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What Does It Take To Excel As A Data Scientist?

What Does It Take To Excel As A Data Scientist

Data science is a new specialization in current computer-based careers. As computers generate data in huge quantities, you have to segregate valuable information from that data and discard irrelevant bits. To do that, you need the help of a Data Analyst or Data Scientist. So people with patience, intelligence, and persistence find themselves as a perfect fit in this career. You can find more information on the qualifications needed to enter this lucrative career.

What Does It Take To Excel As A Data Scientist

Many corporations like Amazon, Google, and Facebook need such experts in analyzing big data from all over the world. Moreover, these scientists are required to make sense of various statistics related to census, astronomy, economics, budget, and human behavior. Therefore, this can be considered a bright career which is in high demand with inflated salary packages. If you cannot afford classroom courses because of your current employment, you can opt for online degrees which take 24 to 32 months with 12 to 16 subjects.

In some universities like JCU, it is not essential to be a graduate. Undergraduates can also become data scientists. In JCU, one subject is taught at a time, with six subjects a year in total. Even if you have completed an online course, you can fetch an approximate salary to the tune of $31,000 to $178,000 according to your skills and experience.

Data analysis is a critical job. A data analyst needs to be a mathematician, computer programmer, visualizer, and statistician all at the same time. You will have to be meticulous, intelligent, and patient. You should also be an expert in time management, prioritizing tasks, focused on your goals, and possess a collaborative attitude. Secondly, you should be ready to accept and acquire such qualities if you don’t have them already.

Data science requires you to persist for hours without giving up. Online courses tend to be distractive because you work as per your convenience. However, this convenience can be quite harmful to this course. You will have to create a schedule of study and adhere to it since you aspire to become a data scientist.

If you are willing to grow, you must be prepared to acquire these qualities. This is because you would be scrutinizing a considerable amount of data, extract relevant information, and analyze it with respect to given statistics. Data mining also requires you to visualize certain aspects beforehand. Analysis of data needs expertise in mathematics, primarily linear algebra and statistics. You should know what data reveals and calculate accordingly. An added advantage can be secured by learning the concept of machine learning for big data.

Learning programming languages like Python and R can enable you to use the processing power of computers for analyzing data. There are many online courses which teach you various programming languages, such as MOOC’s, Coursera, Udemy, and LinkedIn Learning, among others. Data storage devices and databases are essential for data science. It would be highly beneficial if you learnt how to access them and use them in your daily tasks.

The next step would be to learn data munging and data cleaning. In this process, raw data is converted into a systematic, readable form to be cleaned of the redundant data with wiping operations. After collating the right data, reporting it in an easy to read format enables your big bosses to go through it without hassle.

To become an expert, you will have to practice handling diverse data frequently. So, have a good social community. Read a lot about various industries, blogs, stay in touch with your network, and be attentive of current events via news.

Lastly, a data scientist has to analyze data according to demand. For this, they have to face the challenge of mining hidden data. This is not laboratory work. You will have to be social enough to connect with a variety of people, understand their views, and have practical communication skills along with an ability to lead to become a productive team member.

Data science is not exactly rocket science, but it does tend to be intimidating enough to appear as complicated. This stream takes a lot of preparation, but it is a gift which keeps on giving if you invest enough efforts.

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