Data Science

Data science combines statistics and math, advanced analytics, specialized programming, machine learning and artificial intelligence (AI), and with certain specific subject matter expertise to uncover actionable insights hidden in an organization’s data. These insights can be used to guide strategic planning and decision making.

The increasing volume of data sources, and eventually data, has pushed data science as one of the fastest growing sector across every industry. Therefore, organizations increasingly depend on Data scientists for data interpretation and provide applicable recommendations to improve business outcomes.

A data science project involves the following stages:

• Data ingestion: It begins with the data collection of both structured and unstructured data from all possible sources using different methods. These methods include manual entry, web scraping, real-time streaming data from systems and devices too. Data sources could include structured data like customer data, along with unstructured data like log files, audio, video, pictures, social media, the Internet of Things (IoT), and more.
• Data storage and data processing: Companies need to consider different storage systems based on the type of data that needs to be captured as data can have different structures and formats. Data management teams assists to set standards around data structure and storage, which clear the way for workflows around machine learning, analytics, and deep learning models. This stage involves cleaning data, deduplicating, transforming and combining the data using ETL (extract, transform, load) jobs or other technologies of data integration . This data preparation is completely necessary for promoting data quality before loading into a data warehouse, data lake, or other repository.
• Data analysis: Data scientists conduct an experimental data analysis to examine patterns, biases, ranges, and distributions of values within the data. For a/b testing, this data analytics exploration drives hypothesis generation and allows analysts to determine the relevance of the data for use within modeling efforts for predictive analytics, machine learning, and deep learning. Depending on the accuracy of the model, organizations can become reliant on these insights for making business decisions, allowing them to drive more scalability.
• Communicate: Insights are given as reports and other data visualizations that make the insights — and their impact that happens on business — easy enough for business analysts and other decision-makers to analyze. R or Python are data science programming language that includes components for generating visualizations; alternately, data scientists can utilize dedicated tools of visualization.

Data science and data scientist

If Data science is a discipline, data scientists are the practitioners within that field. It is not necessary that Data scientists are directly responsible for all the processes involved in the data science lifecycle. For example, data pipelines are usually handled by data engineers —data scientist could make recommendations with regard to what sort of data is required or useful. While data scientists can raise machine learning models, scaling these efforts at a larger level demands more software engineering skills to advance a program to run more quickly. Hence, it’s usual for a data scientist to associate with machine learning engineers to scale machine learning models.

Responsibilities of Data scientist can commonly overlap with a data analyst, especially with data visualization and exploratory data analysis. However, skillset of a data scientist is usually broader than the average data analyst. Comparatively speaking, data scientist leverage common programming languages, such as Python and R, to perform more statistical inference and data visualization.

Data scientists require computer science and pure science skills beyond those of a typical business analyst or data analyst to perform these tasks. The data scientist must also analyze the specifics of the business, such as healthcare, automobile manufacturing or eCommerce.

To be precise, a data scientist must be able to:

• Have enough knowledge about the business to ask relevant questions and recognize business pain points.
• Apply computer science and statistics, along with business awareness, to data analysis.
• Use a wide range of techniques and tools in order to prepare and extract data — everything from databases and SQL to data mining to methods of data integration .
• Extract insights from big data using artificial intelligence(AI) and predictive analytics, including natural language processing, machine learning models, and deep learning.
• Write programs that automate calculations and data processing.
• Illustrate and convey stories that convey the meaning of results to stakeholders and decision-makers with clarity at every level of technical understanding.
• Explain how the results can be used in order to solve business problems.
• Collaborate with other data science team members, such as IT architects, data engineers, data and business analysts, and application developers.

These skills are in high demand. Therefore, many professionals that are breaking into a data science career, explore a variety of data science programs, such as degree programs offered by educational institutions, certification programs, and data science courses.