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.
What is Data Science?
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!
Why Data Science?
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:
- Fraud prevention. Data scientists can identify anomalous data and create methodologies for predicting fraudulent incidents
- Informed decisions. Accurate, useful data helps management make better decisions, ones that have an increased likelihood of success
- Better customer experiences. The more appropriate and relevant data that businesses have on their customers, the better they can tailor the experience for each consumer
Data Science Tools
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.
- It’s on a cloud platform, so it has all the SaaS advantages of scalability, security, and infrastructure
- Makes machine learning simple and accessible to developers and companies
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.
- Highly scalable
- It has many modules available
- Failures are handled at the application layer
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.
- Over 80 high-level operators simplify the process of parallel app building
- Can be used interactively from the Scale, Python, and R shells
- Advanced DAG execution engine supports in-memory computing and acyclic data flow
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.
- An affordable resource for building complex machine learning solutions
- Takes predictive data patterns and turns them into intelligent, practical applications usable by anyone
- It can run in the cloud or on-premises
- Ideal for client-side Internet of Things (IoT) interactions
- Useful for creating interactive visualizations
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.
- With just a single click or line of code, you can train, test, and compare many different models
- It features Python SDK and APIs
- It comes with a simple model deployment process
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.
- You can sort and filter your data with one click
- Advanced Filtering function lets you filter data based on your favorite criteria
- Well-known and found everywhere
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.
- Easily scalable to fit any size challenge
- Includes robust optimization algorithms
- Simple spreadsheet and API plugins
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.
- Extremely fast
- Keeps costs down since users need only pay for storage and computer usage
- Easily scalable
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.
- Suitable for large science projects if used with Java 8 with Lambdas
- Java has an extensive suite of tools and libraries that are perfect for machine learning and data science
- Easy to understand
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.
- Intuitive use
- It analyzes data, creates models, and develops algorithms
- With just a few simple code changes, it scales analyses to run on clouds, clusters, and GPUs
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.
- Users can easily store and access data in a structured manner
- Works with programming languages like Java
- It’s an open-source relational database management system
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.
- Comes with a suite of text processing libraries
- Offers over 50 easy-to-use interfaces
- It has an active discussion forum that provides a wealth of new information
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.
- All of the resources are located on one platform
- GUI is based on a block-diagram process, simplifying these blocks into a plug-and-play environment
- Uses a visual workflow designer to model machine learning algorithms
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.
- Numerous analytics functions covering everything from social media to automated forecasting to location data
- It features interactive dashboards and reports, letting the user go straight from reporting to analysis
- Contains advanced data visualization techniques such as auto charting to present compelling results and data
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!
Do You Want to Become a Data Scientist?
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 Courses, 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 Bootcamp 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!