The most dominant traits of anyone who is passionate about data science and holds the goal of becoming a sought-after data scientist are the dedication to seek for information and an intense curiosity. As stated by DJ Patil, the Chief Data Scientist of the United States and the previous Head of Data Products at Linkedin, a “desire to go beneath the surface of a problem, find the questions at its heart, and distil them into a very clear set of hypotheses that can be tested” is a must-have trait for any aspiring data scientist.
There is no hard and fast rule that only a person with an educational background in data science would be fit to pursue data science as a career. People from a non-technical background can also shine in the data science sphere provided they put in the required amount of hard work and have immense perseverance to achieve big in the field of data science. In this article, we will list down some of the sure-shot ways that can make a non-technical person shine in data science. We will start by understanding the terms’ Data Science’ and ‘Data Scientist.’
What is Data Science?
Data science is a blend of various algorithms, tools, and machine learning principles that operate with the goal of discovering hidden patterns from raw data. It is used to make decisions and predictions by using prescriptive analysis, predictive causal analysis, and machine learning. It is used to scope out the right questions from the dataset. It is a multidisciplinary field that works at the raw level of data (structured, unstructured, or both) to make predictions, identify patterns and trends, build data models, and create more efficient machine learning algorithms.
Who is a Data Scientist?
Data scientists are responsible for gathering, analyzing, and interpreting large amounts of data. He needs large amounts of data to make inferences, develop hypotheses, and analyze customer and market trends. So, what would be the salary range offered by a data scientist in the United States? How much can a data scientist expect to earn?
Effective Steps to Become a Data Scientist
1. Upskilling Yourself With a Curated Curriculum
The first effective advice given to a novice in data science is to enroll in a well-curated course. Though there are many universities and online learning platforms offering courses in data science, it is important to learn from the best if you want your learning to make a difference to your learning path and career path. A perfect curriculum must cover concepts such as the basics of Python, data visualization, machine learning, data preprocessing, supervised and unsupervised learning, dimensionality reduction, association rules, statistics and probability, etc.
Holding a degree or certification in data science not only increases the value of your resume but also enhances your knowledge and provides you with a profound understanding of the critical concepts of data science.
2. Connecting With Mentors in the Field
Be it any field, it is difficult to navigate on your own, especially when you starting afresh. That is why having a mentor to guide you is a significant factor. This applies to people moving to data science from a non-technical background. You can also have an experienced mentor with not less than five years of experience in data science. This will help you gain networking opportunities, have a sounding board for ideas, goals, and questions, and gain valuable lessons from the people who have experienced highs and lows in their careers.
3. Attending Mock Interviews
If you are very keen on getting placed in the data science field, you must be aware of what the hiring managers are looking for. Mock interviews are a perfect way to gauge your expertise level. There are also many courses available online with career transition assistance that provide placement support to candidates.
4. Attending Data Science Events
To get immersed in the data science community, you would also need to attend physical events. You have many options today, ranging from small meetups to large-scale conferences. The three biggest conferences are KDD (Knowledge Discovery in Data Science), Strata Conference, and NIPS (Neural Information Processing Systems). These will be attended by hundreds or even thousands of industry practitioners, and often feature talks and technical tutorials on data science. The Strata Conference will touch upon the latest trends in the industry. KDD focus son the theory and knowledge of data science while NIPS focuses on the academic advances in the field.
5. Not Compromising With the Basics
A data science always swears by knowing any programming language such as R or Python and having an integral learning foundation in statistics and basic mathematics. Being familiar with the above fields will attune your intellectual capabilities to interpret and analyze data better.
Conclusion
I hope this article gave you an idea of what you need to do to get into data science. I am sure that you would have now understood that it is not necessary to be a technically sound person to become a data scientist. To become a data science expert and know more about data science certifications, check out Global Tech Council.