Key takeaways
- Data scientists are crucial for organisations in today's data-driven landscape, leading to high demand for skilled professionals.
- There is a significant gap between the supply of qualified data scientists and the growing market demand, exacerbated by limited educational opportunities.
- While many aspirants turn to costly online courses, it is possible to acquire data science skills for free without formal certification.
- Becoming a data scientist requires familiarity with programming, mathematics, and various tools, which can take at least six months to master.
- A well-structured learning roadmap is essential to prevent burnout while acquiring the necessary skills for a career in data science.
Data scientist are eternal to the success of any organisations in this current data-driven world, where there is still a colossal amount of data to be analysed. Today, every company strives to acquire professionals who are proficient in data science. However, becoming a data scientist comes with its fair share of challenges.
Difficulties In The Path To Becoming A Data Scientist
Firstly, there are very few colleges that offer data science course, thus there is a huge demand and supply gap in the current market. Secondly, the course fee is too high, thereby making it unaffordable to many aspirants. Data science aspirants are opting for paid online courses for streamlining their learning to enhance their skills. However, if you are willing to gain the skills of a data scientist, you can do it for free as well. The only tradeoff is you will not be certified by any institutes.Skills To Learn
To become a data scientist, one needs to be familiar with a wide range of topics and tools while avoiding burnouts. It often takes at least 6 months to learn and expertise the skill required to become a data scientist. This can be daunting for many, thus it is paramount to devise a roadmap for minimising the burnouts. Here are the most in-demand skills for a data scientist:- Programming
- SQL
- Python
- R
- Mathematics
- Linear Algebra
- Calculus
- Probability and Statistics
- Basic and advanced probability
- Descriptive and Inferential statistics
- A/B Testing
- Data Analytics Skills
- Data wrangling
- Visualization
- Technical writing
- Machine Learning
- Supervised learning
- Unsupervised learning
- Reinforcement learning
- Other Data Science Tools




