Data is one of any organization’s most valuable resources. And while data has its benefits, such as enabling businesses to better understand their customers and financial health, it’s also a complicated science.
It isn’t enough to simply capture your data. You must clean, process, analyze and visualize it to glean any insights. This is where data science tools and software make all the difference.
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As a result of the amount of data collected each day (quintillions of bytes), the data science software market has exploded. There are thousands of tools out there for every stage of data science, from analysis to visualization. Selecting the tools that are best for your organization will require some digging.
What is data science?
In its simplest form, data science refers to the gleaning of actionable insights from business data. These insights help businesses make educated decisions about everything from marketing to budgeting to risk management.
Data science features a unique process with various steps. Data is first captured in raw form from various sources such as customer interactions, daily transactions, your company’s CRM and even social media. This data is then cleaned and prepared for mining and modeling. Finally, the data is ready to analyze and visualize.
SEE: Top 5 things you need to know about data science (TechRepublic)
Each step in the data science process will require specific tools and software. For example, during the data capture and preparation steps, both structured and unstructured data must be captured, cleaned and converted into a usable format. This is a process that will require the help of specialized software.
What is the importance of data science?
For every industry, the use of data to inform business decisions is no longer optional. Businesses must turn to data to simply stay competitive. Global tech leaders such as Apple and Microsoft use data to inform all of their critical decisions, highlighting the success that awaits the data-driven. And by 2025, data will be embedded in every decision, interaction and process according to McKinsey.
In other words, organizations that are not yet using their data will soon be far behind in just a few years. And in the here and now, these businesses are missing out on the many benefits of data science.
Benefits of harnessing your data
Better serve your customers
Analyzing customer behavior data can help you better understand their needs and desires. As a result, you can provide better experiences across your entire organization.
Improve your productivity
Data can highlight areas of your internal processes that are draining your productivity. You can then make the changes necessary to improve operational efficiency.
Prevent future risks
Through data science methods such as predictive analysis, you can use your data to highlight areas of potential risk. By taking action on those risks, you can protect your organization, employees and customers.
Make educated decisions in real-time
Decisions must be made daily that can either make or break your business. Through data science, you have access to real-time analytics about the state of your company. Any decision will then be based on the most up-to-date data.
Optimize your resources
Analyzing company data can help you pinpoint processes and tasks that are draining your financial and human resources. You can then make the necessary changes to protect your bottom line and your employees’ sanity.
Increase your data security
Protecting your data is critical, especially as more of it is created and more devices are used to access it. Data science tools such as machine learning can help you detect potential security flaws and fix them before your data is compromised.
Real-world data science applications
There isn’t an industry that can’t benefit from data science and analytics. For example, in healthcare, data science can be used to uncover trends in patient health to improve treatment for all.
In manufacturing, data science can support supply and demand predictions to ensure products are developed accordingly. And in retail, data science can be used to scour social media likes and mentions regarding popular products, informing companies which products to promote next. Of course, these examples are just scratching the surface of data’s capabilities.
What are the tools used in data science?
There’s a wide range of tools out there to cover each step in the data science lifecycle. Data scientists and organizations typically use multiple tools to uncover the right insights. The following are the basic steps involved in the data science process as well as examples of the common tools used for each.
Data extraction tools
The data extraction step requires organizations to pull data from available sources such as databases and other tools like Excel. Extracting data requires a process called ETL, or extract, transform and load.
During this process, data is extracted from its source, transformed through standardization and then loaded into its repository. Tools for data extraction include Hadoop, Oracle Data Integrator and Azure Data Factory.
Data warehousing tools
In most cases, extracted data is then moved into a data warehouse. The data warehouse is an environment where all data from disparate sources resides. This ensures the data is easier to analyze. Various data warehousing tools exist on the market, including Google BigQuery, Amazon Redshift and Snowflake.
SEE: Data warehouse services: What to consider before choosing a vendor (TechRepublic Premium)
Data preparation tools
The data preparation step is perhaps one of the most complex. It entails preparing your data for analysis by cleaning it (or “scrubbing” it). Cleaning the data involves removing data that is duplicated, incorrect or simply incomplete thus resulting in the most accurate dataset.
Tools such as Python are used to scrub data. However, other tools are available that simplify data preparation such as Alteryx.
Data analysis tools
The next step involves data analysis, also known as data processing. During this step, organizations work to process the data so it can be interpreted. In most cases, data scientists will use concepts such as machine learning to model data. As a result, the data is easier to understand and to draw insights from.
Data science tools such as RapidMiner and Apache Spark are suitable options for the processing step.
Data visualization tools
Once data is prepared and analyzed, it should then be visualized. Data visualization makes it easy to glean insights from otherwise complex datasets. Typically, data is placed into visuals such as charts, graphs and maps. It is then easily shareable with those who need it through dashboards and other tools.
What to consider when choosing data science tools
Selecting data science software is not an easy task. There are plenty of considerations to make and questions to answer.
What is the technical knowledge of your team?
First, you’ll need to consider the technical knowledge of your team. If you have a data scientist on staff, get their input on which tools are best for your organization. This also means you can choose more in-depth tools.
If you do not have a data scientist on staff, it’s best to choose software that features low-code development and self-service as well as other tools such as automated machine learning. You should also prioritize tools that feature a simplified and intuitive user interface.
SEE: 5 ways businesses can use data science tools without hiring a data scientist (TechRepublic)
What are your data science goals?
Just like with any other software implementation, you must also consider your organization’s data science goals. How will you use the data? Which problems are you trying to solve? This will help you pinpoint which solutions are best for you.
How complex is your data?
Next, you must answer this question: Where is your data? The complexity of your current data will determine the software you require for proper analysis. For example, if your data is spread across multiple disparate sources, additional software is required, including a data warehousing solution.
Another consideration to make is how much data you have. For example, some tools are better suited for large-scale data analysis than others.
What is your budget?
Your software implementation should only get the go-ahead after carefully considering the costs involved. Costs will vary depending on a wide range of factors such as:
- How much data you plan to use
- How complex the data is
- How many users will need licenses
- Whether or not you can take advantage of any free or open-source tools
Best data science tools and software
Each organization is unique and so is their tech stack. The tools you need to wrangle, analyze and visualize your data won’t be the same tools other businesses need. Luckily, there are so many options to choose from, each with their own unique capabilities. Here are nine of some of the best tools out there currently, in no particular order.
What it’s best for: Apache Spark is best for fast, large-scale data processing.
Apache Spark is an open-source, multi-language engine used for data engineering and data science. It’s known for its speed when handling large amounts of data. The software is capable of analyzing petabytes of data, all at once.
Batching is a key feature of Apache Spark which is compatible with various programming languages, including Python, SQL and R. Many organizations use Apache Spark to process real-time, streaming data due to its speed and agility. Apache Spark is great on its own or it can be used in conjunction with Apache Hadoop.
Apache Spark is best for fast, large-scale data processing.
SEE: Apache Spark vs Apache Hadoop: Compare data science tools (TechRepublic)
What it’s best for: Jupyter Notebook is best for collaborating on and visualizing data.
Jupyter Notebook is an open-source browser application made for sharing code and data visualizations with others. It’s also used by data scientists to visualize, test and edit their computations. Users can simply input their code using blocks and execute it. This is helpful for quickly finding mistakes or making edits.
Jupyter Notebook supports over 40 programming languages, including Python and enables code to produce everything from images to custom HTML. Plus, as an open-source tool, Jupyter Notebook is free to use.
SEE: Jupyter Notebook vs PyCharm: Software comparison (TechRepublic)
What it’s best for: RapidMiner is best for the entire data analytics process.
RapidMiner is a robust data science platform, enabling organizations to take control over the entire data analytics process. RapidMiner starts by offering data engineering, which provides tools for acquiring and preparing data for analysis. The platform also offers tools specifically for model building and data visualization.
RapidMiner delivers a no-code AI app-building feature to help data scientists quickly visualize data on behalf of stakeholders. According to RapidMiner, thanks to the platform’s integration with JupyterLab and other key features, it’s the perfect solution for both novices and data science experts.
SEE: RapidMiner vs Alteryx: Compare data science software (TechRepublic)
What it’s best for: Apache Hadoop is best for distributed data processing.
Although we’ve already mentioned one Apache solution, Hadoop also deserves a spot on our list. Apache Hadoop, an open-source platform, includes several modules such as Apache Spark and simplifies the process of storing and processing large amounts of data.
Apache Hadoop breaks large datasets into smaller workloads across various nodes and then processes these workloads at the same time, improving processing speed. The various nodes make up what is known as a Hadoop cluster.
What it’s best for: Alteryx is best for offering data analytics access to all.
Everyone within an organization should have access to the data insights they need to make informed decisions. Alteryx is an automated analytics platform that enables all members of an organization self-service access to data insights.
Alteryx offers various tools for all stages of the data science process, including data transformation, analysis and visualization. The platform comes with hundreds of code-free automation components organizations can use to build their own data analytics workflow.
SEE: KNIME vs Alteryx: Data science software comparison (TechRepublic)
What it’s best for: Python is best for every stage of data science.
Python is one of the most popular programming languages used for data analytics. It’s simple to learn and widely accepted by many data analytics platforms available on the market today. Python is used for a wide range of tasks throughout the data science lifecycle. For example, it can be used in data mining, processing and visualization.
Python is far from the only programming language out there. Other options include SQL, R, Scala, Julia and C. However, Python is often chosen by data scientists for its flexibility as well as the size of its online community. And being an open-source tool, this is critical.
SEE: Python programming language: A cheat sheet (TechRepublic)
What it’s best for: KNIME is best for designing custom data workflows.
The KNIME Analytics Platform is an open-source solution that provides everything from data integration to data visualization. One feature that’s worth highlighting is KNIME’s ability to be customized to fit your specific needs. Using visual programming, the platform can be customized through drag-and-drop functionality, without the need for code.
KNIME also features access to a wide range of extensions to further customize the platform. For example, users can benefit from network mining, text processing and productivity tools.
Microsoft Power BI
What it’s best for: Microsoft Power BI is best for visualizations and business intelligence.
Microsoft Power BI is a powerhouse tool for visualizing and sharing data insights. It’s a self-service tool, which means anyone within an organization can have easy access to the data. The platform enables organizations to compile all of their data in one place and develop simple, intuitive visuals.
Users of Microsoft Power BI can also ask questions in plain language about their data to receive instant insights. This is a great feature for those with very little data science know-how.
As a bonus, Microsoft Power BI is also highly collaborative, making it a great choice for larger organizations. For example, users can collaborate on data reports and use other Microsoft Office tools for sharing and editing.
What it’s best for: TIBCO is best for unifying data sources.
As an industry-leading data solution, TIBCO offers a collection of products as part of its Connected Intelligence platform. Through this platform, TIBCO helps organizations connect their data sources, unify that data and then visualize real-time insights efficiently.
TIBCO first enables users to connect all of their devices, apps and data sources into one centralized location. Then, through robust data management tools, users can manage their data, improve its quality, eliminate redundancy and so much more. Finally, TIBCO delivers real-time data insights via visual analytics, streaming analytics and beyond.