Data Science

by | Jun 29, 2022 | Blogs | 0 comments

Data Science?

What is Data Science?

Data science is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data. Data science practitioners apply machine learning algorithms to numbers, text, images, video, audio, and more to produce artificial intelligence (AI) systems to perform tasks that ordinarily require human intelligence. In turn, these systems generate insights which analysts and business users can translate into tangible business value. Data science combines the scientific method, math and statistics, specialized programming, advanced analytics, AI, and even storytelling to uncover and explain the business insights buried in data.

Why Data Science is Important?

More and more companies are coming to realize the importance of data science, AI, and machine learning. Regardless of industry or size, organizations that wish to remain competitive in the age of big data need to efficiently develop and implement data science capabilities or risk being left behind.

 

Types of Data Science

The benefits of a data science platform

A data science platform reduces redundancy and drives innovation by enabling teams to share code, results, and reports. It removes bottlenecks in the flow of work by simplifying management and incorporating best practices.

In general, the best data science platforms aim to:

  • Make data scientists more productive by helping them accelerate and deliver models faster, and with less error
  • Make it easier for data scientists to work with large volumes and varieties of data
  • Deliver trusted, enterprise-grade artificial intelligence that’s bias-free, auditable, and reproducible
  • Data science platforms are built for collaboration by a range of users including expert data scientists, citizen data scientists, data engineers, and machine learning engineers or specialists. For example, a data science platform might allow data scientists to deploy models as APIs, making it easy to integrate them into different applications. Data scientists can access tools, data, and infrastructure without having to wait for IT.
  • The demand for data science platforms has exploded in the market. In fact, the platform market is expected to grow at a compounded annual rate of more than 39 percent over the next few years and is projected to reach US$385 billion by 2025.
  • What do we do with all of this data? How do we make it useful to us? What are its real-world applications? These questions are the domain of data science.       
  • Every company will say they’re doing a form of data science, but what exactly does that mean? The field is growing so rapidly, and revolutionizing so many industries, it’s difficult to fence in its capabilities with a formal definition, but generally data science is devoted to the extraction of clean information from raw data for the formulation of actionable insights.

The data science lifecycle—also called the data science pipeline—includes anywhere from five to sixteen (depending on whom you ask) overlapping, continuing processes. The processes common to just about everyone’s definition of the lifecycle include the following:

  • Capture: This is the gathering of raw structured and unstructured data from all relevant sources via just about any method—from manual entry and web scraping to capturing data from systems and devices in real time.
  • Prepare and maintain: This involves putting the raw data into a consistent format for analytics or machine learning or deep learning models. This can include everything from cleansing, reduplicating, and reformatting the data, to using ETL (extract, transform, and load) or other data integration technologies to combine the data into a data warehouse, Data Lake, or other unified store for analysis.
  • Preprocess or process: Here, data scientists examine biases, patterns, ranges, and distributions of values within the data to determine the data’s suitability for use with predictive analytics, machine learning, and/or deep learning algorithms (or other analytical methods).
  • Analyze: This is where the discovery happens—where data scientists perform statistical analysis, predictive analytics, regression, machine learning and deep learning algorithms, and more to extract insights from the prepared data.
  • Communicate: Finally, the insights are presented as reports, charts, and other data visualizations that make the insights—and their impact on the business—easier for decision-makers to understand. A data science programming language such as R or Python (see below) includes components for generating visualizations; alternatively, data scientists can use dedicated visualization tools.

 

How Does Data Science Work?

Data science involves a plethora of disciplines and expertise areas to produce a holistic, thorough and refined look into raw data. Data scientists must be skilled in everything from data engineering, math, statistics, advanced computing and visualizations to be able to effectively sift through muddled masses of information and communicate only the most vital bits that will help drive innovation and efficiency.

Data scientists also rely heavily on artificial intelligence, especially its subfields of machine learning and deep learning, to create models and make predictions using algorithms and other techniques. 

  • An Introduction to Machine Learning for Beginners 
  • Dep learning Python
  • A Tour of the Top 10 Algorithms for Machine Learning Newbies

 

Data science tools

Data scientists must be able to build and run code in order to create models. The most popular programming languages among data scientists are open source tools that include or support pre-built statistical, machine learning and graphics capabilities. These languages include:

  • R: An open source programming language and environment for developing statistical computing and graphics, R is the most popular programming language among data scientists. R provides a broad variety of libraries and tools for cleansing and prepping data, creating visualizations, and training and evaluating machine learning and deep learning algorithms. It’s also widely used among data science scholars and researchers.
  • Python: Python is a general-purpose, object-oriented, high-level programming language that emphasizes code readability through its distinctive generous use of white space. Several Python libraries support data science tasks, including Numpy for handling large dimensional arrays, Pandas for data manipulation and analysis, and Matplotlib for building data visualizations.

This Data Science with Python program provides learners with a complete understanding of data analytics tools & techniques. Getting started with Python can help you gain knowledge on data analysis, visualization, NumPy, SciPy, web scraping, and natural language processing.

Where Do You Fit in Data Science?

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.

Data Science Career Outlook and Salary Opportunities

Data science professionals are rewarded for their highly technical skill set with competitive salaries and great job opportunities at big and small companies in most industries. With almost 6,000 open positions listed on Glassdoor, data science professionals with the appropriate experience and education have the opportunity to make their mark in some of the most forward-thinking companies in the world.

Below are the average base salaries for the following positions:

Data analyst: Rs. 9, 21,957  

Data scientist: Rs.8, 761,597

Senior data scientist: RS.1, 063,318.75

Data engineer:  Rs.8, 56,643

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Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from noisy, struc,tured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains.

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