The article How To Improve Data Science Workflow? is the best way to explore Data Science. Data Science is linked to the data analysis done by data scientists to develop and improvise algorithms for machine learning and then onto artificial intelligence. This newly emerging field which is set to envelop all business and commercial activities around the world has created a tremendous demand for programmers of machine learning who are known as data scientists.
How To Improve Data Science Workflow? – An Overview!
There is a very heavy demand for data scientists all around the world today as machine learning is important to train systems to work independently in the internet of things environment where every appliance, every white good and every device is getting instructions and troubleshooting self-correction guides from the internet.
This has created a tremendous demand for data scientists like nothing ever before and this demand is set to surpass the demand for IT professionals and programmers 2 decades ago.
The demand for Data scientists and managing data science workflow has become so big that companies have resorted to a new tool known as AutoML or automatic Machine learning. These companies have begun developing frameworks that are typically done by data scientists.
The main functions of a data scientist include pre-processing, selecting and tuning models, selecting features and evaluating the results. The flow of data science projects begins with source data access which is actually raw data followed by data processing and modelling. The next 2 stages are deployment and monitoring. The stage of modelling involves experiments and exploratory analysis.
As an example, the source data of a health care system is likely to be a complex jumble of genome sequence files, excel sheets, word files, scan images and patient records. Data Scientists in this scenario will know they need to access other websites for additional information so they may create an SQL (Structured query language) database server in the cloud and import files to it. A raw data directory can be created and the genome sequencing files stored in that directory.
An Amazon S3 bucket can be created using DVC (Data Version Control) to store raw directories. A python package is used to query external websites. Scans and images go into an HDF5 file in a Quilt package. In essence, data scientists need to monitor SQL servers, S3 buckets, directories, quilt packages and python packages. All this raw data needs to be read-only and a backup is necessary.
The next stage is data processing where all the raw source data is cleaned up for use in the modelling stage. This is a form of feature engineering and care should be taken for easy traceability of all data to its source. At this stage, a computation graph is used.
This is followed by the modelling stage where multiple models may need to be managed with different hyperparameters and then selecting the best result. The selected model is then run into production, monitored. The final steps are exploration and reporting.
The problems that come with data science models have been found to be more related to faulty planning and communication rather than incorrect analysis, wrong codes or bugs. Hence there is a necessity to improve the entire workflow process described above.
Some of the steps to improve workflow include:
Setting the correct objective– Machine learning algorithms do find the right solution but they do not reflect correct prioritisation. So Data scientists need to periodically check whether the objective function is aligned with the client’s priorities. For instance, a new company may prioritise its primary objective to revenue maximisation in order to increase market share rather than aim for profitability. Data scientists need to focus on improving business metrics rather than model metrics
Getting on the same wavelength– Units of analysis need to be standardised between data scientists and the end-users so that each is able to understand the other’s language and time need to be wasted in translating machine and business language and priorities.
Allowing room and time– Data science is a research-based activity and unexpected breakthroughs can come from data sources least expected. Demographics and event-based behavioural data are more likely to give more precise indications of what will sell.
Keeping customers in the loop– Data scientists need to talk to consumers frequently to ascertain their priorities are in match with the models they are developing.
& Keeping solutions as simple as possible.
How To Improve Data Science Workflow? the article explained well that Data Science is a new concept and there are few textbooks or even a regular curriculum in technical institutes. Data Science has to be managed effectively and improved. More data scientists to be made available or the developing AutoML systems could phase out data scientists altogether.
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