Data science is the use of various tools, machine learning principles and algorithms with a goal to discover the hidden patterns from the raw data. It is a multidisciplinary blend of various data inference mechanism and technology to solve analytically complex problems. Domain knowledge is very important in data science course. You can accelerate your career in data science by gaining a sound knowledge of data management, machine learning, statistics, etc. In this write up the importance of different concepts in data science would be discussed briefly.
- Data Management
The raw data is always unstructured and messy. Here the data comes from disparate data sources, mismatched or missing records, and a slew of other tricky issues. Data management is the professional term to describe data wrangling. It involves bringing the data together into cohesive views, cleaning up the data so that it is polished and becomes ready for downstream usage.
Data management requires a sound knowledge of pattern-recognition and clever hacking skills. This help to merge and transform masses of database-level information. If data management is not done properly the dirty data can completely mislead the results. So a good data science aspirant must possess the ability to nimble the data in order to have accurate, usable data before applying more sophisticated analytical tactics.
- Machine Learning
Machine learning is closely associated with data science. It involves two broad class of methods; 1) algorithmically make predictions, and 2) algorithmically decipher patterns in data.
- Machine learning for making predictions
The core concept in this process is to use tagged data to train predictive models. In case of tagged data, the ground truth is already known to the data scientist. Training models signify automatically characterising the tagged data in such a way that it helps to predict tags for unknown data points.
- Machine learning for pattern discovery
This is also known as unsupervised learning. Here the underlying patterns and associations in data are deciphered when no existing ground truth is known. There are many subgroups within this broad category. The most common one among them is clustering techniques. Here the natural groupings which exist in a data set are detected algorithmically.
- Mathematics Expertise
Mathematical expertise is very important in data science. It is the ability to observe data through a quantitative lens. It helps in the data mining process and building data products. The solution to many business problems can be achieved by building analytic models which have their base in hard maths. Here the ability to understand the underlying mechanics is the key to success in building those models.
- Strong Business Acumen
It is very important for a data scientist to have good knowledge of the tactical business. He has the ability to translate the observation to shared knowledge, which makes it possible to solve core business problem. Since the data scientist works very closely with data, he can achieve a lot of understanding from analysing data. Having strong business acumen is as important as possessing sound knowledge in machine learning and data management. There has to be a clear alignment between the data science work and the business goals of the project.
A good knowledge of statistics is very important to be a good data scientist. You should have a sound knowledge of the use of statistical tests, maximum likelihood estimators, distributions, etc. You should have a clear understanding of the different techniques and approach of statistics.
Finally, it can be said that the value of a successful data scientist does not come only from data, math, and tech itself. It comes from leveraging all of the above-mentioned fields to build valuable capabilities and have strong business influence. The importance of domain knowledge in data science course is huge. If you want detailed information then please log in to our website at https://upgrad.com/data-science/.