What is Data Science?

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What is Data Science?

Ramya Shankar
Last updated on December 11, 2022

    The world is going gaga over data scientist jobs, dubbed as the hottest job of the 21st century. Data science is a growing field, and it doesn’t take a great deal of sleuthing to find analysts breathlessly prognosticating that over the next ten years, we’ll be needing millions of data scientists. But what is it, and why is it important? Let's understand.

    What is Data Science?

    It is a multi-disciplinary field that lies at the intersection of the:

    • Hacking skills,
    • Mathematics and statistics, and
    • Substantive expertise.

    It is just not the concept of unifying statistics, data analysis, and their related methods but also comprises their results. The field intends to analyze and understand actual phenomena with the help of data. Data science aims to reveal the features or the hidden structure of complex and natural human and social phenomena using a different point of view from the established or traditional theories and methods. These points of view imply multidimensional, dynamic, and flexible ways of thinking.

    Data Science Life Cycle

    A data science project comprises the following steps: life_cycle_data-science

    1. Capture

    The first step in the data science lifecycle involves acquiring data based on the question(s) to be answered. The project begins with identifying various data sources like logs from web servers, social media data, data from online repositories or excel, and so on. Data acquisition involves acquiring data from all the identified internal and external sources that can help answer the business question(s). It is important to track where the data slice comes from and whether it is up-to-date or not. It is recommended to track this information during the entire lifecycle of a data science project, as data might need to be re-acquired to test other hypotheses.

    2. Maintain

    This step involves data wrangling. The data scientists are required to clean and reformat the data by manually editing it in the spreadsheet or by writing code. Through regular data cleaning, data scientists can easily identify the following:

    • Foibles that exist in the data acquisition process,
    • Assumptions they could make, and
    • Models they could apply to produce results.

    Data, once reformatted, can be converted and uploaded into one of the data science tools.

    3. Process

    This step defines the core activity of the project. It requires writing, running, and refining the programs to analyze and derive meaningful business insights from the data. Such programs are often written in Python, R, MATLAB, or Perl. Enormous different machine learning techniques are applied to the data to identify the ML model that best fits the business requirements. All the models are trained using the training data sets.

    4. Analyze

    After we get the set of process-ready data, we need to analyze it. It involves analysis of the random subset of data. This is a brainstorming stage for data analysts as this is where the patterns in the data are observed, and useful insights are retrieved. There could be a possibility that the dataset could be missing values or there is some unnecessary (dispensable) data. Such inconsistencies are identified and removed in this stage.

    5. Communication

    The goal of this stage is to deploy the models into production for the final user acceptance. The performance of the models must be validated by the user, and if there exist any issues with the model, then it must be fixed in this stage. A data science project is an iterative process. Steps are iteratively repeated as data is acquired continuously, and understanding becomes much easier.

    The Data Science Process

    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 the complexity of data. Such is the complexity and scope of data science huge.

    Data Scientist

    Data scientists examine the questions needing answers and find related data. They have good business acumen and analytical skills, as well as the ability to mine, clean, and present data.

    Skills Required:

    • Programming Skills (SAS, R, Python)
    • Statistical and mathematical skills
    • Storytelling
    • Problem-solving
    • Data visualization
    • Hadoop and SQL
    • Machine learning

    Data Scientist Skills

    Data Analyst

    Data analysts bridge the gap between data scientists and business analysts. They are provided with the questions that need answering from an organization and so they organize and analyze data to find results that align with high-level business strategies. They are responsible for translating technical analysis to qualitative action items and effectively communicating their findings to diverse stakeholders.

    Skills Required:

    • Programming Skills (SAS, R, Python)
    • Data wrangling
    • Data visualization
    • Statistical and mathematical skills

    Data Analyst Skills

    Data Engineer

    Data engineers manage exponential amounts of rapidly changing data. They focus on the development, deployment, management, and optimization of data pipelines and infrastructure to transform and transfer data to data scientists for querying.

    Skills Required:

    • Programming languages (Java or Scala )
    • NoSQL databases (MongoDB)
    • Frameworks (Apache Hadoop)

    Why do We Need Data Science in Day-to-day Life?

    Data science or data-driven science enables better decisions, predictive analysis , and pattern discovery. It associates various areas of work in statistics and calculation to translate data with an end goal of decision-making. Why do we need Data Science in Real Life?

    1. E-Commerce Price Comparison Websites

    These websites are fueled with data that is fetched using APIs and RSS feeds. Such websites offer a single platform to check the prices of something across many online shopping websites, allowing users to select a product by comparing prices, features, etc., from numerous sellers in a single place. PriceRunner, Junglee, and Shopzilla are a few examples of such sites.

    2. Internet Search

    All search engines, including Google, Bing, Baidu, Yahoo, and DuckDuckGo , use data science algorithms to provide accurate outcomes for a search query in just a few seconds.

    3. Digital Marketing

    Although internet surfing is one of the most significant applications of data science and machine learning, the entire digital advertising sector is also another one of its remarkable applications. Data science algorithms find use in displaying banners on different websites. For example, if we search for a data science course, we will start getting recommendations and ads on other websites as well as on our Instagram, Facebook, and other social media accounts.

    4. Image and Speech Recognition

    Image and speech recognition are another two areas that extensively use data science. For example, when we upload a photo on Facebook, we start getting tag suggestions, which is possible due to the image recognition feature that Facebook offers. We use speech recognition in our daily lives as well by using our smartphones featuring smart assistants like Siri and Google. Following the trend, voice-supported speakers and TVs are also being introduced in the market.

    5. Healthcare

    One industry that has greatly benefitted from the advancement of data science is healthcare, where it is used for detecting tumors, artery stenosis, and more. Healthcare employs various methods and frameworks like MapReduce to find ideal parameters for complex medical tasks such as lung texture sorting.

    6. Airline Route Planning

    With data science, an airline can optimize its operations in many ways and thus offer a hassle-free experience to its customers. Presently, it can:

    • Plan the routes, thus predicting if connecting flights will be needed or direct flights could be scheduled.
    • Predict if any delays are possible.
    • Offers promotional offers by observing the booking patterns of customers.
    • Decide which class of planes to purchase depending on the demand.

    7. Logistics

    Companies like DHL, FedEx, and UPS use data science algorithms to enhance their operational efficiency. With optimized algorithms, these companies have discovered the best possible ways to ship, the most appropriate time to deliver, and the best method of transport to pick, subsequently leading to cost-effectiveness, and so on.

    8. Gaming

    Gaming went to the next level with data science. Video games developed these days using machine learning are designed in such a way that they upgrade themselves. EA Sports, Sony, and Zynga are among the few giants who have taken the gaming experience altogether to a new level with the use of data science.

    How does Data Science Differ from Business Intelligence?

    Business intelligence (BI) also comprises both strategies and technologies used for the analysis of business data or information. It can also provide historical, current, and predictive views of business insights. But still, there are some key differences. Unlike data science, which uses both structured and unstructured data, business intelligence uses only structured data. Also, BI is analytical in nature. This means that it provides historical reports of data. In contrast, data science is scientific in nature as it performs in-depth statistical analysis on the datasets.

    Further, it leverages more sophisticated statistical and predictive analysis and machine learning, whereas business intelligence uses basic statistics with an emphasis on visualization. Lastly, business intelligence compares historical data and current data to identify trends. Contrary to this, data science combines historical and current data to predict future performance, possibilities, and outcomes.

    Data Wrangling: Why is it Important in Data Science Life Cycle?

    Often termed data munging, data wrangling is a crucial part of data analysis. By dropping null values and filtering and selecting the right data, data scientists can ensure that any algorithm applied to the refined data is fully accurate and effective. Therefore, data wrangling is the process that involves converting and mapping raw or unstructured data to appropriate data sets, making it valuable for the scientist to run algorithms on it and, thus, produce actionable insights. Data wrangling involves the following 6 core activities:

    1. Discovering: To discover what the data consists of and figure out what could be the best approach for productive analytical exploration.
    2. Structuring: We need this stage because data comes in all sizes and shapes.
    3. Cleaning: This step requires eliminating the data that might otherwise distort the analysis. For example, removing null values from the data sets.
    4. Enriching: This step further enhances and refines the data.
    5. Validating: It takes care of data quality and consistency issues and also verifies that the latter has been addressed properly by applied (data) transformations.
    6. Publishing: It is the final stage of data wrangling, and it is responsible for planning and delivering refined data for the projects.

    Data Analytics: An Overview and How is it Different from Data Science?

    Data analytics is a technique to examine data sets and generate insights by connecting and observing patterns in trends to achieve organizational targets. Unlike data science, it involves dealing with historical data in context and less in AI, machine learning, and predictive modeling. Data analysts usually wrangle data that is either localized or smaller in size. The following table presents the various differences between the two closely related fields:

    Data Science Data Analytics
    Goal Asking business questions and planning strategy. Analyzing and mining business data.
    Size of data Big set of data (big data). A limited set of data.
    Involves Data preparation, cleansing, and analysis to gain insights. Data querying and aggregation to find trends.
    Focus Pre-processed data. Processed data.
    Purpose Finding insights from raw data. Finding insights from processed data.
    Data Types Structured and unstructured. Structured.
    Source Data scientist explores and examines data from multiple disconnected sources. Data analysts usually look at the data from a single source.
    AI and ML More involvement. Less involvement.
    Predictive Analysis Yields more predictable insights. Yields less predictable insights.


    Data science is a lucrative field that is empowering modern businesses to reach new levels of success and possibilities while providing solutions for the many usual challenges. The importance of the same is directly related to the size of data, which is continuously on the rise. Thus, the importance of data science is also expected to rise, creating new working opportunities for professionals in this field.

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    Data science is primarily used to study data in four ways: 1. Descriptive analysis 2. Diagnostic analysis 3. Predictive analysis 4. Prescriptive analysis.

    O: Obtain data S: Scrub data E: Explore data M: Model data N: Interpret results

    The different data science techniques are classification, regression, and clustering.

    Data scientists are professionals who are in charge of collecting, analyzing, and interpreting data that help organizations to make informed decisions.

    As a data scientist, you need to have a good grasp of statistical analysis and computing, machine learning, deep learning, data visualization, data wrangling, mathematics, and programming.

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