Data, if we talk about, is massive and ever increasing. With more technologies coming in the forefront, huge and hefty data is getting accumulated which needs the intervention of Data Experts to tackle it efficiently with a worthwhile use.
The unlimited amount of information is universally present which requires to undertake a crucial task of valuable data extraction. Companies and teams need to work in collaboration to respond to the growing mutable world demands.
The field that comes into the roleplay is Data Science that provides efficient solutions to deal with the diverse data forms adding value to your business.
It is a unification of statistics, data analysis, machine learning, and its implementations, to understand and examine the data reserve.
The interdisciplinary concept makes use of scientific methods, computer logic, algorithms, and systems to produce valuable knowledge and insights from both structured and unstructured type of information.
Data professionals can be categorized as data scientists, data engineers, business analysts, data architectures, business analysts, and data developers, where the demand of their roles is soaring and is estimated to open 700,000 positions by 2020.
The rise is exponentially great, reshaping the existing roles of data scientists and developers where they work closely together reflecting the convergence of software development and data science world.
Take a run down to the roles and responsibilities and cooperative efforts of both data executives and engineers able to produce semantic outcomes involved in refining data.
Data Scientists need not necessarily be outright experts in programming and software development but are applied mathematicians from core solving problems and designing questions to pose the right information.
Data Engineers, on the flipside, are specialists who get this information ready and steady for analysis, deals with plumbing and error logging to scale up the working procedures.
Collaborative Workflow Involving Technique and Technology
Developers and Data experts work on different segments of the same workflow where scientists aim to explore data for new insights and engineers try to execute these insights to automate the work and share them on the web and apps.
Say in building a mobile application; the background process involves the sheer working of both performing their responsibilities and showcasing their best.
Working with the raw content and constructing analytical paradigms to draw useful and applicable data is what the former does further feeding the processed data to the latter, merely translating the given converts into functionalities for the end user.
The constructive approach is, even though different, but the realisation to acquire the structured goal is one.
Working in parallel rooting the same cause needs effective tools for communication to share crucial information and feedback at the earliest, for this, thanks to the Cloud services providing a user-friendly and productive platform to work in association.
Jupyter Notebook, an invention of Cloud is set to comfort the users to write, simultaneously share code, in various languages (Python, Scala, R, Node.js) under one roof where data can be loaded from and saved to any cloud database.
Undergoing cleaning and processing, the results can be used in machine learning practices, finally making a move from the Notebook to direct visualisations and APIs.
PixieDust is another significant open-source component to add to notebooks to prompt the scrutiny of data allowing both data scientists and developers in creating data visualisations without any code and launch them as standalone web apps.
Getting acknowledged with the viable combination of Data Science sphere with the development industry, one can think of dilating his knowledge domain and career prospects by investing time to learn tableau with best tableau certification training course endowing a real value to your business making it intelligent. Thus, you can actively participate in critical decision making, planning, and various other phases of business development to drive its growth.
Data Science Purview
Enhancements in automation, AI, robotics, machine learning, deep learning etc. most menial work is replaced by smart machines.
Nevertheless, the immense potential of Data Science enables an individual to work in those areas where computers do not have a role to play.
The soaring development trends of Artificial Intelligence and Machine Learning are continuously revolutionising the digital world, therefore, possessing relevant qualification and expertise Data Science professionals and education seekers can certainly reap benefits in the future.
As per the Glassdoor’s Best Jobs in America stats of 2016 and 2017:
- DevOps engineers were dispensed with a base salary of $110,000 with 2,725 job openings
- Data engineer, rounded with 2,599 job openings and a median base salary of $106,000.
The average pay for a data scientist holding, even near to five years’ experience in 2016 was $92,000. So be a data scientist today exploring abundant job profiles taking up an appropriate course in the same, thereby, contributing to the world of digitisation.
Advancements in technologies continue to be on the rise where Cloud empowers tech personnel to extract value out of the explored data forms.
The close collaboration of data masters and developers have brought innovation sharing a similar platform possessing different tools and programming languages.
The same is an example of agile working where the combined efforts prove to be capable of delivering business value to users keen to take advantage of the upcoming clever techniques.