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Data analytics, data mining, artificial intelligence, machine learning, and deep learning \u2013 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.<\/p>\n
The field requires data scientists who aren\u2019t just experts in a variety of programming languages and statistical computations but also have good interpersonal skills. The latter is because the findings or statistical insights need to be relayed to stakeholders effectively.<\/p>\n
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To give you a clear idea of what exactly employers are looking for in data scientists, we\u2019ll be walking you through the following topics:<\/strong><\/p>\n <\/p>\n 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.<\/p>\n As a data scientist, you\u2019ll 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<\/a>.<\/p>\n <\/p>\n To get the best data scientist jobs<\/a> out there, you\u2019ll need to have these 8 skills:<\/p>\n <\/strong><\/p>\n Naturally, we begin with the fundamentals of data science. Your foundation needs to be strong. You\u2019ll need to know the principles of AI, machine learning and data science. Have a thorough understanding of topics such as:<\/p>\n <\/strong><\/p>\n 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 evolves. In fact, even the linear regression concept is a statistical analysis concept and has been around for quite some time.<\/p>\n Since statistics includes a lot of concepts that help you in understanding and analysing data, it should not come as a surprise that it\u2019s an important topic for data scientists<\/a>. You\u2019ll need to a have good knowledge of descriptive statistics (mean, median, mode, etc.).<\/p>\n Other topics include probability distributions, CLT, skewness and kurtosis, sample and population, as well as inferential statistics (hypothesis testing and so on).<\/p>\n <\/strong><\/p>\n 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.<\/p>\n Some of these languages are:<\/p>\n <\/strong><\/p>\n As a data scientist, you\u2019ll be expected to extract data from various data sources such as Google Analytics, MongoDB, MySQL and so on. After extraction, you\u2019ll have to work on transforming the data so that it can be stored in a suitable format or structure when doing querying and analysis.<\/p>\n Lastly, you\u2019ll 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 \u2013 analytics.<\/p>\n Therefore, the data science field<\/a> is an excellent career choice for those who have an ETL (Extract, Transform, and Load) background.<\/p>\n <\/strong><\/p>\n 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-led judgements.<\/p>\n Some of the most popularly used data manipulation and wrangling techniques are \u2013 missing value imputation, correcting data types, outlier treatment, scaling and transformation. As a data scientist, you\u2019ll be expected to have a good grasp of such techniques<\/a>.<\/p>\n <\/strong><\/p>\n 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 to the eyes.<\/p>\n For starters, you need to have a good knowledge of basic plots such as bar charts, pie charts, and histograms. As you advance, you\u2019ll need to know waterfall charts, thermometer charts and so on. In the exploratory data analysis stage, such charts are very useful.<\/p>\n 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.<\/p>\n <\/strong><\/p>\n 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\u2019ll move on to more advanced ensemble models like Random Forest and XGBoost.<\/p>\n 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.<\/p>\n <\/strong><\/p>\n As you\u2019ll be dealing with large sets of data to train machine learning models, you\u2019ll need to develop machine learning models. Something which was previously an impossibility owing to the lack of data and computer capability.<\/p>\n 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\u2019t cut it. Such massive data sets are known as Big Data.<\/p>\n 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<\/a>.<\/p>\n 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.\u00a0<\/em><\/strong><\/p>\n \u00a0<\/strong><\/p>\n Q1. Is data science a good career?<\/strong><\/p>\n 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\u2019ll be opting for one where there are competitive salaries and the demand is high.<\/p>\n Q2. What is the average data scientist’s salary?<\/strong><\/p>\n The average base salary of a data scientist in Dublin, Ireland is roughly \u20ac70,000 per year.<\/p>\n Q3. Which degree is best for a data scientist?<\/strong><\/p>\n To begin with, you\u2019ll need to have a bachelor\u2019s 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.<\/p>\n <\/p>\n Some of your top data jobs<\/a> in Dublin, Ireland on offer are:<\/p>\n\n
What Does a Data Scientist Do?<\/h2>\n
Top 8 Skills Employers Look For in Data Scientists<\/h3>\n
1. Extensive Understanding of the Fundamentals of Data Science<\/h5>\n
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2. Knowledge of Mathematical Concepts such as Statistics and Probability<\/h5>\n
3. Fluency in Programming Languages<\/h5>\n
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4. Familiarity with Data Extraction, Transformation and Loading<\/h5>\n
5. Good Grasp of Data Wrangling and Data Exploration<\/h5>\n
6. Data Visualization Knowledge<\/h5>\n
7. Comprehensive Understanding of Machine Learning<\/h5>\n
8. Firm Knowledge of Big Data Processing Frameworks<\/h5>\n
FAQs on Data Science<\/h3>\n
Top Data Scientist and Other Data Jobs in Ireland<\/h4>\n