Critical Areas in Data Science that Challenges Every Analyst
Tapping the knowledge from Data Science courses in Bangalore holds great promise for any analyst. However, like a majority of the data science project, the chances of succeeding with your first data science project could be minimal. Why? Designing analytical models for any data science project presents a unique challenge that can be only understood if you have mapped how your data evolves through various processes.
In this article, I have highlighted the biggest challenges that influence the success rate of data science projects.
Scalability
In Data science courses in Bangalore, you can optimize your project for scalability factors by virtue of hardware / software integration, people management, or improved processes.
Scalability in data science is a highly coveted benchmark that could mark your workmanship in high-growth domains, such as AIOps, Auto ML modeling, data intelligence, and RPA. In data science, scalability is defined as the ability of any data management architecture or process to successfully handle expanding volume of work, keeping all constraints constant. These constraints could be the types of devices used, the size of the data server, and energy levels.
Reliability
Do your data management tools deliver reliability for data exploration and deployment? Once your project clears scalability, would your data science project meet the reliability benchmarks?
The answer would astound you.
When you are designing for a data science project, reliability charts shake up the final outcome in 99% of the cases. When one node fails, the resultant outcome could take out all the succeeding computing models. As a data analyst, your model should stand the test of reliability. That’s why we are seeing a rise of the Data Reliability Engineers who are responsible for keeping databases full-proof systems through various validity of measurement using test-reset reliability, internal consistencies, and inter-rater reliability.
Speed /Agility
Distributed teams that work on Apache Kafka or Hadoop often have a hard time managing speed and responsiveness for their data models.
Speed is directly proportional to the computing clarity and responsiveness of the programming models. Python, R, and C++ are all popular programming languages that are used in data science. Yet, there are numerous critical areas in the “speed” areas alone that can be tactically improved by working with Optimization Principles listed in various Open Source ML discussions.
Besides the usual industrial and academic avenues of data science applications, we have countless other domains where we find speed agility testing could play a critical factor in delivering positive outcomes. Take augmented intelligence and data training modeling that is used in CNNS and GANs, for example.
Security, privacy, and compliance
If you are in the AI ML Data science space, you can’t ignore privacy regulations. In the last 5 years, we have witnessed numerous agencies and government-backed regulators coming down heavily on the data science organizations and ensure they use personal user data responsibly.
Coping with the above-mentioned critical challenges and finding reasonable solutions to meet these requirements would land you decent projects from top ranking organizations.
