Data analytics, data mining, artificial intelligence, machine learning, deep learning – all fall under data science and the list doesn’t end here. Clearly, it is one of the fastest growing fields when it comes to career opportunities and by extension, salaries.
The field requires data scientists who aren’t just experts in a variety of programming languages and statistical computations, but also have good interpersonal skills. The latter because the findings or statistical insights need to be relayed to stakeholders effectively.
To give you a clear idea of what exactly employers are looking for in data scientists, we’ll be walking you through the following topics:
- What Does a Data Scientist Do?
- Top 8 Skills Employers Look for in Data Scientists
- FAQs on Data Science
What Does a Data Scientist Do?
Responsible for compiling and analyzing massive, structured as well as unstructured data sets, data scientists use math, statistics and computer science skills to decode huge sets of data. After that, they use this information to come up with commercial solutions to the problems the company might be facing.
As a data scientist, you’ll be working with different departments like marketing, operations, and so on. Highly in-demand owing to our data and tech-lead economy, the data science field has a lot to offer, if you have the required skills.
Top 8 Skills Employers Look For in Data Scientists
To get the best data scientist jobs out there, you’ll need to have these 8 skills:
1. Extensive Understanding of the Fundamentals of Data Science
Naturally, we begin with the fundamentals of data science. Your foundation needs to be strong. You’ll need to know the principles of AI, machine learning and data science. Have a thorough understanding of topics such as:
- Differentiation between deep learning and machine learning.
- Commonly used tools and terminologies.
- Problems of classification vs. regression.
- Differentiation between supervised and unsupervised learning.
- What data science, business analytics and data engineering are, and the differences among them.
2. Knowledge of Mathematical Concepts such as Statistics and Probability
In order to develop high-quality models, you need to have a good knowledge of statistics. When it comes to Machine Learning, it begins with statistics and then it evolves. In fact, even the linear regression concept is a statistical analysis concept and has been around for quite some time.
Since statistics includes a lot of concepts that help you in understanding and analyzing data, it should not come as a surprise that it’s an important topic for data scientists. You’ll need to a have good knowledge of descriptive statistics (mean, median, mode, etc.).
Other topics include probability distributions, CLT, skewness and kurtosis, sample and population, as well as inferential statistics (hypothesis testing and so on).
3. Fluency in Programming Languages
For data scientists, apart from a good knowledge of maths and statistics, fluency in a variety of programming languages is also necessary. As they are required to handle advanced statistical tools, programming languages become a must-have.
Some of these languages are:
- Python: It can help you in doing everything, right from data mining and website development to running embedded systems using a single language.
- R Programming: It is a software package that consists of functions that help in data manipulation, calculation, as well as a graphical display. In comparison to Python, R is used much more widely in academic environments.
4. Familiarity with Data Extraction, Transformation and Loading
As a data scientist, you’ll be expected to extract data from various data sources such as Google Analytics, MongoDB, MySQL and so on. After extraction, you’ll have to work on transforming the data so that it can be stored in a suitable format or structure when doing querying and analysis.
Lastly, you’ll be loading the data into the Data Warehouse. This is a kind of data management system that is designed to enable and support Business Intelligence activities, in particular – analytics.
Therefore, the data science field is an excellent career choice for those who have an ETL (Extract, Transform, and Load) background.
5. Good Grasp of Data Wrangling and Data Exploration
The process of data wrangling consists of cleaning and unifying cluttered and complicated data collections so that they can be easily accessed and analysed. Even though data manipulation and wrangling might take some time, it is ultimately the best choice and will help you in making better data-lead judgements.
Some of the most popularly used data manipulation and wrangling techniques are – missing value imputation, correcting data types, outlier treatment, scaling and transformation. As a data scientist, you’ll be expected to have a good grasp of such techniques.
6. Data Visualization Knowledge
Data visualization is one of the key aspects of data analysis. This is because data visualization plays an important role in relaying information in a manner that is easy to understand and pleasant on the eyes.
For starters, you need to have a good knowledge of the basic plots such as bar charts, pie charts, and histograms. As you advance, you’ll need to know waterfall charts, thermometer charts and so on. In the exploratory data analysis stage, such charts are very useful.
Therefore, data scientists need to procure the required data visualization skills in order to effectively connect with the end-users. Programs such as Tableau, Power BI, Qlik, Sense and several clothes have quite a user-friendly interface.
7. Comprehensive Understanding of Machine Learning
A must-have ability for any data scientist worth his grain, machine learning plays a key role in data science. Machine learning helps in the creation of predictive models. Starting with simple linear and logistic regression models, you’ll move on to more advanced ensemble models like Random Forest and XGBoost.
The knowledge of the codes for these algorithms is necessary, however, the understanding of how they operate is crucial. This understanding is what helps in hyperparameter adjustment and finally, the development of a model that has a low error rate.
8. Firm Knowledge of Big Data Processing Frameworks
As you’ll be dealing with large sets of data to train machine learning models, you’ll need to develop machine learning models. Something which was previously an impossibility owing to the lack of data and computer capability.
Since you have to deal with a large amount of data these days and it can be organised or unstructured, using the usual processing methods won’t cut it. Such massive data sets are known as Big Data.
To handle this type of data, frameworks such as Hadoop, Spark, etc. are needed to handle it. This is why most businesses nowadays are using Big Data analytics to unearth the latest business insights. Therefore, a data scientist is required to have this skill.
Since the data science field is growing at such a swift pace, so is the demand for talented data scientists. So before going in for any data scientist job, ensure that you possess all the skills given above.
FAQs on Data Science
Q1. Is data science a good career?
There are a lot of opportunities in the data science field as it is growing very fast. So the answer is yes. If you opt for a career in data science, you’ll be opting for one where there are competitive salaries and the demand is high.
Q2. What is the average data scientist’s salary?
The average base salary of a data scientist in Dublin, Ireland is roughly €70,000 per year.
Q3. Which degree is best for a data scientist?
To begin with, you’ll need to have a bachelor’s degree in data science or a computer-related field to get entry-level jobs. However, most data science careers require you to have a master’s degree.
Top Data Scientist and Other Data Jobs in Ireland
Some of your top data jobs in Dublin, Ireland on offer are:
- Data Scientist
- Machine Learning Engineer
- Data Analyst
- Data Engineer
- Big Data Engineer
For more information on the data scientist and data engineering market, feel free to download our latest IT Salary Survey.