With its cutting-edge technological advancements, intellectual stimulation and substantial financial incentives, data science may be a wonderfully satisfying profession of work. Nevertheless, there are many obstacles and challenges in this area, which might be demoralizing for amateurs. As an aspiring data scientist, having a well-defined plan can give you a strong benefit. Therefore, I am providing you the following comprehensive steps, based on my personal perspective and experience. I genuinely hope that this guide will be useful for those interested to start a career in data science:
1) Evaluate your aptitude for data science. The goal of data science is to get business insights from unstructured data by integrating software engineering, statistics, and machine learning. You can subscribe to news letters and learn about the latest trends and achievements in the field.
2) Start by building a strong foundation in mathematics and computer science. Data science involves working with large datasets and using algorithms to analyze and draw insights from them. Therefore, a strong background in mathematics, especially in statistics and ,linear algebra as well as computer science, is essential.
3) Gain experience with programming languages commonly used in data science, such as Python and R. These languages have powerful libraries and frameworks for data manipulation, visualization, and machine learning, and they are widely used in the industry.
4) Online courses provided by Coursera, edX, DataCamp, and Dataquest can be a good starting point for beginners. Alternatively, you can earn a degree in a field such as computer science, statistics, or mathematics, which will provide a strong foundation for a career in data science.
5) Work on personal projects to practice your skills and build your portfolio. Choose a dataset that interests you (from Kaggle or Google Dataset Search) and try to answer questions in accordance to your knowledge, or more interestingly, solve a problem using data analysis and machine learning techniques.
6) After working on your personal projects, sharing them is a key to success and promoting oneself to the employers as it demonstrates your achievements and practical skills. You can build public or private repositories for your projects using Github services.
7) Persistence matters the most. Keep yourself up to date with the latest developments in the field and never stop learning. The novel tools and techniques are being developed all the time since data science is a rapidly evolving field. Keep learning and expanding your skill set to stay competitive in the job market.
8) Be on a look out for the job listings. LinkedIn is a really good platform. Similarly, another excellent source is Glassdoor, which has a plethora of ads for jobs in data science, machine learning and other related fields. For freelancing career, Upwork, Fiverr, Freelancer , and Toptal provide a good framework.
9) Connect with people via hackathon. If you are not familiar with the idea, hackathons are competitions where teams of participants try to create a software prototype in a short period of time, usually over the course of a weekend. The winning team is then selected by the hackathon judges, and its members are given a cash prize or the chance to launch a business based on their idea.
10) Consider earning a professional certification, such as the Certified Data Scientist (CDS) Certified Data Scientist offered by the Institute for Data Science. This can help you stand out to potential employers and demonstrate your expertise in the field.
Top comments (2)
Precise and informative!
Thank you for this