data science is the most promising field in near future, with the advancement of technology and statistical models in recent times, a new data wave is knocking at our doors for a complete revolution. It relates to an interdisciplinary field of study that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. As diverse does this field sounds, its team also has to be diverse enough to carry out tasks efficiently! To understand this in a better way let’s follow the pipeline for a data science project.
The most important aspect of this job is to Understand the Business Problemat the beginning, in the meeting with clients, a data science professional asks relevant questions, understands and defines objectives for the problem that needs to be tackled. Asking various questions in order to understand the project. in a better way is one of the many traits of a good data scientist. Now they care up for Data Acquisition to gather and scrape data from multiple sources like web servers, logs, databases, APIs and online repositories and finding the right data takes both time and effort.
After data is gathered next comes Data Preparation which involves data cleaning and data transformation. Data Cleaning is the most time-consuming process as it involves handling many complex scenarios like dealing with inconsistent datatypes, misspelt attributes, missing and duplicate values and many more things. Then data is modified in the transformation step based on the mapping rule, in a project ETL tools are used to perform complex transformations that help the team to understand the data structure in a better way.
Then to understand what can be actually done with the data is very crucial and for the sameExploratory Data Analysis is being applied. With the help of EDA, defining and selection of feature variables that will be used in model development is done. Next is the core activity of a data science project which is Data Modelling. Various machine learning techniques are being applied here such as KNN, Naive Bayes, Decision Tree, Support Vector Machine, etc to the data. in order to identify the model that best fits the business model. Next, the model is trained on the training dataset and testing is done to select the best performing model. Various computer languages such as Python, R, SAS etc are used by the team to model the data.
Now come the trickiest part Visualisation and Communicationin which the team meets the clients again to communicate the business findings in a simple and effective manner to convince the stakeholders, in which tools such as Tableau, Power BI, Qlik view, etc are used which can help to create powerful reports and dashboards. And finally, the model is being deployed and maintained. The selected model is tested in a pre-production environment before deploying it in a production environment and after successful deployment, the team uses dashboards and reports to get real-time analytics. Further, the team also monitors and maintains the project’s performance and this is how a data science project is completed!
Hence, Building and structuring of a good team here is very essential to meet the business need of an organisation. It is not very surprising to state that data science isn’t a single field. It is actually three different jobs with people working together to produce the final answers.
