From stand-up calls, iterative developments, collaboration between internal teams and cross functional teams, agile practices make software development more efficient and productive. Delivering a working software with limited scope initially and incremented later on will satisfy the customer.
The development and operations team work combined to streamline the process followed throughout the project. Various tools are employed for version controlling, code repository, CI/CD pipeline and process automation.
With lots and lots of packages in python, more can be done with less code. Through python learning, one can have in-depth understanding of Data science(AI/ML), Data Engineering and Data Analytics.
Using a web framework for developing web applications will accelerate the process and make debugging easy. Django is such a powerful web framework for python used to build a website quickly with its modelview-template architecture.
Given the ability of organization to collect massive data, data engineering becomes a necessity to make the data transformed to a format that is accessible to data scientist and data analyst. ETL tools are used to perform the Extract, Transform and Load processes for the data.
Using statistics, the raw data is analyzed and interpreted for trends and patterns. These data patterns are then used in making strategic decisions for businesses. Many tools and techniques are used to get quick and reliable insights to data.
ML algorithms are used to build AI systems that can generate actionable insights. With these insights business values can be derived which can be an optimal solution for potential business problems.
Making decisions based on data helps to improve customer retention and helps to outperform competitions. It can be incrementally adopted as a practice when more and more data gets poured in over time.
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