Blog | Data Science and Technology

Data Science is a multidimensional field that uses scientific methods, tools, and algorithms to extract knowledge and insights from structured and unstructured data.But in reality, he does so much more than just studying the data. I agree that all his work is related to data but it involves a number of other processes based on data.Data Science is a multidisciplinary field. It involves the systematic blend of scientific and statistical methods, processes, algorithm development and technologies to extract meaningful information from data. The average Data Scientist’s work week as follows: Typical work weeks devour around 50 hours. The Data Scientists generally maintain internal records of daily results. The Data Scientists also keep extensive notes on their modeling projects for repeatable processes. The good Data Scientists can begin their career with a $80k salary, and the high-end experts can hope to make $400K. The industry attrition rate for DS is high as organizations fre

As a data scientist, you probably want and need to learn Structured Query Language, or SQL. SQL is the de-facto language of relational databases, where most corporate information still resides. But that only gives you the ability to retrieve the data — not to clean it up or run models against it — and that’s where Python and R come in.R and Python both share similar features and are the most popular tools used by data scientists. Both are open-source and henceforth free yet Python is structured as a broadly useful programming language while R is created for statistical analysis. A little background on R R was created by Ross Ihaka and Robert Gentleman — two statisticians from the University of Auckland in New Zealand. It was initially released in 1995 and they launched a stable beta version in 2000. It’s an interpreted language (you don’t need to run it through a compiler before running the code) and has an extremely powerful suite of tools for statistical modeling and graphing

Converting raw and quantitative data into organized and informative information needs a lot of brain power and understanding. It is true that everyone can’t be Aryabhatta but, you can be hardworking, focused and dedicated. So, it is time to show your dedication and hard work for learning maths and statistics for data science. Mathematics and Statistics are two of the most important concepts of Data Science. Data Science revolves around these two fields and draws their concepts to operate on the data. Today, we will explore the various concepts that build up data science and their practical usages in this field.Data Science has become a trending technology in the world today. In order to learn data science, you must reinforce your knowledge of mathematics and statistics. So let’s first explore how much maths is required for data science – Math for Data Science Mathematics has created an impact on every discipline. The magnitude of the usage of mathematics varies according to the

The standard job description for a Data Scientist has long highlighted skills in R, Python, SQL, and Machine Learning. With the field evolving, these core competencies are no longer enough to stay competitive in the job market . Data Science is a competitive field, and people are quickly building more and more skills and experience. This has given rise to the booming job description of Machine Learning Engineer, and therefore, my advice for 2020 is that all Data Scientists need to be developers as well. To stay competitive, make sure to prepare yourself for new ways of working that come with new tools. 1. Agile Agile is a method of organizing work that is already much used by dev teams. Data Science roles are filled more and more by people who’s original skillset is pure software development, and this gives rise to the role of Machine Learning Engineer.More and more, Data Scientists/Machine Learning Engineers are managed as developers: continuously making improvements to Mac