Data science is an actively growing field that is very valuable to a wide range of industries. There are many opportunities options for people who want to leverage on data and derive actionable insights. From extracting data and storing it in a manner that can easily be accessed by all stakeholders, analysing data to support decision making, and using historical data to predict future performance. There is definitely a lot to do with data in any organisation. This article will focus on creating a roadmap to easily get into data science and learn all the necessary concepts.
Data science is a field that entails analysing and exploring data to extract actionable insights that inform decision making. Data science borrows a lot of concepts from statistics, computer science and mathematics. Another important aspect of data science is domain knowledge about a specific industry when applying the data in solving problems. This article [(https://dev.to/cesarwrites/the-power-of-domain-knowledge-in-building-a-successful-data-science-career-4k9a)] talks more about domain knowledge and its impact in data science. Therefore, as someone seeking to start a career in data science it is important to align your skills to the relevant disciplines.
Contrary to popular perception a masters or Ph.D degree is not a requirement to succeed in data science especially in a technical facing role. However, it is important to have understanding and experience in using the necessary tools and technologies.
R and python programming languages are important for data scientists because they contribute to exploring data, creating visualisations and statistical modelling. Python is quite popular in the data science space because it is versatile and provides many libraries that are valuable when creating data science solutions. Another important programming skill for data scientists is understanding how to create queries. MySQL is a good place to start if it is your first time learning to write SQL queries.
Statistical concepts such as hypothesis testing and regression analysis are very important for data scientists. Additionally understanding calculus, probability and linear algebra are very important as they are embedded in the machine learning algorithms applied by data scientists in building data models.
Once you have understood the programming basics and the statistical foundations of data science the next topic would be machine learning. Machine learning entails the use of algorithms to mimic human reasoning and give computers the capacity to make decisions without being explicitly programmed. Clustering and classification algorithms are popular when creating machine learning models. The popular machine learning techniques are supervised learning, unsupervised learning and reinforcement learning. Diving deeper into machine learning enables you to understand the many algorithms available and which one to select based on the problem you are trying to solve.
As mentioned before, data science is quite a wide field and in order to succeed you must be able to demonstrate your expertise in at least one specialisation. The popular career paths in data science include;
- ML engineer
- Data scientist
- Data visualization expert
- Data analyst
- Analytical engineer
- Data ecologist
At this point you have an understanding of important data science concepts and a few projects that showcase your skills. It is important to create a data science portfolio where you can show your potential employers and clients your skillsets. There are many existing platforms that enable you to build a data science portfolio starting with GitHub, Kaggle and even building your own website where you showcase your work.
As a beginner building a successful career in data science can feel intimidating. However, there are many available resources online that are even accessible freely. You only need to create your learning path, work on tangible projects and plug yourself to communities where you network with likeminded professionals. Most importantly keep learning as new techniques are continuously coming up in the dynamic data science field.