DATA SCIENCE FOR BEGINNERS: INTRODUCTION AND IMPORTANCE OF DATA SCIENCE.
DATA SCIENCE FOR BEGINNERS: INTRODUCTION AND IMPORTANCE OF DATA SCIENCE.
Data science is an inter disciplinary field that uses algorithms, scientific methods and systems to extract knowledge from structured and unstructured data. Data science unifies statistics, data analysis and related methods into one so as to understand actual phenomena. Data science touches many different fields as discussed by the panelists at the guttulus.com they include mathematics, statistics, computer science, information science and domain knowledge. Data science composes of preparing data for analysis and presenting findings to inform high level decisions in companies. Everything about science keeps on changing because of the impact of information technology, data deluge, graphic design, complex system, business and communication. The concepts of data science have been around for some time as it dates back all the way to the 1960’s, whereby it was known as data analysis. In 1974 Peter Naur proposed that it was an alternative name for computer science. In 1996 the International Federation of Classification Societies became the first to feature data science as a topic. However, the definition was still debated up until 1997 when Jeff Wu suggested that it should be renamed data science. The term data scientist as used in the modern age is attributed to DJ Patil and Jeff Hammerbacher in 2008. Over the years it has been an ever growing field and a career as a data scientist is ranked as the third best job as discussed by experts at runrex.com. Most careers require an educational background whereby you should have at least a bachelor’s degree in a quantitative field or as another alternative join a coding boot camp to help in pre-qualification to supplement a bachelor’s degree. Most data scientists join in at any stage and may hold a masters or even a PhD making it a very competitive field to make a career out of. Data science touches many fields including the following;
- Machine learning scientist-as the name suggests it deals with machines in the sense that one understands new methods of data analysis and the creation of algorithms.
- Data engineering- they are very important aspects in this field as they mostly deal with design, building and integration of data from various resources also not forgetting they manage big data.
- Data analyst- they allow companies to meet their needs by utilizing large sets of data in order to gather information
- Data consultant- they maintain the usage of all the information yielded from data analysis
- Data architect- they are associated with the creation of data solutions that are optimized for the performance and also not forgetting they help in the designing of application
- Application architects- these architects as the name suggests help in the monitoring of how the applications interact with users and also with the other apps in the field.
Due to the growth of data science as a career option, there has been a massive growth of data driven companies all over the world from over 333 billion in the year 2015 to 1.2 trillion collectively in 2020. At guttulus.com the experts have made an observation that due to the utilization of big data, business models have been altered thus allowing creation of new companies and the improvement of existing ones to the required standards and all of them revolving around data science. Data scientists breakdown big data into usable information in form of algorithms and statistics that help companies determine their optimal operations. Application of data science techniques has led to the development of different varieties of techniques as shown below
- Linear regression
- Logistic regression
- Decision tree
- Support vector machine
- Clustering- this is the grouping of data together
- Dimensionality reduction- it is the reduction of in the complexity of data so that it can be performed more quickly.
- Machine learning- this is the inferencing of patterns from data in order for tasks to be performed
All over the world, there are many different data interpretation languages. The great minds at runrex.com have been able to compile the languages used by machines and applicable in data science.
- Python- in this type of language there are some few libraries that are used in data science for example numpy, pandas and scipy. This language has a simple syntax that is commonly used for data science.
- Julia- as compared to the others this is a high performance, dynamic programming language well suited for computational science and numerical analysis.
- R- this language was created to be specially used by statisticians and also to be used for data mining.
For something to have a good and stable working condition it has to have an equally stable framework. At guttulus.com the panelists have compiled a great list of frameworks used in data science as follows;
- Tensor flow- it is used for creating models that are used in machine learning that are created by google.
- Pytorch- this type is developed for machine learning that was created by Facebook.
- Jupyter notebook- it is a web interactive interface that allows faster experimentation
- Apache Hadoop-it is used to process data over a large distributed system
