Hi,i am writing blogs for our platform House of Bots on Artificial Intelligence, Machine Learning, Chatbots, Automation etc after completing my MBA degree.
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Hi,i am writing blogs for our platform House of Bots on Artificial Intelligence, Machine Learning, Chatbots, Automation etc after completing my MBA degree.
Data science is ubiquitous and is broadening its branches all over the world. The invisible hand of data science in the form of ranking algorithm governs the news streams and feed, recommendation engines that guide the content we see on Netflix and YouTube. Similarly, survival analyses for the estimation of time queues and neural networks for self-driving cars. But there includes a lot of challenges which hinders a data scientist while dealing with data.
Some of the major obstacles faced by data scientists:
Identifying the Issue:
The hardest challenge faced by data scientist while examining a real-time problem is to identify the issue. They have to not only understand the data but also make it readable for the common man. The insights from the analysis should remove the major glitches and hiccups in the business. Data scientists can use dashboard software which offers an array of visualization widgets for making the data meaningful.
Data Quality:
Machine learning and deep learning algorithms can beat human intelligence. Algorithms are exemplary at learning to do exactly what they are taught to do but the problem occurs when data given is poorly curated. For example, Microsoft's Tay, chatbot learned about tweets on the internet and ultimately ended up chaotic. Machine language is a boon and a bane, they have the immense power to learn things so rapidly but they will be able to reproduce only what they have been told. Henceforth data quality is of prime importance and data scientist will have the herculean task to curate data.
Data Quantity:
For a data scientist, a development of a powerful model is of top priority. A complicated problem requires an intense model with more crucial model parameters. However, more the model parameters more the data requirement. Also, it is quite challenging to find quality data to train such models. Even unsupervised learning or algorithms demand a huge amount of data to form a meaningful output.
Multiple Data Sources:
Big data allows data scientist to reach the vast and wide range of data from various platforms and software. But handling such a huge data poses a challenge to the data scientist. This data will be most useful when it is utilized properly. To an extent, this problem could be solved with the help of virtual data warehouses which can effectively connect data from enumerable locations using cloud-based integrated data platforms. The deeper the reach of data the more useful insights and conclusions.
Prediction:
Sometimes in data science, unexpected results may be obtained which may or may not be the end with the rightful conclusions. In such a challenging situation, a data scientist should press on supervised learning for future exploration, model selection and appropriate selection of algorithm. With sufficient time and power, a data scientist can generate models of predictive strength having little interpretation.
Communicating with Real People:
These days, data scientists must do more than understand their data they need to make their data understood by others. The results of their work are used to resolve business problems, create an efficient supply chain, automate of operations, nourish customer relationships, launch revenue lines, and establish strategic competitive advantages.
Communication becomes automated with the ability to distribute results, reports and performance indicators to chosen groups or users. Notifications and alerts are set up according to predetermined conditions. As the business model embraces collaboration, these tools are essential to business-wide communication.
Conclusion:
In the journey of data science and machine learning, data scientists face many obstacles. One should never compromise on quality over the quantity of data. The recommended solution would be:
Make a dataset using Mechanical Turk only if the problem is specific
Clustering the data in a natural way and collectively labeling them
Use of data archives which have been properly collected (UI machine learning library)
Also, data scientists can create meta-algorithms that can help data from other similar but different datasets. Another option is to cluster, adapt and map different data types and data sets in an unsupervised manner.