The field of data science is multidisciplinary and covers many areas from mathematics to machine learning. Data scientists work on new ways of capturing and analyzing data with the help of various technical and scientific methods. Since it crosses between technology, math and science, data science is regularly involved in new discoveries in different sectors.
Data scientists are relied on by the society to provide scientific-based insights that will inform and improve services and processes. Data scientists can work for the government, R&D, private sector or in academia.
Make a List Of Online Learning Tools and Resources
The field of data science is very broad. Not just that, it is also an ever-changing field as the technology continues to evolve. It is vital that you don’t allow yourself to become overwhelmed by the industry’s pace. You should always strive to keep on with your learning goals. You can do this by keeping a list and tracking your progress. This will undoubtedly motivate you. Whether your goal is to improve the programming skills you already have or you want to increase your knowledge of statistics, try to keep track of your progress.
Learn Some Programming Skills
When we think of coding, our mind first goes to developers and software engineers. However, data science actually relies on programming and a lot of data scientists need knowledge of Python, R, C++, SQL, Hadoop and Tableau. A lot of data scientists actually perform their tasks in data science with python. It is also advisable to gain basic knowledge of machine learning since data science sometimes overlaps with it.
Exercise Patience
“Patience is a virtue” might be a popular saying, but not everyone is naturally patient. It can be very frustrating working with complex data. Even people who enjoy puzzles will sometimes have their limits tested.
Instead of becoming frustrated and throwing in the towel, you should take a breather. After some time, go back to the issue with a clear head. If you remain stuck, try asking for help from a colleague. More often than not, a fresh pair of eyes can be helpful in spotting problems you may have overlooked or providing solutions you may not have thought of.
Communicate Your Ideas
Even though data science is majorly focused on tech and mathematics, it is important that you don’t neglect the human aspect of this role. After all, it is an applied science. If you communicate in a comprehensive manner, your value will increase and you will become a vital part of your work. Attending events and joining hackathons will help to give you confidence (and an avenue) to discuss your work.
Machine Learning and AI
Even though data science and AI normally go together, a lot of data scientists find themselves not being proficient in Machine Learning. However, this field includes working with data sets that will need Machine Learning techniques like logistic regression, decision trees, and supervised machine learning. These skills will be helpful in solving different data science issues based on predictions of organizational outcomes. For beginners, machine learning doesn’t need that much math or programming knowledge – you mostly need motivation and interest.
