Last updated on July 4th, 2022 at 07:35 am
Top 10 Tech Tips And Tools That Data Scientists Should Know?
The future will see the unlocking of nearly 3.1tn USD of data harnessed and held proprietary by governments and businesses. The present number of people who clean and handle such data from multiple sources and in multiple formats is grossly insufficient to handle the present and future volumes of data.
The technology, skills, and training of people on emerging skills are racing ahead and require an eclectic blend of technological knowledge, tools, techniques, skills and best practices learned from day-to-day slip-ups and lessons learned from them. The infrastructure and machines are seeing rapid changes to unleash computing power, processing power, hardware and software storage power.
One of the most popular careers of modern times is a data scientist. Data science continues to grow because there are far fewer people than the huge volumes of data we are constantly being generated globally every nanosecond. And just as this data continues to grow the demand for data science careers grows. And this lot of aspirants will never fail to find a highly paid job for the next couple of decades if they do a Data Science course to re-skill themselves and stay abreast of emerging techniques in the field.
We now explore the topmost tech tips, apps, and useful tools for data scientists that have the potential to make their work a bit easier.
Analytics Platform- KNIME:
This tool used for raw data analysis tool is good for extracting useful information from it. Being a free open-source application it makes it easy to build analysis and extraction apps around raw data sets.
Lambada- AWS (Amazon Web Service):
The Lambada platform is an event-driven server-less platform helping put models into production in an Amazon Web Service environment. A 3USD fee is charged for access and data scientists with a creative theory can test it on raw or live data. Besides eliminating storage and infrastructure needs one uses a cloud-based environment and has no waiting for implementation or developer intervention.
Python suite:
This suite is taught in a data science course and forms part of the toolkit. While you do not need mastery in it Python knowledge is essential to handle your work better.
Flask micro-services:
Part of the Python suite, the micro web framework Flask tool is useful for writing programs in Python and transforming them into web calls. It is very useful in microservices building and creates large datasets shortcuts.
PySpark:
PySpark from the Python suite can scale humungous volumes of data. It is used with ML and Data platform ETLs for creating the pipelines.
Feature-tools and engineering:
Deep Learning allows data scientists to use datasets that are semi-structured while turning their features into useful insights and applications of this kind of data. Feature tools use such data to define associations between data tables, to produce and generate a coherent model. It can effectively take the grunt out of the data scientist’s job.
RapidMiner:
Any data science course will teach you that data cleaning and preparation is the most time-consuming part of working with data. RapidMiner automates and makes this chore more manageable and easier. Most times the delays in cleaning raw data in big data projects cause time delays that prove fatal to the project.
Athena from Amazon:
Athena is an AWS tool very useful for storing large tranches of data and datasets. Google BigQuery and Microsoft Azure are competing platforms very similar in nature but with a suite of different capabilities and tools.
Fusion Tables in Google:
Google’s Fusion Tables launched in 2009 scores in data visualization and is useful to gather, share data tables and visualize data.
Microsoft Power BI:
The 2014 version of Power BI is a business analytics solution using raw data to create models, intelligence and visualizations on their own company dashboards adding to the value and applications of raw data.
Parting notes:
Data science is a well-paying career choice that is exciting, satisfying and challenging. Making raw data useable, involves cleaning, parsing, and making the data transferable and useful. Without tools, this work can be beyond human capacity and it is the technology that steps in to automate, quicken and make the job easier. Doing the data science course at Imarticus Learning can unleash the innovator in you by skilling you with comprehensive knowledge and the appropriate technology and tools to make a career in data analysis.
For more details regarding this in brief and for further career counseling, you can also contact us through the Live Chat Support system or can even visit one of our training centers based in - Mumbai, Thane, Pune, Chennai, Bangalore, Hyderabad, Delhi and Gurgaon.
