In simple terms, data science combines large amounts of data or big data to form useful information for decision-making processes. Often used inter-changeably, the terms ‘data science’ and ‘data analytics’ are actually two separate aspects of how data is used (yes, we were surprised, too!).
Data Science is a multidisciplinary field which focuses on statistics and computation. It encompasses all aspects of how data is gathered and studied, and how information is extracted from it. Data Analytics, on the other hand, refers to the processing and performing of statistical analysis on existing data sets. While data science looks at how information (i.e. data) is gathered and studied, data analysis is a method used in gathering evidence to support specific goals or to provide actionable insights to specific areas of the data gathered.
So, you’ve decided you want to be a data analyst or pursue a career in data science? Either way, you will need to master the essential skills to get hired!
Programming would serve as a key component to the organisation of collected data. Consequently, knowledge of programming languages such as Python, Perl, C/C++, and database querying languages such as SQL will help in organising and fine-tuning unstructured data sets.
A solid understanding of probability and statistics is an important skill set. Statistical knowledge will come in handy to understand the data you are working with, in particular, whether a particular technique is or is not a valid approach in collecting and analysing data and to avoid common errors and pitfalls which may stifle decision-making processes.
While we’re more likely to think of cowboys than data when we hear the word ‘wrangling’, the ability to actually wrangle data is a valuable skill in the field of data science. There may be occasions where you will have to deal with unorganised or messy data, for example, missing values, inconsistent dates and string formatting, etc. You may also have to work with unstructured data i.e. understand and manage data that is coming from different channels.
4. Data Visualisation and Communication
The ability to communicate, or describe your findings or how techniques work to audiences, and visualise, or be familiar with data visualisation tools to make them easier for audiences to understand is especially important.
In firms which are taking their first steps in making data-driven decisions, the ability to visualise and communicate data will be what helps convey ideas, theories and findings to your audience and enable them to effectively be put into action.
A key skill in any job is the ability to solve problems. You might need to de-bug a programme or do a quick research on a particular software. In fact, you will be required to think out of the box and get innovative in the way you resolve a problem. In any case, strong problem-solving skills will set you apart from the rest!
Why not get started on the right track and get certified with Project DEEP’s Professional Certificate in Data Science & Analytics ?
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