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Data Science

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.

What is data science?

Data science is a multidisciplinary method to extracting unlawful insights from the large and ever-increasing volumes of data collected and created by today’s organizations. Data science includes preparing data for analysis and processing, performing advanced data analysis, and presenting the results.

A data scientist must be able to do the following:

• Apply mathematics, statistics, and the scientific method

• Use a wide range of tools and techniques for evaluating and preparing data—everything from SQL to data mining to data integration methods

• Extract insights from data using predictive analytics and artificial intelligence (AI), including machine learning and deep learning models

• Write applications that automate data processing and calculations

• Tell—and illustrate—stories that clearly convey the meaning of results to decision-makers and stakeholders at every level of technical knowledge and understanding

• Explain how these results can be used to solve business problems

The data science maturation

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 development 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 reliable format for analytics or machine knowledge or deep learning models. This can include everything from cleansing, deduplicating, and reformatting the data.

• Pre-process or process: Here, data scientists examine biases, patterns, ranges, and deliveries of values within the data to control the data’s suitability for use with predictive analytics, machine learning, and/or deep learning procedures.

• Analyse: This is where the discovery happens—where data scientists make statistical analysis, projecting analytics, regression, machine learning and deep learning algorithms, and more to extract insights from the prepared data.

Data science tools

• 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.

• Python: Python is a general-purpose, object-oriented, high-level programming language that highlights 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.

Data science algorithms are designed the way to get data in every aspect of data entry. Data science application are much famous or widely known in this era of data analysis. Data science basic can be studied or learn through data science blogs, data science books, data science boo camps offered by different institutions. Data science certifications, diploma, and courses are available in the market to become a good and expert data scientist.

Different data science companies offer different packages to learn data science beginner, intermediate and expert level to polish your professional career.