Data science is the practice of extracting insights from data to solve problems. It requires a combination of mathematical modelling, statistics, data visualisation, and software engineering as it helps identify patterns and trends and map them to business goals.
These skills are in high demand, which means that if you have the aptitude and curiosity to learn about data and its applications, it could make your life easier.
The problem is, it’s a full-time job just keeping track of all the information you need for your company or project. Here are three easy ways to automate your data science work to make your life easier.
Data Preparation
Data is in abundance and everywhere. However, not every organisation can maximise its usefulness because of the need for data preparation before utilisation. Data preparation tasks are labour intensive and could account for most of the time and resources allocated to a project.
Due to redundancies, inaccuracies, and improper formatting, to mention a few, data must be organised into appropriate domains, labelled correctly, and validated before it can be helpful to the organisation.
When possible, it could be helpful to implore tools that simplify and automate many of the most painful data preparation processes.
Some framework that helps automate data preparation is Snorkel – Automated Training Data Preparation and OpenRefine – Automated Data Manipulation.
Data Analysis and visualisation
Nowadays, most organisations have enormous datasets, yet only having massive datasets doesn’t enhance the business except if investigated for insights to drive decision making.
A reasonable amount of time goes into data analysis, feature selection, and feature engineering in the lifecycle of a data science project or any machine learning project. It is an essential part of a data science project that involves activities like data cleaning, handling missing values, handling outliers, handling imbalanced datasets, and handling categorical features.
To save time in data analysis and visualisation, the following framework – Dtale, pandas profiling, sweetviz, and autoviz can help you automate your tasks.
In addition, after understanding the KPIs and objectives, automate data analysis and visualisation using tools like PowerBI and tableau.
Link your database with PowerBI, and your data will be automatically analysed once you refresh. It will enable you to focus on the next significant activity rather than repetitive tasks.
Model building and development
The essence of AutoML is to automate repetitive tasks such as pipeline creation and hyper-parameter tuning to enable data scientists to focus on the business problems on hand. Data scientists can accelerate ML development by using it to implement efficient machine learning.
AutoML is a valuable tool to automate model building and development. It has been in use in developing recommender systems and other live applications. Although AutoML is not advisable in some safety-critical systems, it saves cost and improves operational efficiencies in domains where it is functional. AutoML takes care of repetitive tasks and lets the data scientists focus on what matters. It supports reproducibility and transparency.
You might use data science models to predict when certain events will occur or see if a specific change will improve your business metrics. In these cases, you’ll develop your models so that they’re easy to understand by anyone in your team.
Some common framework for AutoML is Auto-Sklearn, MLBox and TPOT.
Conclusion
Fortunately, there are many ways to automate specific data science tasks to help you save time and effort while providing consistent results. If you’ve tried any of these tips, let us know in the comments below!
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