Data science is everywhere and is now broadening its branches across the world. Invisible hand of the data science in a form of the ranking algorithm rules the news feed and streams, the recommendation engines guiding the content that we often see on YouTube and Netflix. Similarly, the survival analyses for estimation of the time queues and the neural networks for the self-driving cars. However, there includes many challenges that hinders the data scientist when dealing with data. Let’s go through some major obstacles that are faced by the data scientists.
Knowing the real Issue
One hardest challenge that is faced by the data scientist today when examining the real-time problem is identifying the main issue. They need to not just understand data but as well make this readable for a common man. Insights from the data analysis must remove major glitches in the business. The data scientists will use the dashboard software that gives a wide range of the visualization widgets to make this data meaningful.
Quality of Data
The machine learning and the deep learning algorithms will beat human intelligence one day. The algorithms are quite exemplary in learning to do what they’re taught to do however problem happens when the data given is very poorly curated. Machine language is one big boon and bane, they’ve an immense power of learning things very fast but they can reproduce just what they are told. Thus, data quality is highly important and the data scientist may have herculean chore to curate data.
Quantity of Data
For the data scientist, development of the powerful model is highly important. The complicated problem needs the intense model with crucial model parameters. But, if more the model parameters than more data requirement. So, it is very challenging to find the quality data for training these models. Even the unsupervised learning and algorithms demand the huge data to form the meaningful output.
Multiple Sources of Data
The big data enables the data scientist to actually reach the wide range of the data from different software and platforms. However, handling huge data poses the challenge to data scientist. The data is highly useful when it’s used rightly. To an extent, the problem can be solved with help of the virtual data warehouses that will effectively connect the data from many locations by using the cloud based integrated platforms. Deeper the data reach more useful conclusions and insights.
What are the predictions?
At times in the data science, any unexpected results might be obtained that might not be an end with rightful conclusions. In this challenging situation, data scientist must press on the supervised learning for the future exploration, appropriate choice of algorithm and model selection. With enough power and time, data scientist will generate the models of the predictive strength having very little interpretation.
There was study conducted on sample of over 16000 data scientist and concluded 10 difficult challenges that are faced in the profession. Challenges faced differ according to the job description.