business results.

Analysts can gain practical insights from the data science lifecycle, which includes a variety of roles, tools, and processes. According to Factored AI, a data science project often goes through the following phases:

• Data ingestion

The lifecycle starts with the gathering of data, both unstructured and structured, from all pertinent sources through a range of techniques. These techniques can involve real-time data streaming from systems and devices, online scraping, and human entry. Along with unstructured data like log files, video, music, images, the Internet of Things (IoT), social media, and more, data sources might include structured data like customer data.

• Data processing and storage

Depending on the kind of data that needs to be recorded, businesses must take into account various storage systems because data can have a variety of formats and structures. Teams responsible for data management aid in establishing guidelines for data organization and storage, which makes it easier to work with analytics, machine learning, and deep learning models. This phase involves employing ETL (extract, transform, load) jobs or other data integration tools to clean, deduplicate, transform, and combine the data. Before loading into a data warehouse, data lake or other repository, this data preparation is crucial to improve data quality.

• Data analysis

To investigate biases, trends, ranges, and distributions of values within the data, data scientists perform an exploratory data analysis. The creation of hypotheses for a/b testing is driven by this data analytics exploration. Additionally, it enables analysts to assess the suitability of the data for modeling purposes related to deep learning, machine learning, and/or predictive analytics. Many organizations generally rely on this knowledge when making corporate decisions in order to achieve greater scalability, depending on the model.

• Communicate

Insights are finally made easier to understand for business analysts and other decision-makers through reports and other data visualizations that highlight the insights and their implications for the company. The components for creating visuals are built into data science programming languages like R or Python.

Data scientist versus Data science

Data scientists are data science professionals. Not all steps in the data science lifecycle are the direct purview of data scientists. For example, data engineers are often responsible for data pipelines, but data scientists can provide advice on the type of data that is needed or useful. Although machine learning models can be created by data scientists, more software engineering expertise is needed to scale these efforts and make the programs run faster. To scale machine learning models, it is common for a data scientist to work in collaboration with machine learning developers.

The duties of a data scientist and a data analyst frequently overlap, especially when it comes to exploratory data analysis and data visualization. A data scientist's skill set, however, is usually more extensive than that of a conventional data analyst. In contrast, data scientists use popular programming languages like R and Python to perform greater data visualization and statistical inference.

Data scientists need specialized computer science and pure science abilities that go beyond those of a standard business analyst or data analyst to do these jobs. The data scientist also needs to be knowledgeable about the particulars of the industry, such as eCommerce, healthcare, or the manufacture of automobiles.

A data scientist needs to be able to, in summary:

• Have adequate knowledge about the company to be able to identify business pain issues and ask important inquiries.

• Apply commercial acumen, computer science, and statistics to data analysis.

• Utilize a variety of tools and methods to prepare and extract data, such as databases, SQL, data mining, and data integration techniques.

• Predictive analytics and artificial intelligence (AI), such as deep learning, natural language processing, and machine learning models, can be used to extract insights from large data sets.

• Tell tales that eloquently explain the significance of findings to stakeholders and decision-makers across all technical knowledge levels.

• It looks for ways in which the results can be applied in order to solve the company's issues.

• Work along with other members of the data science team, such as the IT architects, data engineers, and application developers.

Due to the great demand for these abilities, many people who are just starting out in the data science field look into a range of data science programs, including degree programs, certification programs, and courses given by educational institutions.