Data Science Demystified: What It Is and Why It Matters

Data science is a rapidly growing field that has become increasingly important in today’s data-driven world.

It involves using a combination of mathematical and statistical techniques, specialized programming, advanced analytics, artificial intelligence (AI), and machine learning to extract insights from data.

These insights can be used to inform decision-making and strategic planning, making data scientists valuable assets to organizations.

Data scientists are in high demand, and their role has been recognized as one of the “sexiest jobs of the 21st century” by Harvard Business Review.

The increasing volume of data sources and data has made data science one of the fastest-growing fields across various industries.

What is Data Science?

Data science is a field that combines various techniques such as mathematics, statistics, specialized programming, advanced analytics, AI, and machine learning with domain knowledge to extract insights from an organization’s data. These insights can then be used to make informed decisions and devise strategic plans.

The exponential growth in the volume of data sources and data has made data science one of the fastest-growing fields across various industries.

This has led to the role of a data scientist being recognized as the “sexiest job of the 21st century” by Harvard Business Review.

As a result, organizations are heavily dependent on data scientists to understand their data and provide recommendations to improve their business outcomes.

Data Science Cycle

The data science cycle is an iterative process that consists of several steps, each building upon the previous one.

This cycle is ongoing and recursive, as the insights from the analysis may lead to the need for additional data ingestion and processing, leading to further analysis and communication.

1. Data Ingestion

Data Ingestion is the first step in the data science cycle. This step involves collecting and acquiring data from various sources.

This data can come in many forms, such as structured or unstructured, and can be collected from internal or external sources.

2. Data Storage and Data Processing

Data Storage and Data Processing is the next step in the data science cycle. In this step, the data that was collected in the previous step is stored and prepared for analysis.

This includes cleaning, formatting, and transforming the data to make it usable.

3. Data Analysis

Data Analysis is the third step in the data science cycle. In this step, various techniques such as statistical analysis, machine learning, and data visualization are used to extract insights from the data.

The data scientist uses their expertise and knowledge to uncover patterns and trends in the data that can be used to inform decision making.

4. Communicate

Communicate is the final step in the data science cycle. This step involves communicating the insights and findings from the data analysis to relevant stakeholders.

This can be done through reports, presentations, or other visualizations. The goal of this step is to ensure that the insights and recommendations from the data analysis are effectively communicated and understood by those who will use them to make decisions.

What Does Data Science Do?

Over the past decade, data scientists have become an essential part of almost every organization.

These professionals are a unique blend of technical expertise and business acumen, who are adept at building complex algorithms to process and analyze large amounts of data, and use it to inform decision-making and strategy.

They possess the technical skills to extract insights from data, and the ability to communicate those insights to stakeholders across the organization.

Data scientists are curious individuals who are driven by results. They possess a wealth of industry-specific knowledge and strong communication skills, which enable them to explain highly technical results to non-technical audiences.

They have a solid background in statistics and linear algebra, and are well-versed in programming languages and technologies related to data warehousing, mining, and modeling to build and analyze algorithms.

Why Become A Data Science

Demand increased 28% 2026

5.971 Number of job openings

Average base salary: $127.500

#2 Best job in America 2021

Data Science Role

Data is everywhere and expansive. A variety of terms related to mining, cleaning, analyzing, and interpreting data are often used interchangeably, but they can actually involve different skill sets and complexity of data.

1. Data Scientist

Data scientist has consistently been ranked among the top three jobs in America by Glassdoor since 2016.

As data becomes more readily available, the demand for data science professionals is not limited to just large tech companies but also across industries, both big and small.

However, the high demand for data scientists is met with a shortage of qualified candidates to fill open positions. The need for data scientists is expected to continue to grow in the future.

According to LinkedIn, data scientist is one of the most promising jobs in 2021, and companies are increasingly seeking individuals with data-science-related skills.

The statistics below demonstrate the significant and growing demand for data scientists.

Skills needed: Programming skills (SAS, R, Python), statistical and mathematical skills, storytelling and data visualization, Hadoop, SQL, machine learning

2. Data Analyst

Data analysts serve as a link between data scientists and business analysts.

They are given the questions that need to be answered by an organization and use data to find answers that align with the organization’s high-level business strategy.

They are responsible for interpreting technical analysis and converting it into actionable items and effectively communicating their findings to a wide range of stakeholders.

Skills needed: Programming skills (SAS, R, Python), statistical and mathematical skills, data wrangling, data visualization

3. Data Engineer

Data engineers handle vast quantities of rapidly changing data. Their main focus is on the design, implementation, upkeep and improvement of data pipelines and infrastructure to move and transform data for use by data scientists.

Programming languages (Java, Scala), NoSQL databases (MongoDB, Cassandra DB), frameworks (Apache Hadoop)

Data Science Salaries

Data science is a highly in-demand field and salaries for data scientists can vary depending on a number of factors such as location, experience, and industry.

According to a survey conducted by Glassdoor, the average salary for a data scientist in the United States is around $117,345 per year.

However, this number can vary widely depending on the location, with data scientists in San Francisco earning an average of $144,000 per year, while those in Houston earn an average of $104,000 per year.

Additionally, the industry plays a role as well, with data scientists in the technology industry earning an average of $120,000 per year, while those in healthcare earn an average of $104,000 per year.

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Experience is also a significant factor that affects data science salaries. Entry-level data scientists can expect to earn around $85,000 per year, while those with more experience can earn upwards of $150,000 per year.

It is worth noting that the data science field is constantly evolving, and the salary range can vary depending on the source and the time of the data collection.

It is also worth to consider that other factors such as benefits, bonuses, and stock options can also affect the total compensation of a data scientist.

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