5 stimulating facts about data science tools to handle real time data.
To all the readers who are here on my article, Being a technology geek person, I would like to give an insight about the processes that drives all the industries in the fast- paced environment.
Did you ever think about the processes and methodology involving automation and data exchange that reshapes the global manufacturing landscape?
You may have different presumptions. However, scientists and analysts have said their views in different perspectives which are identical.
Are you all inquisitive to know their views ?
They all have one strong perception that the new revolutionary technologies are behind “Data Science”.
There are lot of hidden concepts behind one breakthrough technology like a chain reaction, that gears up the industry’s growth. Thus bringing the fact that automation is a result of Data Science.
Let us explore! Data Science is the use of data, to answer questions. It involves statistics, computer science, data cleaning, formatting and data visualization. It combines these different fields of work in statistics and computation to interpret data for decision-making processes.
A Data Scientist is responsible for extracting, manipulating, pre-processing and generating predictions from the database. They require multiple data science tools and programming languages for an effective data operative process.
When I was in a hunt for a domain which could help in my career growth and expertise my managerial skill, I found there is a great demand in data science to get acquainted with the tools essential to carry out daily operations.
It persuades me to grasp the opportunity to learn about various Data science tools and techniques. I am here to enhance your knowledge with unique skills. Let’s look at the 5 stimulating reasonable facts about data science tools to handle real-time data.
“ Think analytically, rigorously, and systematically about a business problem and come up with a solution that leverages the available data.”
– Michael O’ Connell
MICROSOFT EXCEL :
Microsoft Excel is a powerful analytical tool for Data Science. Today, it is widely used for data processing, visualization, and complex calculations.
Excel comes with various functions, pivot tables, filters, slicers, etc. You can also create your own custom functions and formulae using Excel. The purpose of excel extends from calculating enormous amounts of data to creating powerful data visualizations and spreadsheets, thus becoming an ideal choice for the Data scientists to dwell on it.
You can also connect SQL with Excel to manipulate and analyze the built-in data. With the release of ToolPak Kit, it is now much easier to compute complex analyses.
R PROGRAMMING :
R is a programming language with free software environment for statistical computing and graphics. It is popularly used among statisticians, data miners, data analysts, etc.
If you’re accelerating your career in this technology-driven world, it’s best to learn various tools and techniques. Self-directed learning makes you independent and increase your ability to empower in various skills. You can have hands on experience by using open-source software like R, Rstudio and GitHub.
R programming is the computing environment which induces you to learn exactly how to import, organize the data, create charts and graphs. It also exports data and makes you learn about R packages, advanced-data visualization, generating plots and maps using JSON structures, implementation of statistics, data munging/wrangling, data manipulation and so on.
Python is an interpreted and high-level programming language that lets you work quickly and integrate systems more effectively. It can be used on a server to create web applications, data handling, data loading, merging and reshaping data, data visualization and data transformation.
You can learn from python libraries like pandas, numpy to perform data analysis and manipulation of data in the form of series and data frames employing different methods of analysis.
Tableau is a data visualization software. It is the most powerful, secure, and flexible end-to-end analytics platform. It helps people and organisations to be more data-driven. The important aspect of Tableau is its ability to interface with databases, spreadsheets, OLAP (Online Analytical Processing) cubes, etc.
Tableau helps you to understand the dependencies between the predictor variables such as the ability to visualize geographical data and for plotting longitudes and latitudes in maps. It is the market-leading choice for modern business intelligence.
Along with visualizations, you can also use its analytics tool to analyze data. The Tableau Desktop feature allows you to create customized reports and dashboards to get real-time updates. It also provides cross-database joint functionality that allows you to create calculated fields and join tables which helps in solving complex data-driven problems.
MySQL is an open-source relational database management system. It can create, design and operate with SQL databases. It creates a platform to work with sophisticated real-life databases and gain theoretical insight about relational databases. You might become a proficient MySQL Workbench user and acquire the top-notch coding techniques.
If you are a coder, you can enhance your knowledge in “Structured Query Language” thus paving a path to different job roles such as SQL developer, Database administrator, Business Analyst and Business Intelligence. MySQL facilitates to load the enormous amount of data in the database via different softwares like selenium software.
You might think, why are we using “MySQL”?
Some of the world’s largest and fastest-growing organizations including Facebook, Google, Adobe, Alcatel Lucent and Zappos rely on MySQL to save time and cost by empowering their high-volume Web sites, business-critical systems and packaged software.
We can summarize that the growing need for organized data has resulted in the higher demands for data scientists, miners and also data analysts. Data science tools are beneficial to perform as decision-makers, responsible for analyzing and handling a large amount of structured and unstructured data.
I’m sure you’re curious to learn more.
” What you’re going through is not what you’re going to be ” so believe in yourself to face the VUCA world and become an expert in data science!
“All our dreams can come true, if we have the courage to pursue them.”
– Walt Disney
Still, if you have any doubts regarding data science tools, feel free to post your questions in the comment section.
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