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Essential Skills For Becoming An Expert In Data Science

Essential Skills For Becoming An Expert In Data Science

By: SMITH THOMPSON

For the past few years, high-qualified specialists in data science have been in strong demand. The digital transformation of businesses, industries, finances, education, and other fields is the main reason why this qualification has become so popular.

In most cases, the primary mission of all data scientists is predictive analytics involving four directions of work:

  • Extraction of the most valuable information from a wide range of data sources and search for patterns in these data sets;
  • Data preparation for modeling (sampling, data cleaning, patterns generation, integration, and data formatting);
  • Data modeling and visualization;
  • Building machine-learning algorithms for developing and testing patterns of business metrics.

Many companies across the globe search for highly skilled specialists in data science and offer high salaries. Therefore, if you want to make a great career in data science, you will have to obtain particular hard and soft skills. Below you can find the most significant of them.

Hard Skills   

Hard skills represent professional competencies, which your potential employer can evaluate and examine. The demand for the grounding and the level of professional skills depends on the company you are planning to work in. For example, in a large-scale corporation, data scientists must know the particulars of mathematics and statistics. Marketplaces and media companies search for experts in recommendation system design. For big retail enterprises, it is vital to hire specialists in machine vision.

Programming skills are the inevitable store of your knowledge. Today, the most widespread programming languages are Python and R. Python is comfortable to read. Besides, this computing language is beneficial because of its statistical analysis capability. It has different packages for machine learning, data visualization, and data analysis, which make Python appropriate for data science. R language can also solve every problem in data science. However, it is less popular.

Basic mathematics. It may seem that there is no need to be an expert in fundamental mathematics as present libraries contain a large number of prepared solutions for data analysis and training algorithms. Nevertheless, when it comes to resolving a difficult and atypical task, knowledge of hard science is vital. That is why many companies check candidate’s knowledge in these areas.

Understanding the core principles of machine learning is an integral part of data science. Building machine learning models implies that you have to be good at mathematical analysis, linear algebra, and probability theory. It is important to pay special attention to learning statistics, as it helps to estimate data significance and interpret prediction models. Statistics is at the heart of machine learning. These two concepts are interconnected.

Machine learning is a subset of artificial intelligence, giving computers the ability to learn from previous experience without programming. Therefore, a data scientist must now controlled and un-controlled algorithms such as linear and logistic regressions, K-means clustering, decision tree technique, etc. Fortunately, the majority of ML algorithms can be implemented with Python or R libraries. A competent data science expert has a clear understanding, which algorithm is the best fit for a certain data set.

Deep learning is a subset of machine learning. Deep learning assumes building multi-layer neural networks, where we need a more advanced and quick data analysis. The number of hidden neural layers defines the network depth. Computer vision, natural language processing, reinforcement learning, and other tools are integral parts of deep learning. Data science specialists must constantly improve their professional skills in these fields as the deep learning tendency is growing too fast.

Skills in data management and data processing are necessary for every data scientist. The data management includes data extraction, conversation, and loading. It means that a data scientist extracts data from a diversity of sources. Then he converts data into a format proper for analysis. Lastly, he downloads it into the data warehouse. The data processing means that all the information from the storage has to be unified before analyzing it.

Essential skills and traits of elite data scientists | CIO

Soft Skills

This skill-set is essential in every career. It helps to solve different problems and communicate with other people.

If you want to succeed in data science, you will need to have the skill to report audibly and for a large audience. This skill is especially advantageous in the business field, as your future customers may not understand technician terminology. In other words, a data science expert must have the ability to inform the audience in plain language. A perfect opportunity to improve your communication skills is to take part in conferences and online meet-ups.

A data scientist is always a team member, and a successful project is always a product of teamwork. Every specialist in data science should have good communication skills and understand his role in the team. For example, you may test your teamwork skills at the «Kaggle» service.

If you plan to work in the business field, a talent to see the commercial aspects is necessary. A data scientist should know how to find optimum solutions with minimum costs. Companies that use data science are gaining a profit look for specialists who know how to fulfill business ideas with the help of data.

Critical thinking is also a must-have soft skill in data science. It helps to find approaches and ways of problem-solving when others cannot see them. Critical thinking is the ability to see the problem from different sides, analyzing the sources, and displaying your curiosity.

Conclusion

So if you want to build a brilliant career in data science, check this post about the most popular ML jobs to start your action right now. This field is constantly expanding. The demand for high-skilled specialists continues to grow. However, there is still a lack of competent data specialists in the labor market.

It is not easy to become a data science expert. Nowadays, it is not enough to know computing languages and the basic principles of machine learning. This occupation calls for several other professional and personal skills. It is a difficult task, but not impossible. The time and resources that you spend on training and improving your skills in data science will be fruitful in the future.

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