Data science remains one of the most promising and in-demand career pathways for qualified people. Today's effective data professionals recognize that they must go beyond the traditional capabilities of large-scale data analysis, data mining, and programming.
Companies recognized the need for data experts competent in organizing and analyzing enormous amounts of data in 2008, and the term "data scientist" was established.
Effective data scientists are able to identify relevant questions, collect data from a multitude of different data sources, organize the information, translate results into solutions, and communicate their findings in a way that positively affects business decisions. These skills are required in almost all industries, causing skilled data scientists to be increasingly valuable to companies
This concept is further complicated by the fact that there are additional positions that are sometimes mistakenly perceived to be similar but are actually quite distinct. Data analyst, data engineer, and other similar positions are available. I'll get to that later.
The graphic below depicts some of the main fields that a data scientist might use. In the ideal situation, a data scientist's level of expertise and expertise in each vary along a scale ranging from beginner to adept to expert.
While data scientists come from a variety of educational and work experience backgrounds, the majority of them should be strong in, or in the best case, specialists in, four key areas. The following are listed in no specific order of significance or importance:
Other abilities and expertise are also highly desirable, but these four are the most important in my opinion. For the remainder of this paper, these will be referred to as the data scientist pillars.
Data scientists collaborate closely with business stakeholders to learn about their objectives and how data may help them achieve them. They construct algorithms and prediction models to extract the data that the business need, as well as help, evaluate the data, and share findings with peers.
While each project is different, the process for gathering and analyzing data generally follows the below path:
The following are some of the most prevalent data science jobs.
Data scientists construct algorithms and predictive models, as well as do customized analyses, using data modeling procedures.
Data analysts manipulate massive data sets to find trends and draw relevant findings that may be used to guide strategic business decisions.
Data engineers clean, combine and organize data from a variety of sources before transferring it to data warehouses.
The tasks of data scientists and data analysts are sometimes confused, yet they are actually quite different. To put it another way, data scientists create techniques for modeling data, whereas data analysts study data sets for trends and conclusions.
The function of a data scientist is typically thought to be more senior than that of a data analyst because of this distinction and the more technical nature of data science; yet, both roles may be attainable with identical educational backgrounds.
Python is the most common coding language I typically see required in data science roles, along with Java, Perl, or C/C++. Python is a great programming language for data scientists
Python can be used for practically all of the phases required in data science operations due to its versatility. It accepts a variety of data types and allows you to effortlessly import SQL tables into your code. You can build datasets using it, and you can find almost any form of the dataset you need on Google.
in MMT our team is divided into three groups :
Data integration: data often resides in a number of separate data sources, our data integration team in MMT makes sure that the Information from all of those different sources are pulled together for analytical needs or operational actions
Data analytics: after storing the data in our database, the analytic team dives deeper into these data to make decisions, analysis, and predictions using different technologies like Python and Rstudio
Data visualization: once the data analytic team gets results … the results have to be presented in shiny and clear dashboards, that’s when the data visualization team comes in, they can deliver a clear message for users using the data already existing in our database stored by the data integration team or the results from the analytic team