How to Boost the Data Science Project?

By Jyoti Nigania |Email | Jun 25, 2018 | 5721 Views

Unlike other projects in the organization, AI data-driven products are new for most of the organizations and the best way to go from research to production. Data science has created so much hype in the world of IT sectors that from big to small companies all are now hiring employees who have knowledge regarding this subject. The data science industry's job market is hot today. Data science is helpful for the employees to get understand about data and then make it in a proper way so that it can be communicated in a better way which is valuable for the companies. Following are the ways to boost a data science project by deploying machine learning solutions into few of the main products:

Discuss the evaluation metrics prior with the management: Before starting the project the individual should discuss with the management because they have more knowledge and ideas for accomplishing a particular project.  But sometimes they don't understand machine learning, but they know your customers as they are the domain experts. Different industries use different KPI's like ad tech people like the Click-Through Rate, in finance it will be the return on investment (ROI) and in cyber-security its negative rate. So only once have an agreement on a metric than the individual can start the project.

Get a good data set: This is obvious that we should need a good data set. The data science project brings the large number of relevant and unbiased data. If the individual accesses all the datasets then its building is not too hard or expensive. The dataset needs to represent the actual production data, and only we can check if we have significant, renewal test set that comes directly from the production data. Hence we should gather the right amount and right quality/ relevant amount of data.

Split the work between the team members: If we are working in a big team then we should split the work between the team members and create harmony among each other, this is the wise decision to get better output without any fatigue. For some scenarios of machine learning projects it is essential to divide the work among the team. 

Decouple training and prediction: During the research phase the training and the prediction occur simultaneously this field need not require any extra effort it's an ongoing process with the research. Here we need to ship the prediction model and get realize that it is coupled with the training code. And at the very beginning we need to separate the prediction or evaluation code from the training code. Hence, prediction algorithms are usually very simple and they can be implemented in diverse languages.

Data pre-processing code should run successfully: The data pre-processing code is simple and doesn't require a complicated ecosystem. It would be wise to fit the pre-processing part where it requires les resources especially when you have to run this model on client's machine. 

Build reporting infrastructure: We have to build a reporting infrastructure so that everyone could easily see the results all the time. It's better to have an automatic process that evaluates the model so that some unbiased and fair results can be obtained. 

Data recovery and cache: Its good idea to use the caching for storing the pre-processing results to speed up the things. However, we should always be able to recover the cache from the original data and never believe only on the cached findings or results.

Do research continually: Another most important point is to do the research on continuous basis to fulfill the long term goals, so we should have pragmatic plans along the short term goals. 

Hence, these following are the lifecycle of the data science project. By applying above steps in accomplishing the project one can better frame their findings. As this field is the highest paying amongst all the fields. While looking for the data science as a career one should consider all the roles and work according to their skill set on each project.

Source: HOB