What Skills are Required
to Start a Data Science Course?
Are you looking to start training in data science, but aren't sure if you have the necessary skills? Discover all the skills you need to join such a course!
For several years, data science has been a rapidly growing field offering many exciting professional opportunities.
A course can allow you to start your career in this future-oriented sector, allowing you to acquire a technical expertise highly sought after by companies.
According to the U.S. Bureau of Labor Statistics, the number of job offers for these professionals is expected to increase by 36% until 2031. This is much higher than the average for all professions, which is only 5%.
In addition, according to Glassdoor, the average salary for a data scientist exceeds $100,000 annually. This profession therefore allows for a high salary.
However, to be able to enroll in such a course, several basic skills are indispensable, especially personal qualities. Fortunately, some data science courses include projects to practice soft and hard skills. Here is the knowledge you need to have before starting your training.
Understanding of mathematics and statistics
First and foremost, data science is based on a solid mathematical foundation. Therefore, a deep understanding of mathematics and statistics is essential to succeed in this field.
According to a study conducted by Burtch Works in 2020, 88% of data science professionals had a graduate degree in mathematics, statistics, computer science or other related fields.
To be able to start a course, you must have knowledge in linear algebra, differential and integral calculus to understand the underlying concepts of data science.
Similarly, skills in probability and statistics are necessary to analyze and interpret data. Statistical techniques are used by data scientists to explore data, perform hypothesis tests, build predictive models, and much more. You must master concepts of distributions, statistical tests, and regression.
In addition, tools such as Python, R or Matlab offer advanced features for data manipulation, statistical computing, and mathematical modeling. It is therefore important to know how to handle them.
Programming and data manipulation
Computer programming is at the heart of data science. It allows us to manipulate data sets and extract meaningful information.
A student in data science must absolutely master at least one programming language. The most commonly used in this field are Python and R, offering a multitude of specific libraries and packages facilitating the use of data, model construction, and visualization of results.
The survey conducted by O'Reilly Media in 2019 reveals that Python is the most popular programming language among data science practitioners, as 66% of them report using it regularly.
In the age of Big Data, it is crucial to be able to work with large amounts of data. This implies understanding the most common formats, and techniques such as data cleaning and transformation or extraction of relevant features.
Moreover, knowledge of SQL (Structured Query Language) is important for interacting with databases and performing queries to extract useful information.
Basic knowledge in machine learning
Machine learning is one of the pillars of data science. It is the ability to develop models and algorithms that allow machines to learn from data.
To start training and succeed brilliantly, a basic understanding is required. You must understand the different types of problems solved by ML such as classification, regression, and clustering.
In 2020, a study conducted by KDNuggets proved that supervised machine learning algorithms, such as logistic regression, random forests, and neural networks are the most commonly used by data scientists.
A deep understanding of model training and evaluation techniques is also necessary.
Beyond theory, you will need practical experience with popular machine learning libraries such as scikit-learn, TensorFlow, and PyTorch. Knowing these libraries and applying them to real problems is a valuable skill.
Visualization and communication of results
A data scientist must not only be able to analyze data: they must be able to communicate their results to non-technical people by graphically representing complex information.
To follow a course, it is therefore necessary to have the ability to create clear and informative graphics and visualizations in order to simplify the presentation for stakeholders.
Tools such as Matplotlib, ggplot or Tableau can be used to create these data visualizations. A talent for communication and teaching is also a real asset, in order to translate technical concepts into accessible terms.
Curiosity and critical thinking
Data science is not only based on technical skills, but also on soft skills such as curiosity and critical thinking. A good data science course will train critical thinking skills through projects.
Faced with the rapid pace at which technologies and trends evolve in this field, it is essential to remain constantly curious and ready to explore new problems and concepts.
Critical thinking allows questioning results and methods, and going beyond conventional approaches to search for improvements and innovative solutions.
Collaborative skills are a cornerstone of success in the field of data science. As the data landscape becomes increasingly complex, professionals in this domain must seamlessly collaborate with diverse teams to extract meaningful insights from data and translate them into actionable strategies.
Effective collaboration goes beyond technical prowess and requires clear communication, empathy, and the ability to work harmoniously with colleagues from various backgrounds.
Data scientists often work alongside domain experts, analysts, engineers, and business stakeholders. Thus, the ability to understand and integrate different perspectives is paramount to achieving project success.
Collaboration also involves bridging the gap between technical jargon and non-technical stakeholders. Data scientists must skillfully communicate their findings, methodologies, and implications in a manner that is easily comprehensible to decision-makers. This ensures that the insights derived from data analysis inform strategic choices across the organization.
Furthermore, collaborative skills extend to teamwork within the data science team itself. Sharing knowledge, brainstorming solutions, and providing constructive feedback foster an environment of continuous learning and improvement.
Embracing diversity, promoting open dialogue, and appreciating the strengths each team member brings to the table form the foundation of successful collaboration within the data science landscape.
You now know all the skills you need to start a course in data science: a multidisciplinary field requiring a combination of skills in mathematics, statistics, programming, machine learning and communication.
This list of qualifications may seem intimidating, but they will allow you to progress more quickly in your curriculum, offering you the opportunity to deepen your skills and acquire new ones!
About the Author
Originally from Ukraine, Mickael Komendyak grew up in France. In early 2021, he attended a data science bootcamp at Le Wagon and joined the Datasientest team in May 2021. Mickael enjoys building deep learning models and likes to read and write data science blogs.