Data science combines statistics, artificial intelligence (AI), scientific methods, and data analysis. Different companies rely on data when making business decisions. Especially at this point in time where data volumes are massive and are mostly available for everyone.
Data are collected through different channels of technologies and companies thrive on storing these valuable data, but it takes an expert to be able to analyze and provide insights into these; that’s where data scientists come in. Data Scientists showcase trends and insights to help companies make an informed business decision. Through these data, companies are able to determine processes, systems, and products that work and what needs improvement.
Data scientists are one of the highest-paid IT roles in the industry. The demand for data scientists has climbed up for the past years. According to Glassdoor, the pay range of a data scientist in Ontario is from CA$70,000 to CA$116,000 and the average base pay of CA$90,855 per year.
Being a data scientist has more to it than sorting, cleaning, and understanding data; there is also a requirement in being able to apply different techniques like machine learning, artificial intelligence, and statistical modeling.
Essential Skills for a Data Scientist
Statistics is very important for a data scientist. Data-driven companies where stakeholders depend on data for decision-making heavily rely on data scientists.
With programming, you are able to manipulate data and apply algorithms to come up with insights. Data scientists do not need to be experts in the matter but should be comfortable enough working with it. Python and R are the most used programming languages by data scientists.
Visualization is an essential of data science. It is the gateway to create more meaningful information. This is also the part where data scientists tell the story based on the data they have gathered. Pie charts, histograms, heat maps, etc. can be used for your visualizations. The common tools used in this are Tableau and Power BI.
Through machine learning, you are able to build predictive models. This is helpful for organizations that manage huge amounts of data. Machine learning includes K-nearest neighbors, Random Forests, Regression Models, and so on.
Data wrangling is where data scientists clean and unify a mess and complex sets of data. This is the part where you have to understand how to deal with the imperfections in data and prepare it for further analysis.
It is always a good asset to have a software engineering background. Writing clean and good-quality codes can help you in collaborating with other team members.
Tools for Data Scientists
It is also essential for data scientists to master several tools that will enable them to efficiently work through different data sets.
Kaggle has recently conducted a survey for data science in their 2020 Kaggle Data Science and Machine Learning Survey. This shows us the top programming languages used by data scientists. Getting the top spot is Python, followed by SQL and R.
For machine learning, the most common machine learning frameworks are Scikt-learn, Tensor-Flow, and Keras.
While for cloud computing platforms, Amazon Web Services (AWS) takes the top spot then followed by Google Cloud Platform (GCP) and Microsoft Azure.