We live in a world where data is king. Our lives, our personal information, our finances, our jobs, our entertainment, have all become digitized and stored as data. Paper and hardcopy are dead—long live the electronic revolution!
As a result of this increase in generated data, there is a corresponding need to study and maintain it. That’s where the field of data science comes in. Of course, you need the right tools to conduct all the tasks associated with data science.
Do you see where this is heading?
Today, we’re looking at data science, specifically the most popular tools used to make sense of data and what advantages each one offers. But first, a definition.
Data science is defined, unsurprisingly, as “the study of data.” That certainly narrows it down! Fortunately, there’s more to it. The purpose of data science is to gain knowledge and insights from all kinds of data, structured or unstructured.
The process includes creating and implementing ways of recording, analyzing, and storing data to glean useful information effectively. Data science isn’t computer science; the latter focuses on creating algorithms and programs for processing and recording data, while data science focuses on analysis and may not even require a computer!
We mentioned earlier that since so much of life is digitized data, it’s only logical to study data science. But there are specific reasons why understanding data science and applying it to more careers is a good idea:
Let’s take a look at those tools and their advantages now, placed in alphabetical order:
This tool is a machine-learning (ML) resource that takes raw data and shapes it into real-time insights and actionable events, particularly in the context of machine-learning.
Advantages:
This open-source framework creates simple programming models and distributes extensive data set processing across thousands of computer clusters. Hadoop works equally well for research and production purposes. Hadoop is perfect for high-level computations.
Advantages:
Also called “Spark,” this is an all-powerful analytics engine and has the distinction of being the most used data science tool. It is known for offering lightning-fast cluster computing. Spark accesses varied data sources such as Cassandra, HDFS, HBase, and S3. It can also easily handle large datasets.
Advantages:
This tool is another top-rated data science resource that provides users with a fully interactable, cloud-based GUI environment, ideal for processing ML algorithms. You can create a free or premium account depending on your needs, and the web interface is easy to use.
Advantages:
D3.js is an open-source JavaScript library that lets you make interactive visualizations on your web browser. It emphasizes web standards to take full advantage of all of the features of modern browsers, without being bogged down with a proprietary framework.
Advantages:
This tool is described as an advanced platform for automated machine learning. Data scientists, executives, IT professionals, and software engineers use it to help them build better quality predictive models, and do it faster.
Advantages:
Yes, even this ubiquitous old database workhorse gets some attention here, too! Originally developed by Microsoft for spreadsheet calculations, it has gained widespread use as a tool for data processing, visualization, and sophisticated calculations.
Advantages:
If you’re a data scientist who wants automated predictive model selection, then this is the tool for you! ForecastThis helps investment managers, data scientists, and quantitative analysts to use their in-house data to optimize their complex future objectives and create robust forecasts.
Advantages:
This is a very scalable, serverless data warehouse tool created for productive data analysis. It uses Google’s infrastructure-based processing power to run super-fast SQL queries against append-only tables.
Advantages:
Java is the classic object-oriented programming language that’s been around for years. It’s simple, architecture-neutral, secure, platform-independent, and object-oriented.
Advantages:
MATLAB is a high-level language coupled with an interactive environment for numerical computation, programming, and visualization. MATLAB is a powerful tool, a language used in technical computing, and ideal for graphics, math, and programming.
Advantages:
Another familiar tool that enjoys widespread popularity, MySQL is one of the most popular open-source databases available today. It’s ideal for accessing data from databases.
Advantages:
Short for Natural Language Toolkit, this open-source tool works with human language data and is a well-liked Python program builder. NLTK is ideal for rookie data scientists and students.
Advantages:
This data science tool is a unified platform that incorporates data prep, machine learning, and model deployment for making data science processes easy and fast. It enjoys heavy use in the manufacturing, telecommunication, utility, and banking industries.
Advantages:
This data science tool is designed especially for statistical operations. It is a closed-source proprietary software tool that specializes in handling and analyzing massive amounts of data for large organizations. It’s well-supported by its company and very reliable. Still, it’s a case of getting what you pay for because SAS is expensive and best suited for large companies and organizations.
Advantages:
Are you prepared enough for your next profession as a Data Scientist? Try answering these Data Science Foundations with R Practice Test Questions and find out yourself!
With data use exploding everywhere, it’s no surprise that there are many excellent opportunities to have a challenging and rewarding career in the field of data science. If you’re looking for a data science career, there are many different means for the newcomer to learn data science.
Fortunately, you don’t have to look very far. Simplilearn offers a multitude of resources to help you learn everything you need to know about the basics of data science and more advanced data scientist skills.
When you’re ready to kick your data science career into high gear, you must look into Simplilearn’s Data Science Master’s Program, presented in collaboration with IBM. You will experience world-class training by an industry leader on the most in-demand data science and machine learning skills. Gain hands-on exposure to critical technologies and tools used in data science, including R, SAS, Python, Tableau, Hadoop, and Spark.
The program gives you six courses, over 30 in-demand skills and tools, over 15 real-life projects, and a certification that’s recognized industry-wide. Data scientists can earn an annual average salary of $113,309, according to Glassdoor.
If you’re already a data scientist, then consider Simplilearn’s Data Science certification program in partnership with Purdue University as a means of upskilling. The more you know, the more valuable a professional you are, something which could come in handy somewhere down the line.
Check out Simplilearn today and embark on an exciting and profitable career!
Name | Date | Place | |
---|---|---|---|
Data Science with R Programming | 12 Mar -10 Apr 2021, Weekdays batch | Your City | View Details |
Data Science with R Programming | 15 Mar -31 Mar 2021, Weekdays batch | New York City | View Details |
Data Science with R Programming | 27 Mar -25 Apr 2021, Weekend batch | Atlanta | View Details |
Simplilearn is one of the world’s leading providers of online training for Digital Marketing, Cloud Computing, Project Management, Data Science, IT, Software Development, and many other emerging technologies.
Data Science with R Programming
Data Scientist
Data Science with Python
*Lifetime access to high-quality, self-paced e-learning content.
Explore CategoryData Science Career Guide: A Comprehensive Playbook To Becoming A Data Scientist
A Day in the Life of a Data Scientist
Data Science Tutorial for Beginners
Data Science Interview Guide
Role of Citizen Data Scientist in Today's Business
Top 50 Data Science Interview Questions and Answers for 2021