The Rise of Data Science: Skills Every Business Needs Today

Every business is focused on harnessing, organizing, and analysing data. The number of jobs in data science is growing every day. Most companies realize the benefits of data science could have on their business and their competitiveness. As companies recruit more and more people to handle the data science function, the focus is on what specific skills are required for this role. What skills should a data scientist typically possess and what should you be looking for? Let us look at these in detail.

Data science requires that various disciplines are merged to extract valuable insights and make informed decisions. Professionals in this field, leverage their expertise in statistics and computer science to analyse data. Apart from technical skills, a data scientist will also require some non-technical skills to succeed. Outlined below are some key skills (both technical and non-technical) that every data scientist must possess.

Programming skills

Data scientists require proficiency is programming languages. Typically, proficiency in languages like Python, R, Java, C, C++, SQL, and Scala will be an asset. Programming languages help data scientists organize unstructured data sets.

Machine learning and deep learning

Machine learning is a branch of artificial intelligence (AI) and computer science that focuses on getting data and algorithms to teach AI to imitate the way humans learn. Machine learning is a highly sought after data science skill. A data scientist is expected to bring an expertise in various machine learning algorithms, techniques, and frameworks. Knowledge of deep learning frameworks could put a data scientist ahead of their peers. Deep learning is a subset of machine learning that uses multi layered neural networks and simulate complex decision-making that normally only the human brain is capable of.

Cloud computing and big data

Data scientists are required to handle and analyse large datasets. To handle Big data will require a knowledge of Hadoop, Spark, SQL, Hive and Apache Pig. To store, control and handle data applications online using remote servers is known as cloud computing. Knowledge of cloud computing tools to analyse and visualize data will be crucial for a data scientist.

Mathematics and statistics

Data scientists must have a solid foundation in mathematics. Knowledge of principles such as linear algebra, regression, optimization, classification and density estimation are very important. Data scientists are also required to have a strong knowledge of statistics. Knowing descriptive and inferential statistics is important for understanding data and making informed decisions in statistical modelling. Concepts such as Mean, Median and Mode, P-value, Bayes’ theorem, standard deviation, and variance must be mastered.

Data collection and data visualization

Data scientists are required to collect data from various sources in different formats and structures. Knowledge of specific tools to handle this like Microsoft Excel, Python, Ruby, Trifacta and SQL will be very helpful. An important data science skill is to effectively communicate and present data insights. This is called data visualization. There are tools that aid in this like Excel VBA, Tableau, Qlik Sense, and Power BI.

Strong business acumen

A data scientist must channel their technical skills, discern the potential problems and challenges, and solve them to benefit the organization. As such a strong business acumen will help you explore new business opportunities and help your organization grow. A data scientist must understand business objectives and the underlying process that drives business and growth.

Strong communication skills

While a data scientist is adept at extracting, analysing and understanding data, in order that the organization benefits from your services, you must be able to effectively communicate your findings to team members who don’t have the same background as you. Key decision makers are dependent on the vital insights that a data scientist can present. Hence, the ability to communicate the results of their analysis, skills in visualization and reporting data are very important. A data scientist may have the best analytical skills in the world, however, unless they can make their findings understandable to everyone they work with, they will be of little use to any business. They must demonstrate how they can improve performance and drive success.

Creativity

There are no rules as to how a company can use data. Data science is an emerging field and a data scientist can come up with new methods of gathering, interpreting, analysing, and profiting from a data strategy. A data scientist who can help solve business problems and come up with new and innovative ways to apply the information gathered from data analysis will become a superstar.

Critical thinking and decision making

In the process of analysing and evaluating data, a data scientist sometimes must make a judgement or choice. This is known as critical thinking. They also must sometimes, choose the best course of action from a range of alternatives after weighing all options. Also, creative, and innovative thinking can generate novel ideas and unconventional answers.

Conclusion

Becoming a data scientist is challenging and requires a combination of technical skills, domain knowledge and practical experience. The field of data science encompasses disciplines such as mathematics, statistics, programming, and machine learning. A data scientist will need dedication, continuous learning, and hands on practice in order to be successful.