Essential Data Science Skills

Data Science Technical Skills

A data scientist may require many skills, but what sets them apart is their technical knowledge. Many technical skills and knowledge of specialized tools will be essential for data scientists to be familiar with but there exists a core set of of technical knowledge that can be applied to a majority of problems across doamins.

Data scientists use programming skills to apply techniques such as machine learning, artificial intelligence (AI) and data mining. It is essential for them to have a good grasp on the mathematics and statistics involved in these techniques. This allows a data scientist to know when to apply each technique.

In addition to understanding the fundamentals, data scientists should be familiar with the popular programming languages and tools used to implement these techniques.

Data Visualization

Data visualization is an essential skill to acquire for a data scientist. Visualization enables the data scientist to see patterns and guide their exploration of the data. Second, it allows them to tell a compelling story using data. These are both critical aspects of the data science workflow.

Programming/Software

Data scientists use a variety of programming languages and software packages to comprehensively and efficiently extract, clean, analyze, and visualize data. Though there are always new tools in the rapidly changing word of data science, a few have stood the test of time.

Below are a few important and popularly used tools that aspiring data scientists should familiarize themselves with to develop programming and software data scientist skills:

R

R was once confined almost exclusively to academia, but social networking services, financial institutions, and media outlets now use this programming language and software environment for statistical analysis, data visualization, and predictive modeling. R is open-source and has a long history of use for statistics and data analytics

Python

Python, unlike R, was not primarily designed for data analysis. The pandas python library was created to fill this gap. Python has gained in popularity with a very extensive ecosystem of tools and libraries for all aspects of the data science workflow in addition to be used for software engineering tasks.

Tableau

Tableau provides a high-level interface for exploring and visualizing data in friendly and dynamic dashboards. These dashboards are essential at organizations that prioritize data-driven decision making.

Hadoop

Hadoop is an open-source software framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. Hadoop offers computing power, flexibility, fault tolerance and scalability. Hadoop is developed by the Apache Software Foundation and includes various tools such as the Hadoop Distributed File System and an implementation of the MapReduce programming model.

SQL

SQL, or Structured Query Language, is a special-purpose programming language for managing data held in relational database management systems. There are multiple implementations of the same general syntax, including MySQL, SQLite and PostgreSQL.

Some of what you can do with SQL—data insertion, queries, updating and deleting, schema creation and modification, and data access control—you can also accomplish with R, Python, or even Excel, but writing your own SQL code could be more efficient and yield reproducible scripts.

Apache Spark

Similar to Hadoop, Spark is a cluster computing framework that enables clusters of computers to process data in parallel. Spark is faster at many tasks than Hadoop due to its focus on enabling faster data access by storing data in RAM. It replaces Hadoop’s MapReduce implementation but still relies on the Hadoop Distributed File System.

Statistics/Mathematics

Today, it is Software that run all the necessary statistical tests, but a data scientist still needs to possess the sensibility to know which test to run when and how to interpret the results.

A solid understanding of multivariable calculus and linear algebra, which form the basis of many data science and machine learning techniques, will allow a data scientist to build their understanding on strong foundations.

An understanding of statistical concepts will help data scientists develop the skills to understand the capabilities, but also the limitations and assumptions of these techniques. A data scientist should understand the assumptions that need to be met for each statistical test.

Data scientists don’t only use complex techniques like neural networks to derive insight. Even linear regression is a form of machine learning that can provide valuable learnings. Simply plotting data on a chart and understanding what it means are basic but essential first steps in the data science process.

Mathematical concepts such as logarithmic and exponential relationships are common in real-world data. Understanding and applying both the fundamentals as well as advanced statistical techniques are skills that data scientists need to find meaning in data.

Though much of the mathematical heavy lifting is done by computers, understanding what makes this possible is essential. Data scientists are tasked with knowing what questions to pose, and how to make computers answer them.

Computer science is in many ways a field of mathematics. Therefore, the need for mathematical data scientist skills is clear.

Data Scientist Soft Skills

Data science requires a diverse set of skills. It is an interdisciplinary field that draws on aspects of science, math, computer science, business and communication. Data scientists may benefit from a diverse skill-set that enables them to both crunch the numbers and effectively influence decisions.

Because data scientists focus on using data to influence and inform real-world decisions, they should be able to bridge the gap between numbers and actions. This requires skilled communication and an understanding of the business implications of their recommendations. Data scientists should be able to work as part of a larger team, providing data-driven suggestions in a compelling form. This requires skills that go beyond the data, statistics and tools that data scientists use.

Communication

Data scientists should be able to report technical findings such that they are comprehensible to non-technical colleagues, whether corner-office executives or associates in the marketing department.

Make your data-driven story not just comprehensible but compelling.

One important data scientist skill is communication. In order to be effective as a data scientist, people need to be able to understand the data. Data scientists act as a bridge between complex, uninterpretable raw data and actual people. Though cleaning, processing and analyzing data are essential steps in the data science pipeline, this work is useless without effective communication.

Effective communication requires a few key components.

Visualization allows a data scientist to craft a compelling story from data. Whether the story describes a problem, proposes a solution or raises a question; it is essential that the data be presented in a way that leads the audience to reach the intended conclusions. In order for this to happen, data scientists should describe the data and process in a shared language, avoiding jargon and unnecessary complexity.

Business Acumen

Business awareness could now be considered a prerequisite for effective data science. A data scientist should develop an understanding of the field they are working in before they are able to understand the meaning of data. Though some metrics, like profit and conversions, exist across industries, many key performance indicators (KPIs) are highly specialized. This data makes up the industry’s business intelligence, which is used to understand where the business is and the historical trends that have taken it there.

The unique goals, requirements and limitations of each industry define every step that a data scientist takes. Without understanding the underlying aspects of the industry, it could be impossible to find meaningful insight or make useful recommendations.

A data scientist may be most effective when they truly understand the business they are advising. Though data can provide unique insights, it may not capture the full picture. This requires a data scientist to be aware of the processes and realities at play in their industry. Though they may share a job title, the precise goals and tasks of a data scientist will vary greatly by industry. To be successful, a data scientist should understand the industry that they are working in.

Data-Driven Problem Solving

Data-driven problem solving allows data to inform the entire data science process. By using a structured approach to identify and frame problems, the decision making process could be simplified. In data science, the vast quantity of data and tools creates nearly endless avenues to pursue. Managing these decisions is an essential job for a data scientist. Data science both informs and is informed by the data-driven problem solving process.

A data scientist is likely to know how to productively approach a problem. This means identifying a situation’s salient features, figuring out how to frame a question that will yield the desired answer, deciding what approximations make sense, and consulting the right co-workers at the appropriate junctures of the analytic process. All of that in addition to knowing which data science methods to apply to the problem at hand.

A data scientist’s job is to understand how to take raw data and derive meaning from it. This requires more than just an understanding of advanced statistics and machine learning. They also need to integrate their understanding of the problem domain, available information and their goals when deciding how to proceed.

Data science problems and solutions are never obvious. There are many possible paths to explore, and it is easy to become overwhelmed with the options. A structured approach to data-driven problem solving allows for a data scientist to track and manage progress and outcomes. Structured techniques such as Six SigmaExternal link:open_in_new are great tools to help data scientists and teams solve real world data science problems.

Excerpted from Essential Data Science Skills