Data Science Master Program

Technical Skills

Though a data scientist may require many skills, their technical knowledge is what sets them apart. There are many technical skills and specialized tools that data scientists need to be familiar with. Different businesses will use different tools and languages in their workflow. However, all data scientists positions will require a core set of technical knowledge that can be applied to many problems. These core technical skills could be considered essential for all data scientists.

Data scientists use programming to apply techniques such as machine learning, artificial intelligence (AI) and data mining. They should have an understanding of the mathematics and statistics involved in these techniques in order to understand 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. They should also understand the principles of software engineering in order to integrate the languages and tools they use.

Data Visualization

Data visualization could be an essential skill for all data scientists. Humans are inherently visual and have far easier time recognizing patterns visually. Visualization plays two essential and equally important roles in data science. First, it enables the data scientist to see patterns and inform their exploration of the data. Second, it allows them to tell a compelling story using data. These are both essential parts of the data science workflow.

Scatter plots and histograms are essential elements of exploratory data analysis. Without visualizing data, it is difficult to know where to start. Deriving meaning from data only matters if you can share that meaning with others. In order to do this, the data should be presented in attractive and informative visuals. Data storytelling requires a data scientist to creatively use data visualization to craft a narrative that informs the audience and explains their reasoning. Without these tools, data science could be ineffective at implementing change.

There are many data visualization tools available to data scientists: most programming languages provide libraries for visualizing data. Python data visualization can be done with Matplotlib and pandas. JavaScript offers the D3.js visualization library. R offers ggplot2 as well as many other data visualization tools. Tableau is a high-level platform for visualizing data from many different sources.


Data scientists use a variety of programming languages and software packages to flexibly 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. Here are six important and broadly used tools that aspiring data scientists should familiarize themselves with:

  • 1. 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. This means it has a huge network (called CRAN) that provides packages for many data analysis tasks.

  • 2. Python: Python, unlike R, was not designed for data analysis. The pandas python library was created to fill this gap and enables efficient data storage and vectorized processing operations. Now that data analytics and data processing libraries have been developed for Python, however, the likes of Bank of America and Facebook are using Python for data science. The high-level programming language is powerful, fast, friendly, open and easy to learn. It’s long history of general programming use makes it easy to merge Python data processing with general-purpose code.

  • 3. Tableau: Seattle-based software company Tableau offers a suite of products that complement data science standbys such as R and Python. Tableau may not be the best tool for cleaning or reshaping data, and its relational model doesn’t allow for procedural computations or offline algorithms, but it is great for data exploration and interactive analysis. Tableau provides a high-level interface for exploring and visualizing data in friendly and dynamic dashboards.

  • 4. 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.

  • 5. 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.

  • 6. 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.


Software runs all the necessary statistical tests these days, but a data scientist still needs to possess the statistical 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 analysis techniques, is likely to allow a data scientist to build in-house implementations of analysis routines as needed. An understanding of statistical theorems helps data scientists 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 information. 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 allow data scientists 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 data scientists to have a good foundation in math is clear. Understanding concepts like irrational and rational numbers help data scientists write efficient and accurate code.

Non-Technical 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.


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.

One important skill that every data scientist should have 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. It starts with effective visualization. Humans are inherently visual and can understand and process data better when it is presented visually. This step is essential both for data exploration and communication.

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

Data scientists are needed in nearly every industry. As the availability of data grows, so do the applications. Data science is no longer a field limited to tech and financial companies. Each industry has unique goals, datasets and constraints. In order for a data scientist to be effective, they should understand the field they are applying their skills to.

A 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 industries 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 Sigma are great tools to help data scientists and teams solve real world data science problems.

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