Estimated reading time: 6 minutes
What skills does a data scientist need? This is a question anyone looking to become a data scientist will ask. A data scientist needs a broad range of skills to perform their role. A data scientist role is to interpret data, validate the meaning of data, and use data to drive an outcome. Validating the meaning of data and using data to drive an outcome will differ depending on the industry. For example, a sales data scenario might mean understanding customer buying patterns better (validate the meaning) to increase sales to new and existing customers (the outcome). It might be to identify unusual trading patterns in financial transactions and then define the rules to block abnormal transactions. For online dating services, a data scientist’s role may be to understand what shared characteristics members that successfully match have to recommend better matches. Regardless of the business domain, a data scientist works in, they need specific fundamental skills.
A successful data scientist needs the following skills:
Table of contents
Communication and presentation skills
One of the most critical skills a data scientist needs is excellent communication and presentation skills. Many business professionals who run organisations and employ data scientists don’t understand the technicalities of what a data scientist does. A data scientist must be conscious of this, communicate clearly and simplify complex concepts. On the other hand, business professionals understand their business very well. They are often the subject matter experts a data scientist will work alongside. A good data scientist needs to bridge the data science and business worlds.
Statistics
Statistics and statistical analysis are cornerstone skills for a data scientist. Statistics which is described as “the discipline that concerns the collection, organisation, analysis, interpretation, and presentation of data”, allows a data scientist to make sense of the data they are working with. Data scientists use statistical techniques for correlation analysis, clustering analysis and time series analysis, along with a wide range of statistical tests. Understanding statistically significant relationships between variables (data points) enable a data scientist to draw valid conclusions from the data.
Machine Learning
A data scientist creates machine learning models to make predictions. There are two significant classifications of machine learning models that a data scientist will use, supervised and unsupervised. Within these classifications, there are very well understood machine learning models that a data scientist can use depending on the problem to be solved. A data scientist will need to be familiar with each of the most common machine learning models. They will need to know the best model to use for a particular scenario. Additionally, they will need to understand the hyperparameters used to tune the model and increase the accuracy.
As you can see, expert knowledge is required when working with the range of models involved in machine learning. It’s widely recognised that there is a shortage of data scientists. To address this issue and increase the adoption of machine learning, there have been significant advancements in automating the entire machine learning process. Many new products are available that automate and attempt to simplify machine learning and make it easier for data scientists and non-data scientists. AutoML provides techniques and methods for non-data scientists to work with machine learning. AutoML is also very helpful for data scientists as it speeds up working with machine learning models.
Database and SQL skills
Understanding databases and how to work with databases is an essential skill that every data scientist should know at a minimum. Databases are used to store data in a structured manner to be used by an application, reported on by a BI tool or interpreted by data scientists. Standard Query Language (SQL) is the language that is used to work with data in a database. The SQL language allows you to query the tables and objects within a database and return the results of that query. Even knowing the basics of SQL is a significant advantage when working with data. A data scientist must be very familiar with the leading databases’ aggregate and analytic SQL functions. These functions make such calculations as standard deviation, ranking, median and others very straightforward to calculate in SQL.
Programming Skills
Programming skills are essential skills for data scientists. Python is by far the leading programming language for data scientists. Phyton had long been an excellent programming language for working with data and files, long before the term “data scientists” was invented. It has since become the de facto language for data science. Phyton comes with extensive support for working with data, and many of the leading data science libraries are implemented in Python. Taking the time to learn Python is a very worthwhile exercise. It is an essential tool in any data scientists’ toolbox.
Project management
Project management skills are an asset to anyone working with data, regardless of their role. Data scientists will work closely with project managers on complex projects. Understanding project management and agile are excellent skills to help every data scientist’s career. Data science projects can be very exploratory based projects. Exploration naturally lends itself to going down different avenues as part of the exploration process. This can be extremely interesting to data scientists, but they must understand where they are in a project and what deliverables are due at each milestone. Data science projects in business need to deliver to the organisation.
Cloud
If you are a permanent data scientist within an organisation, you’ll need to become familiar with the organisation’s cloud platform’s data and machine learning offerings. AWS, Azure and Google Cloud offer data, machine learning and AI services. If you are a freelance data scientist, you may need to be familiar with offerings from more than one cloud platform. Data tools and ML/AI services for all stages of data science-related activities are constantly evolving and maturing. Staying up to date and aware of new developments is very important as this area of the cloud is moving very fast. Familiarity with cloud security is also highly recommended.
Data Security
Data security is a critical aspect of working with data. Data scientists will have access to sensitive competitive information on how an organisation performs. The responsibility of data security firmly relies on an organisation to manage. In addition, a data scientist should ensure any data they work with is treated sensitively. Data should be anonymised where appropriate. Datasets should only be shared internally with authorised employees. Findings and insights should be treated with care as these may be extremely valuable and strategic for the organisation.
Other data skills
There are other data-related skills that a data scientist may need to be familiar with, depending on the size of the organisations they work with. A larger organisation will employ data engineers, BI developers, machine learning engineers and report developers. Smaller organisations may not have the luxury of all these roles, and a data scientist may need to wear different hats.
Data warehousing
Data science experiments or ML model outputs may need to be stored in a data warehouse to enrich the existing data.
Data Engineering
Data engineering skills will be needed to make datasets available for data science projects.
Machine Learning Engineering
Productionising machine learning models is a complex activity and requires specialist ML and cloud skills.
