The Successful Data Scientist

Professionals all over the world share a mutual ambition to become successful and highly notable names in their chosen fields. Young data practitioners and even aspiring Data Scientists often see themselves making groundbreaking research and technological discoveries to solve real-life problems. 

When asked what a successful Data Scientist looks like, names such as Yann LeCun of Meta, Dr. DJ Patil of LinkedIn and Caitlin Smallwood of Netflix quickly come to mind. These names tend to be familiar in the data world because of their significant contribution to leading applications of machine learning, research and efforts in building strong data teams in their organisations. 

As a matter of fact, Mr Yann LeCun is called the founding father of Convolutional Nets for his role in deep learning and the invention of the Convolutional Neural Network Method which is widely used for image, videos and speech recognition. This anecdote leads us to two frequently asked questions: ‘Who is a Data Scientist?’ and ‘What skills are required to succeed as a Data Scientist?’

Identifying a Data Scientist

A Data Scientist is a skilled and knowledgeable analytical expert who makes use of statistics, scientific computing, methods, processes, algorithms and systems to extract knowledge and insight from structured and unstructured data to uncover solutions to business challenges.

Data Scientists are valuable in all industries; be it in manufacturing or services. Data scientists in these fields are reliable assets as their roles include analysing, predicting and contributing to meaningful insights necessary for decision-making.

Technical Skills Required of a Data Scientist

While there is a combination of various skills that every Data Scientist needs to build a successful data science career, this article focuses on five invaluable technical skills.

      •  Statistical or Mathematical Skills: Statistics is a field of maths and science concerned with collecting, analysing, interpreting and presenting data. It’s not surprising that most Data Scientists have a solid background in maths as it is a prerequisite for a successful career. Some scientists start with a degree in finance, mathematics or even computer science. Because Data Scientists work with big data, and mathematics is the basics of machine learning algorithms, one should have a solid background in maths. Data Scientists with a strong foundation in linear algebra, probability and statistics tend to avoid bias fallacies and logical errors when analysing data. Data practitioners with statistical skills can produce accurate results to improve organisational activities.

        • Programming Languages: Having a strong knowledge of various programming languages such as Python, Pert, C/C+, SQL and R enables Data Scientists to efficiently work in their field. A programming language is a system of notation for writing computer programs. These programming languages are mostly text-based and are used daily to organise and analyse data. There are many programming languages and having a piece of good knowledge about them will enable a data scientist to excel in his/her field. This skill can be learned and mastered over time.

          • Data Visualisation: Simply put, data visualisation is the graphical representation of data. Data Scientists are in the business of turning numbers into easy and understandable stories and information. This includes charts, graphs and maps to help non-statistical people  gain a better understanding of data. Through visual representation, it is easier to understand patterns and trends in a large dataset. A good knowledge of data visualisation and software – such as Power BI, Tableau, Chartlist, Datawrapper, and Google Charts – will help Data Scientists transfer their skills into dashboard models and business intelligence reports that are more helpful.

            • Data Cleaning: Data cleaning skill is essential to the day-to-day activities of a Data Scientist. It is considered the foundation of data science basics, as clean data improves productivity and improves the quality of information. This skill is important for producing error-free and useful data. It also improves client relations, staff productivity, and prevents losses. To achieve success and confidence in their practice, Data Scientists must develop data cleaning skills . Some well-known data cleaning tools include OpenRefine, Trifacta wrangler, Tibco Clarity and Winpure Clean and Match.

              • Machine Learning: Machine Learning is the use and development of computer systems that can imitate intelligent human behaviour without explicit instructions. This skill is important because it gives organisations insight into customer behaviour, operational patterns and support the development of new products and services. Machine learning skill is important to various industries, particularly the fast-evolving tech industry. This skill is highly sought after as the world is moving away from gathering and analysing data to predicting human behaviours, and promoting smart actions through the use of artificial intelligence. Machine learning is essential to data science practice because it introduces speed, accuracy and efficiency beyond human capabilities.

            How Data Scientists make use of these skills is highly dependent on the industry they find themselves in and their roles. However, these top five skills are what every Data Scientist needs to provide solutions to valuable business problems and prepare for a successful career.

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