Data science is a "concept to unify statistics, data analysis, machine learning, domain knowledge and their related methods" in order to "understand and analyze actual phenomena" with data. It uses techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, domain knowledge and information science. Machine learning is a creating tool for extracting knowledge from data. In ML models can be trained on data independently or in stages: training with a teacher that is, having human-prepared data or training without a teacher, working with spontaneous, noisy data. Big Data work with huge amounts of often unstructured data. The specifics of the sphere are tools and systems capable of withstanding high loads. Data Science is the addition of meaning to arrays of data, visualization, collection of insights, and making decisions based on these data. The field specialists use some methods of machine learning and Big Data cloud computing, tools for creating a virtual development environment and much more.
Computer science gives us an edge in understanding and working hands-on with aspects of BIG Data. Big data works mainly on important concepts like map-reduce, master-slave concepts etc.
Data extraction involves heavy usage of SQL in data sciences. SQL is one of a primary skill in data sciences. For data analysis, knowledge of one of the programming language (R or Python mostly) is elementary.
Computer scientists invented the name machine learning, and it’s part of computer science, so in that sense, it’s 100% computer science. Furthermore, computer scientists view machine learning as “algorithms for making good predictions.”
Visualizations are an important aspect of data science. Although Data science has multiple tools available for visualization, complex representation requires that extra coding effort.
Python seems to be the most widely used programming language for data scientists today. This language allows the integration of SQL, TensorFlow, and many other useful functions and libraries for data science and machine learning.
The scientific method includes the collection of empirical evidence, subject to specific principles of reasoning.
Despite the idea that Big Data will kill the need for theory and the scientific method, the human element is necessarily involved in the generation, collection and interpretation of data. The scientific method is a way to help us understand how the world really works. To be of real, long-term value to business, analytics needs to be about understanding the causal links among the variables. Through trial and error, the scientific method helps shed light on identifying the reasons why variables are related to each other and the underlying processes that drive the observed relationships.
Data wrangling skills are so integral to the job, many leading tech companies typically ask new data science candidates to perform a series of data transformations, including merging, ordering, aggregation, etc., using data science programming languages R, Python, Julia, or even SQL, along with a specific data set. A good data wrangler knows how to integrate information from multiple data sources, solving common transformation problems, and resolve data cleansing and quality issues. A data wrangler also knows their data intimately, and is always looking for ways to enrich the data. Over time, data scientists will develop a code toolbox of commonly used data wrangling tasks so that when the occasion arises, they can just dip into their box of tricks to solve the problem at hand. A lot of my toolbox deals with date handling and imputing missing values. Aside from hand-coding data wrangling solutions, there are a number of products that can kick-start the process without coding.
Data science is the field of interdisciplinary systems in which it observes information from data in several forms. It is also used to modify and to build Artificial Intelligence software in order to obtain the required information from the huge data sets and data clusters. Data-oriented technologies like Hadoop, Python, and SQL are covered by using data science. Data visualization, statistical analysis, distributed architecture are the extensive uses of data science.
Data scientists report percentages and based on the SQL queries they can make line graphs by using simple tools. They can build interactive visualizations, analyze trillion records and develop the techniques of cutting-edge statistics. The main goal of data scientists is to get a better understanding of data.
Data science skill set
Data wrangling: data processing, formatting and transformation
Data visualization and communication