What is Data Science
We are living in an emerging digital economy, and this economy is undoubtedly driven by data. It is creating a buzz in each and every domain that you can think of. As we are gaining access to a consistent flow of information in the form of unstructured data, the requirement to facilitate the conversion of the same into actionable insights is more prominent than ever before. Data Science can be considered a field of study that amalgamates programming skills, domain expertise, and knowledge of statistics and mathematics in order to extract insights from data that are meaningful. The practitioners of Data Science apply machine learning algorithms to images, video, text, numbers, audio, and more in order to produce AI systems to perform tasks that require human intelligence. Data Science has emerged as one of the fastest-growing fields across each and every industry because of the accelerating volume of subsequently data and data sources. The lifecycle of data science involves tools, processes, and roles that allow analysts to gain actionable insights. More and more companies and businesses are beginning to realize the importance of AI, Machine Learning, and Data Science. The organizations and businesses, irrespective of the
size of their business or the industry they serve, intend to possess an upper hand over their competitors.
Functioning of Data Science Gathering Data The first step is to gather raw data from various sources that provide data analysts with a clear explanation of the business problem. For example, gathering data on the sales that were close. This involves capturing data, data acquisition, signal reception, data entry, and extraction of data.
Data Storage As we all are well aware of the fact that data can be extracted in various formats and structures, it is important for businesses to ponder upon the requirement to consider various storage systems based on the characteristic of data that requires capturing. The teams associated with data management help in setting up standards around data structure and storage, which facilitates workflows around deep learning models, analytics, and machine learning.
Statistical Analysis The further step involves utilizing statistical analysis in order to find out the patterns that were followed by the leads that were closed. The data analytics exploration helps in driving hypothesis generation for a/b testing while allowing analysts to determine the relevance of data for utilizing within modeling efforts for machine learning, predictive analytics, or deep learning.
Communication Insights gathered are then presented as reports and other data visualizations that facilitate the easier impact of insights on business analysts and decision-makers to get through. Data Science programming languages like Python or R involve components for generating visualizations. As an alternative, data scientists are able to use dedicated visualization tools.
Requirements for Data Science Well, there are several pre-requisites that are required to be fulfilled in order to drive data science solutions in a business efficiently, and they are:
Statistics, Linear Algebra, and Probability The knowledge of inferential statistics and descriptive statists is crucial if you are dedicated to building a career in the field of Data Science. With the knowledge and skill of carrying out adequate statistical analysis, you would be able to create several inferences and understand the data at hand.
Programming Knowledge As you already know that statistical analysis and computations are required for Data Science processes. It is necessary for professionals in order to be familiar with programming languages such as R or Python programming. By possessing the knowledge of library support and scripting, you will be able to create machine learning models from scratch with ease.
Big Data and Cloud Cloud comes into the picture when a machine learning model is deployed at scale as it can magnify the outcomes and learnings for any problem that arises within the business. Big Data holds the potential to provide us with a better perspective on how to manage large and complex data for our business problems.
SQL, Excel, and Visualization Tools Most popular visualization tools, such as Tableau, PowerBi, etc., can provide us with a great interactive interface to speak for various data points that can help in performing initial analysis just to understand the data. With the knowledge of Excel and SQL, you will be able to understand the representation of data in a tabular format or data frames that can help in data wrangling or data manipulation.
The Role of a Data Scientist The job of a Data Scientist in an organization will involve exploratory data analysis, data extraction, loading, and transformation, statistical analysis, data manipulation, data modeling, visualization, and gathering actionable insights. Data Analysts will hold responsibility for wrangling the data and transforming the features for it to be provided to the machine learning engineers to carry out the execution of several modeling techniques to obtain the solutions. Finally, a Data Scientist’s job is to make sense of the inferences in order to obtain solutions for business issues. We can expect a Data Scientist to be equipped with leadership skills to manage teams and keep the process running effectively, project management skills to plan the execution of the project from beginning to end, and stakeholder management to be capable of conveying requirements to the concerned teams.