What do Data Scientists Really Do?

Q: What Skills Are Needed to Be a Data Scientist?

Data scientists need various skills at work, such as: Programming skills: They need to know how to use statistical programming languages like Java, R, Python, and Structured Query Language (SQL). Statistical skills: All data scientists should have a working knowledge of statistics. They should be familiar with statistical tests, distribution, and estimation. They need to use statistics to validate techniques and evaluate experiments. ML skills: Working with both structured and unstructured data and using ML can help them accomplish tasks with ease. Familiarity with k-nearest neighbors (data classification approach depending on the nearest data points), ensemble methods (classifying data points that improve prediction), and random forests (method of classification using decision trees) would be useful for understanding key points. Mathematical skills: Mathematical concepts help improve algorithm optimization strategies that can bring about profitable wins for organizations. Calculus and algebra form the basis of algorithmic processes, especially for those who want to build in-house implementations. Data wrangling skills: Data scientists need to map and transform data from raw into a specific format to make it more useful for analytics, particularly when the dataset has imperfections. Data visualization skills: These are incredibly important when it comes to making data-driven decisions. Data scientists need to make sure that they can make sense of the data they are working on. They must be familiar with visualization tools such as ggplot, matplotlib, d3.js, or tableau. Software engineering skills: A strong background in software engineering can help data scientists handle data logging and develop products for data acquisition.

Data science recently became a buzzword in tech. Its popularity made becoming a data scientist a coveted career. But not all are familiar with what a data scientist does and the skills you should have to become one.

This post provides you the answers to your most pressing questions about data scientists.

What Is Data Science?

Data science refers to the use of a variety of skill sets, such as programming, mathematical, and statistical know-how to study information. It involves gathering, processing, and deriving insights from data. It deals with large amounts of data referred to as “big data.”

If you’re wondering how big is the so-called “big data” data scientists have to work with, you only have to look at social media sites like Facebook. More than 500 terabytes of new data are added to its database every day, with millions of people uploading pictures and videos and engaging with one another. Data science aims to understand this data, see patterns, and predict the future.

What Do Data Scientists Do?

Data scientists rely heavily on data. They extract meaning from, visualize, analyze, manage, and store data to give organizations insights for their decision-making processes. Some of the tasks expected of a data scientist include:

Acquiring data to jumpstart the discovery process
Processing and filtering data
Integrating and storing data
Investigating data and doing exploratory data analysis
Using machine learning (ML), artificial intelligence (AI), and statistical modeling to solve organizational issues
Measuring data and improving results
Presenting data analyses to executives and stakeholders
Analyzing trends and patterns and their relationships with acquired data

What Skills Are Needed to Be a Data Scientist?

Data scientists need various skills at work, such as:

Programming skills: They need to know how to use statistical programming languages like Java, R, Python, and Structured Query Language (SQL).

Statistical skills: All data scientists should have a working knowledge of statistics. They should be familiar with statistical tests, distribution, and estimation. They need to use statistics to validate techniques and evaluate experiments.

ML skills: Working with both structured and unstructured data and using ML can help them accomplish tasks with ease. Familiarity with k-nearest neighbors (a data classification approach depending on the nearest data points), ensemble methods (classifying data points that improve prediction), and random forests (a classification method using decision trees) would be useful for understanding key points.

Mathematical skills: Mathematical concepts help improve algorithm optimization strategies that can bring about profitable wins for organizations. Calculus and algebra form the basis of algorithmic processes, especially for those who want to build in-house implementations.

Data wrangling skills: Data scientists need to map and transform data from raw into a specific format to make it more useful for analytics, particularly when the dataset has imperfections.

Data visualization skills: These are incredibly important when it comes to making data-driven decisions. Data scientists need to make sure they can make sense of the data they are working with. They must be familiar with visualization tools, such as ggplot, matplotlib, d3.js, or tableau.

Software engineering skills: A strong background in software engineering can help data scientists handle data logging and develop products for data acquisition.

What Are the Requirements to Become a Data Scientist?

Here are the requirements for becoming a data scientist:

A degree in computer science, mathematics, IT, or statistics
Considerable experience working in a related field
Strong problem-solving skills
Capacity to work individually or with a team
Familiarity with data collection and analysis
Strong verbal and visual communication skills

What Projects Can Data Scientists Work On?

An impressive portfolio of projects can help boost your chances of landing a high-paying job as a data scientist. Some of the projects you can work on are:

1. Data Cleaning

Part of a data scientist’s responsibility is cleaning or filtering data, depending on how it will be used. Taking on such projects can help improve your chances of getting hired. To start, you can take on data-cleaning projects by looking for messy datasets. A good start would be the data.gov website.

2. Exploratory Data Analysis

One of the things that can make your portfolio stand out is gaining experience in exploratory data analysis (EDA) to show that you can look beyond a hypothesis by summarizing major points. Your ability to show off your data investigation skills can make you a valuable part of any data science team. You can check out the IBM Analytics Community for some exploratory data analysis (EDA) datasets that you can work on.

3. Interactive Data Visualization

If you want to work for business-focused organizations, knowledge about dashboards is critical. By using dashboards, data teams can easily collaborate and develop insights for data visualization. One useful dashboard tool is Dash by Plotly.

What Is the Difference between Data Scientists and Data Analysts?

While the two terms overlap in terms of tasks and the specific skills needed to perform each role, they mainly differ in that data scientists gain insights from information, while data analysts go a step further by identifying and predicting trends and patterns.

As such, data scientists use programming languages and mathematical modeling techniques to process data and visualize trends. On the other hand, data analysts use Excel or SQL to clean, organize, and analyze information. While data scientists can do the work of data analysts, the latter would need advanced programming and ML skills to become data scientists.

A Day in the Life of a Data Scientist

With all you have learned about what a data scientist does and the requirements to become one, there is no doubt that data science entails a lot of work. A data scientist deals with terabytes of data, with billions or even trillions of records. So, what does a day in the life of a data scientist look like?

You might imagine rows of computer screens, countless cups of coffee, and long work hours. But this video showing how a day in the life of a data scientist as he works from home is quite interesting.

He built new data models, presented one to a client, and was even able to work out in between and play a virtual reality (VR) game at the end of the workday.

Who Are Some Notable Data Scientists You Can Follow on Twitter?

Want to get inspiration from well-known data scientists? Here are some that you should follow:

Dean Abbott (@deanabb): Data scientists who want to learn more about data analytics should follow Abbot, the founder and president of Abbot Analytics. For several years, Abbot worked on data mining and visualization methods to address business issues.
Kenneth Cukier (@kncukier): Working as the data editor of The Economist, Cukier has also done significant research in AI. He also authored a book on big data and is often requested to speak on his knowledge of data science.
Nando de Frietas (@NandoDF): Google DeepMind’s lead scientist for ML focusing on neural networks, deep learning, and Bayesian optimization can tell aspiring data scientists about data-driven robotics.
John Elder (@johneleder4): Popular in the data mining field, Elder is the data scientist to follow if you want to learn about advanced analytics, biometrics, and text mining.
Fei-Fei Li (@drfeifie): Serving as the co-director of Stanford University’s Human-Centered AI Institute, Dr. Li has done impressive work on deep learning and data analytics.

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The field of data science is ever-growing. Along with this growth comes the increased demand for data scientists. Now that you know the answer to “What do data scientists do?,” you might want to brush up on your skills to enter the field.

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