In the age of data-driven business, having a data scientist within your company is crucial. By 2025 alone, there will be a shortage of 800,000 employees working in European companies who are skilled in data science – because data science is huge. From data analysis to machine learning and customer contact – the scope of responsibilities is vast. This makes the job role of a data scientist an allround talent, because they are involved in almost every step of a data project. So, it is not very surprising that a data scientist needs to have a wide range of skills.
Do you want to become a data scientist, or are you already working in a data role? In this blog article we will give you an overview of the five most important skills every data scientist needs to have to be successful in their job.
Basically, you can divide the data scientist skills into two categories: the hard skills like technical skills, and the soft skills which are social and communicative skills. We will explain both categories to you.
The hard skills of a data scientist
Hard skills are mainly the technical qualifications which are typical for the profession. For a data scientist these are the necessary skills of understanding and applying machine learning algorithms. Your mathematical skills are the basis for this. Let’s take a closer look at them.
1 Mathematical skills
Mathematics is the ultimate basis for you to generate value from your data. By using your mathematical skills, you analyze data, write algorithms and validate the results. The following three areas of mathematics are especially relevant: Statistics, linear algebra and analysis.
As a data scientist, you should be able to explain and apply the following terms blindfolded:
- Mean, median, mode
- Standard deviation, mean absolute deviation from median
- Variance, interquartile range
- Normal distribution, histogram, boxplot
- Correlation, covariance
- Multiplication, transposition of a matrix or a vector
- Determinant and inversion of a matrix
- Intrinsic values, intrinsic vectors and singularity values of a matrix
- Derivatives, gradient, chain rule, product rule
- Zero points, extreme values, saddle points
- Statistical testing, p-test, t-test, AB-test
- Gradient method, convergence, divergence
- Classification, regression
- Bayes Theorem
- Linear regression, logistic regression, decision tree
- Random forest, support vector machine, neural network
- principal component analysis, singular value decomposition
- Recall, precision, sensitivity, F-score
- Euclidean distance, p-norm
- Coefficient of determination (R² – value)
In general, you can never do too much math. As a data scientist, you should understand the above list as basic knowledge. Besides mathematical skills, programming skills are also hard skills that you should master.
2 Programming skills
Enormous amounts of data and the complexity of modern algorithms make computers indispensable for every data scientist. Besides a rough understanding of the computer hardware (CPU, GPU or RAM), as a data scientist you must have a passion for programming.
There’s no doubt: The programming language Python needs to be in every data scientist’s repertoire. In almost all cases, you only need to know Python 3. More rarely, skills in C, Scala or Julia are necessary.
Python is so popular mainly for these reasons:
- Python is very easy to learn and write.
- Python is the second most popular programming language in the world (as of November 2020). For data science, Python is the most popular language. So, there is a large community that makes Python more and more powerful.
- There is a huge number of data science libraries. These allow calculations to be executed in C and using GPUs to guarantee high speed.
As a data scientist you should have a good knowledge of the following Python libraries:
Data processing:
- Numpy
- Pandas
- PySpark
Machine learning:
- Scikit-learn
- TensorFlow and Keras
- PyTorch
Visualization:
- Matplotlib
- Plotly
- Seaborn
Just as with mathematical skills, this list should be your base. Again, it says: You can never know too much!
We know that this is a lot of things to keep in mind. You have to apply mathematics and programming regularly in everyday life to be able to perform all the processes in a project. Let’s take a look at the skills you need to implement processes.
3 Process management
To successfully manage projects and get the most out of your data, you need extensive skills in data preparation, creating machine learning models and writing SQL queries for databases. We’ll explain to you exactly which skills you need:
Data preparation:
- Encoding categorical data
- Feature engineering
- Dealing with missing values
Machine learning:
- Overfitting and underfitting
- Hyperparameter optimization
- Selecting algorithms depending on the situation
Databases:
- Writing SQL queries
- Connecting relational tables
- Using structured and unstructured data
Deployment:
- Integrating algorithms in IT infrastructures
- Cloud computing
- Continuous deployment
But technical skills alone are not enough to be successful as a data scientist. You also need soft skills that complete your profile. Let’s take a closer look at these.
The soft skills of a data scientist
As a data scientist you also have to be well-versed in soft skills. These often whether a project succeeds or fails. You need to be able to communicate with colleagues, customers or decision makers in a target group-oriented way and to integrate their wishes into your algorithms and processes. By all means as a data scientist you need to develop a thorough domain knowledge. You act as a connector between product and abstract technology. So, your communication skills should be a priority – or in data science terms: data storytelling.
4 Data storytelling
Data storytelling is a collection of different techniques and methods to convey complex, data-driven results to non-experts. As a data scientist, you use findings from cognitive sciences. On the one hand, it is about creating a story from your data – the data story. Stories are easy to understand and stick in the mind of the listener. On the other hand, explanatory visualizations play a major role.
These are graphs that use colors and shapes to direct the viewer’s attention. It allows you as a data scientist to be a connection between experts and decision makers. Unfortunately, data storytelling is a neglected skill and difficult to master. In general, soft skills require a lot of experience.
Besides data storytelling, project management skills are very important. Agile project management in particular has established itself in data science projects.
5 Agile working
The methodology of agile working is based on various best practices collected over the years. Agile working has its origin in software development. In practice, this means delivering products quickly and developing them in iterative feedback loops. As a result, companies no longer bring a finished, perfect product to market, but often a beta version first, which is tested and optimized. In data science projects, it is often impossible to predict which challenges you will face and whether the planned solutions are feasible. This unpredictability is the reason why agile working has become widely accepted.
These are the top five skills that every data scientist needs to have. We hope this blog article has brought you new insights. Last but not least, we would like to point out one additional skill: As a data scientist you must enjoy your work, because you need to constantly develop yourself and learn new things. Knowledge is power and it’s constantly evolving. This should also apply to you and your skills!
Get to know StackFuel’s online education and training to take your data skills to the next level.
In the age of data-driven business, having a data scientist within your company is crucial. By 2025 alone, there will be a shortage of 800,000 employees working in European companies who are skilled in data science – because data science is huge. From data analysis to machine learning and customer contact – the scope of responsibilities is vast. This makes the job role of a data scientist an all-round talent, because they are involved in almost every step of a data project. So, it is not very surprising that a data scientist needs to have a wide range of skills.
Do you want to become a data scientist, or are you already working in a data role? In this blog article we will give you an overview of the five most important skills every data scientist needs to have to be successful in their job.
Basically, you can divide the data scientist skills into two categories: the hard skills like technical skills, and the soft skills which are social and communicative skills. We will explain both categories to you.
The hard skills of a data scientist
Hard skills are mainly the technical qualifications which are typical for the profession. For a data scientist these are the necessary skills of understanding and applying machine learning algorithms. Your mathematical skills form the basis for this. Let’s take a closer look at them.
1 Mathematical skills
Mathematics is the ultimate basis for you to generate value from your data. By using your mathematical skills, you analyze data, write algorithms and validate the results. The following three areas of mathematics are especially relevant: Statistics, linear algebra and analysis.
As a data scientist, you should be able to explain and apply the following terms blindfolded:
- Mean, median, mode
- Standard deviation, mean absolute deviation from median
- Variance, interquartile range
- Normal distribution, histogram, boxplot
- Correlation, covariance
- Multiplication, transposition of a matrix or a vector
- Determinant and inversion of a matrix
- Intrinsic values, intrinsic vectors and singularity values of a matrix
- Derivatives, gradient, chain rule, product rule
- Zero points, extreme values, saddle points
- Statistical testing, p-test, t-test, AB-test
- Gradient method, convergence, divergence
- Classification, regression
- Bayes Theorem
- Linear regression, logistic regression, decision tree
- Random forest, support vector machine, neural network
- Principal component analysis, singular value decomposition
- Recall, precision, sensitivity, F-score
- Euclidean distance, p-norm
- Coefficient of determination (R² – value)
In general, you can never do too much math. As a data scientist, you should understand the above list as basic knowledge. Besides mathematical skills, programming skills are also hard skills that you should master.
2 Programming skills
Enormous amounts of data and the complexity of modern algorithms make computers indispensable for every data scientist. Besides a rough understanding of the computer hardware (CPU, GPU or RAM), as a data scientist you must have a passion for programming.
There’s no doubt: The programming language Python needs to be in every data scientist’s repertoire. In almost all cases, you only need to know Python 3. More rarely, skills in C, Scala or Julia are necessary.
Python is so popular mainly for these reasons:
- Python is very easy to learn and write.
- Python is the second most popular programming language in the world (as of November 2020). For data science, Python is the most popular language. So, there is a large community that makes Python more and more powerful.
- There is a huge number of data science libraries. These allow calculations to be executed in C and using GPUs to guarantee high speed.
As a data scientist you should have a good knowledge of the following Python libraries:
Data processing:
- Numpy
- Pandas
- PySpark
Machine learning:
- Scikit-learn
- TensorFlow and Keras
- PyTorch
Visualization:
- Matplotlib
- Plotly
- Seaborn
Just as with mathematical skills, this list should be your base. Again, it says: You can never know too much!
We know that this is a lot of things to keep in mind. You have to apply mathematics and programming regularly in everyday life to be able to perform all the processes in a project. Let’s take a look at the skills you need to implement processes.
3 Process management
To successfully manage projects and get the most out of your data, you need extensive skills in data preparation, creating machine learning models and writing SQL queries for databases. We’ll explain to you exactly which skills you need:
Data preparation:
- Encoding categorical data
- Feature engineering
- Dealing with missing values
Machine learning:
- Overfitting and underfitting
- Hyperparameter optimization
- Selecting algorithms depending on the situation
Databases:
- Writing SQL queries
- Connecting relational tables
- Using structured and unstructured data
Deployment:
- Integrating algorithms in IT infrastructures
- Cloud computing
- Continuous deployment
But technical skills alone are not enough to be successful as a data scientist. You also need soft skills that complete your profile. Let’s take a closer look at these.
The soft skills of a data scientist
As a data scientist you also have to be well-versed in soft skills. These often whether a project succeeds or fails. You need to be able to communicate with colleagues, customers or decision makers in a target group-oriented way and to integrate their wishes into your algorithms and processes. By all means as a data scientist you need to develop a thorough domain knowledge. You act as a connector between product and abstract technology. So, your communication skills should be a priority – or in data science terms: data storytelling.
4 Data Storytelling
Data storytelling is a collection of different techniques and methods to convey complex, data-driven results to non-experts. As a data scientist, you use findings from cognitive sciences. On the one hand, it is about creating a story from your data – the data story. Stories are easy to understand and stick in the mind of the listener. On the other hand, explanatory visualizations play a major role.
These are graphs that use colors and shapes to direct the viewer’s attention. It allows you as a data scientist to be a connection between experts and decision makers. Unfortunately, data storytelling is a neglected skill and difficult to master. In general, soft skills require a lot of experience.
Besides data storytelling, project management skills are very important. Agile project management in particular has established itself in data science projects.
5 Agile working
The methodology of agile working is based on various best practices collected over the years. Agile working has its origin in software development. In practice, this means delivering products quickly and developing them in iterative feedback loops. As a result, companies no longer bring a finished, perfect product to market, but often a beta version first, which is tested and optimized. In data science projects, it is often impossible to predict which challenges you will face and whether the planned solutions are feasible. This unpredictability is the reason why agile working has become widely accepted.
These are the top five skills that every data scientist needs to have. We hope this blog article has brought you new insights. Last but not least, we would like to point out one additional skill: As a data scientist you must enjoy your work, because you need to constantly develop yourself and learn new things. Knowledge is power and it’s constantly evolving. This should also apply to you and your skills!
Get to know StackFuel’s online education and training to take your data skills to the next level.
