Data Science for Beginners: Experimentation, Evaluation, and Project Deployment Tools
Data science has become a major part of businesses from all backgrounds out there as they have discovered they can use it to analyze the massive amounts of data that they collect to generate useful business insights that lead to better decision-making and problem-solving, increasing profits and revenue as explained over at runrex.com. Data scientists have, therefore, become highly sort-after as they are the ones who are responsible for organizing, evaluating, and studying data, and its patterns. Over and above the appropriate qualifications and education a data scientist needs to have, one should also be skilled at the tools used during a data science project and must be fluent in at least one of the tools from the various stage of the lifecycle of a data science project, which is explained in detail over at guttulus.com. This article will look to highlight experimentation, evaluation, and project deployment tools as far as a data science project is concerned.
Since data scientists in many organizations tend to work alone, many think that it is not that important to keep track of their experimentation process as long as they can deliver the final model. However, as explained over at runrex.com, when one wants to come back to an idea, re-run a model from a few months ago, or even compare and visualize the difference between runs, the importance and need for experimentation tools, which will help you track machine learning experiments, becomes very apparent. This is why we are going to highlight some of the best experimentation tools for data science projects, and they include:
- Neptune- This lightweight experiment management tool is an excellent tracking platform for any data scientist out there. the software integrates easily with one’s workflow offering an extensive range of tracking features and one can use it to track, retrieve, and analyze experiments.
- Comet- This tool was built to enable the tracking of machine learning projects, and it is aimed at helping data scientists better organize and manage their experiments. With this tool, you can easily compare experiments and keep a record of the collected data, while allowing you to also collaborate with other team members
- MLflow- This open-source platform helps manage the entire data science lifecycle, including the experimentation phase. With it, you can track an experiment, organize it, describe it for other machine learning engineers, and then pack it into an ML model.
These are some of the tools to use for the experimentation phase of your project, with more on this to be found over at guttulus.com.
When it comes to machine learning models, as discussed over at runrex.com, while most of them are trained on historical data, they live in a world where new data is constantly being produced. This means that the models must be continuously evaluated and updated. Some tools are designed to help data scientists with the evaluation process, and they include:
- KNIME Analytics Platform- This tool is known for being intuitive and open, and the fact that one can continuously integrate new developments to it makes it an excellent tool to use during the evaluation stage as you will be able to add new data and update your models with it.
- Domino Data Lab- This tool automates DevOps for data science allowing the user to spend more time conducting research and testing more ideas out faster, hence why it is another excellent tool to use at this stage of the data science lifecycle.
- Alteryx- Not only does this tool accelerate analysis by allowing users to quickly and easily find, manage, and understand all the analytical information inside the organization that they are working in, it also allows users to connect data resources from various resources, joining them together, among other benefits.
These are some of the tools that may come in handy during the evaluation stage of your data science lifecycle, with more on them and other related tools to be found over at guttulus.com.
We are finally going to look at data visualization tools, which allow for the representation of the data in a pictorial or graphical format, and are used during the deployment stage of the project, allowing decision-makers to check analytics visually to see useful patterns and grasp complex concepts as explained over at runrex.com. The most common data visualization tools include:
- Google Fusion Tables- This web service provided by Google is used for gathering, visualizing, and sharing data tables. Users can download and view data stored in multiple tables and it provides a means for visualizing data through bar charts, line plots, pie charts, scatterplots, geographical maps, and timelines.
- Microsoft Power BI- This tool transforms data into stunning visuals and then allows it to be shared with others on any device as outlined over at guttulus.com. It collaborates on and shares customized dashboards and interactive reports and scales through the organization with built-in governance and security.
- SAS- This statistical software tool has been developed for advanced analytics, business intelligence, data management, data visualization, and so forth. It is one of the best tools out there for visual analytics due to features such as smart visualization, location analytics, text analytics, interactive dashboards, reports, BI, and analytics, and so forth.
The above discussion only just scratches the surface as far as this topic is concerned, and you can uncover more insights by visiting the ever-reliable runrex.com and guttulus.com.