The profession of data science has been growing rapidly over the past several years, and this trend is expected to continue due to the momentum in the study of artificial intelligence that has resulted from a number of recent breakthroughs. As more sectors begin to appreciate data science, additional business opportunities, as well as challenges, will arise.
Data science may be learned well through various learning strategies, but one of the most interesting is to learn by doing data science challenges. In addition to preparing you for the future, learning the newest tools, techniques, algorithms, and packages in the business is a byproduct of tackling challenges in data science.
Importance Of Data Science Challenges
Data science challenges are important for the following reasons:
- To see how you stack up against the best in the field of data science,
- Experience the benefits of hands-on training.
- Get paid to accomplish what you enjoy most and expand your financial stability.
- To increase your marketability to prospective employers, you should:
- Ability to compete for top data science positions.
- Working together and connecting with others who share your interests
- Provides an opportunity to showcase your skills in front of a potential employer and acts as a stepping stone in the hiring process.
- Taking a creative tack on tough business problems can give you an edge over the competition.
- Develop assurance in your data science abilities.
Challenges Faced By Data Science Professionals
1. Preparing the data
To provide the highest quality of data for analysis, data science professionals and data scientists spend roughly 80% of their time cleaning and preparing data. However, 57% of them view it as the most tedious and time-consuming aspect of their professions. Every day, they must sort through terabytes of information from various formats, sources, functions, and platforms while keeping track of their actions in a log.
2. Use of several data sources
Data scientists may now access a plethora of information from several sources thanks to big data. A data science professional faces a formidable problem in making sense of this massive data set. Proper application of this information is key to realizing its full potential. Virtual data warehouses, which can efficiently connect data from many places via cloud-based integrated data platforms, offer a potential solution to this challenge. There will be more actionable insights and conclusions gleaned from a larger data set.
3. Protection of personal information
The protection of sensitive information has become an urgent problem. Because of the vast number of connected data sources, it is becoming increasingly vulnerable to cyberattacks. Therefore, data scientists have a hard time gaining buy-in for their use of the data due to the cloud of uncertainty and uncertainty surrounding it. Adhering to international standards for data protection is one approach to giving you peace of mind about the safety of your data. Additional security measures, such as the usage of cloud computing, could also be taken. It’s possible that machine learning may also be used to prevent cybercrime and other forms of fraud.
4. Quantity of data
Data engineers and scientists alike place a premium on model development because of the impact it can have on their work. An involved model with more critical parameters is needed for a challenging problem. The more variables in the model, the larger the data needs will be. Moreover, it is extremely difficult to find high-quality data to train such models. Unsupervised learning and algorithms also require a massive amount of input data to provide useful results.
5. Quality of data
AI, particularly deep learning algorithms, can outperform human brainpower. The problem arises when the data given to the algorithm is not well curated, despite the fact that algorithms excel at learning to do exactly what they are trained to do. The incredible speed with which machines can learn is both a strength and a weakness, but they will be limited to repeating what they have been programmed to know in terms of language. The quality of data is now crucial, and data curation will be a monumental undertaking for data science professionals.
6. Tagging the problem
When looking into a real-world issue, data scientists face the most difficult obstacle of all: determining what the problem actually is. They need to be able to interpret the data and also translate it so that the average person can grasp it. The study should help eliminate the most significant problems plaguing the company. For data visualization, data science professionals may use the many tools available in dashboard software.
7. Creating a pathway for data
Terabytes of unstructured data created from a variety of sources have replaced megabytes as the primary unit of data management in the current day. This information is extremely extensive, well beyond the capacity of current processing methods.
8. Grasping the business issue
Data engineers need to have a firm grasp of the business issue at hand before they can effectively analyse data and construct a solution. Most data engineers take a slow approach to this challenge, jumping straight into data analysis without first articulating the business issue or goal.
9. Prediction
Unexpected outcomes are possible in data science, and the final findings drawn may or may not be correct. Experts in data science should persevere in supervised learning for more research, model selection, and algorithm selection in the face of such difficulties. If given enough resources, data science professionals can produce highly accurate, but otherwise unintelligible, predictive models.
10. Communicating the results
Most firm managers and shareholders don’t understand how the models work or what technologies are used to build them. Crucial business choices must be made in front of charts and graphs or based on the results provided by a data scientist. Since those in charge will have trouble understanding the results if they are communicated in technical terms, doing so won’t help much.
