If you plan to have a career in Data Science, then you also need to be aware of the required skill set required for the profession. In this blog post, we would discuss all the required skills that might help you in shaping your career as a data science professional.
So, let’s first discuss the job profile of a data scientist in an organization and then we would discuss the skills required.
Nowadays the demand for data scientists has increased considerably. A data scientist is responsible to predict the growth and performance of an organization by analyzing and researching the organizational data.
Have a look at the roles and responsibilities of the data scientists:
Data scientists use mathematics, statistics, and programming skills to organize a huge amount of data and provide business-related solutions to the problems that were hidden so far. They easily predict the analysis of the output of big data processing result. So, a data scientist needs to have strong mathematics and statistical skills. A potential data scientist may possess all these skills and have knowledge of data science tools and techniques. He may acquire online training and get certified for the same as well.
Data scientists are expected to possess some of the basic skills like deep thinking, intellectual curiosity, ability to discover new concepts, etc. A data scientist’s motivational factor is not money instead is the ability to solve complex and typical data problems. They tend to discover the hidden truth behind to find the optimal solution.
As far as an academic qualification of a data scientist is concerned then they must possess a specific statistical, mathematical and technical degree to become the same. For this multidisciplinary job profile, even those candidates who have completed Ph.D. in statistics can be the suitable ones. Today online training programs can get you both practical and theoretical data science knowledge in no time. Mainly used programming languages by the data science professionals are R, SQL, Java, Hadoop, and Python.
R is the most preferred language for data science professionals and is specially designed for the data science needs. All the analytical data science problems can be solved by using R. However, R has a steep learning curve but still is being used as a programming language by more than 43% of the data science professionals.
Python is one of the most used and common programming languages that is required by the data scientists along with C/C++, Java, and Perl. Several programmers use Python due to its simplicity and compatibility like features.
Various data formats can be used as input for this language and SQL tables can also be imported into the code. The user can also create their own data sets through Python and find them on Google as well.
Although you may not surely require Hadoop, it is usually preferred in lots of cases. Experience of Pig and Hive can also be beneficial. amazon S cloud experience may also be an added advantage for this profession. As per a LinkedIn, post-Apache Hadoop is considered as the second most important skill for data science profession.
Hadoop can be used for data filtration, data exploration, summarization, and data sampling. Being a data scientist, if you may have to deal with huge amount of data that cannot be stored at a place, then you will have to send it to various locations and there you will have to use Hadoop. It can help you in quickly sending data to various locations and places.
Apache Spark has become one of the most popular big data technologies across the globe and is a Hadoop like computation framework. The one and the only difference is Spark is considered as a faster framework then Hadoop. This is because in Hadoop the work is read and written on the disc, while in case of Spark it is cached in memory.
Apache Spark is specially designed for data science to run the complicated programs quickly. Data scientists can also handle unstructured data efficiently with the help of Apache Spark. The features and speed of the platform make Spark a suitable platform for data science professionals.
Machine learning includes a neural network, adversarial and reinforcement learning, etc. If you want to stand out from other successful and experienced data science professionals then you must be aware of all the popular machine learning techniques that are logistic regression, decision tree, supervised machine learning and many more. You can solve prediction based organizational problems and solve them quite easily and quickly.
There are very few data scientists that are expert in machine learning skills like supervised and unsupervised learning, computer vision, survival analysis, and reinforcement learning. Familiarity with machine learning concepts can help the data scientists to work with large datasets.
A vast amount of data is regularly produced by the organizations that need to be translated and formatted. Management professionals require and use graphical data more than raw data. Data visualization tools like Matplottlib, Tableau, ggplot, and d3.js must be clear to the professional's that can help them in converting the complex results to a comprehensive form. Here the fact is that many professionals do not understand technical terms, so it is quite good for them to show them visual representations and result.
Through visualized results, organizations can get data insights quickly and grasp the facts and create new business opportunities.
Data scientists must know the techniques to handle structured and unstructured data. Unstructured data is that data that is not stored in tabular form. It may be like videos, blog posts, social media posts, customer reviews, audio files, and text files. As such type of data is not streamlined so it is quite essential to sort this data so that is can be processed. Unstructured data analysis is also termed ‘dark analytics as it is complex to process and understand, but unstructured data can help in the decision-making process of the organization.
Companies always look for those professionals who can fluently and clearly translate technical findings to any non-technical team like sales or marketing department. Being a data scientist, you may have to communicate with various departments of the organization like sales, marketing, operations, and others. So, you must have the skills to create story-line around the data so that anyone can easily understand it.
Data scientists cannot work alone, they need to work with executives, product managers, designers, marketers, clients, and developers. To collaborate with various team members, you must know how to deal with colleagues and teammates. It becomes easier to finalize your business goals with the help and support of your team members. You must have the skills to translate the data so that it can be understood by everyone within the organization either a technical or non-technical person.
Being Intellectual is an important part of the career of a data scientist. With high curiosity, they can acquire more knowledge. As data scientists spend most of their working hours with data, so they can provide an accurate result. Moreover, they regularly update their knowledge and learn new technologies and tools to know the current trend of data science.
Role of data scientists involves more technical skills than non-technical ones. In order to be a proficient data scientist, you need to learn and understand all the tools and technologies that may make the job easier. Companies deal with huge amount of data that they may use for their business goals to increase their ROI, so they hire skilled and experienced data science professionals. By learning this most in-demand and promising skill you can also shape your career and earn a high salary.
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