Top 8 Data Scientist Skills to Pursue the Career in Big Data

Data science skills you need

The need for qualified data scientists continues to grow as the big data industry rapidly expands. Data science is a relatively new field that requires individuals to have a diverse range of skills. If you are interested in pursuing a career in data science or are simply curious about what skills are needed to succeed in this discipline, read on! We have the top 8 data scientist skills to help you achieve your career aspirations.

Data Science: Industry Overview

The data science industry is one of the hottest and most rapidly growing industries today. The field incorporates aspects of data mining, machine learning, statistics, and computer science to make sense of large data sets. Data scientists are in high demand as organizations seek professionals responsible for helping make better business decisions through data analysis.

There are many reasons why data science is such a popular and in-demand field. It includes various disciplines, which can provide valuable insights while put together. Such disciplines include but are not limited to:

  1. Data engineering
  2. Data preparation
  3. Data mining
  4. Predictive analytics
  5. Machine learning
  6. Data visualization

Data science teams are often composed of individuals with different skill sets, which is what makes the field so unique and powerful. These multifunctional professionals can solve complex business problems and make informed decisions about the strategies and products of an organization. Data science teams often have access to more data than ever before, making them a valuable asset to any developing company.

The skills for data science are changing as the industry evolves. Data scientists need to be able to adapt to new technologies and methods to keep pace. Old problems require new solutions, and that’s how data specialists contribute to the bottom line. Let’s see how far we’ve come and explore the top 8 skills for data science in 2022 and beyond.

Data Scientist Skills to Boost Your Professional Progress

Programmers working in an open space office

Skill #1: Programming and Database

A data scientist should know how to code. It is one of the most essential skills for a specialist aiming to reach the top of their field. The ability to code gives a data scientist the power to automate tasks, build models, and deploy solutions.

Moreover, data scientists use programming languages to clean, manipulate, and analyze data. Database management is another critical skill for this profession. While storing, retrieving and manipulating data across various database technologies, professionals must have a good understanding of data structures, schema design, and SQL (Structured Query Language).

For instance, knowing how to code in JavaScript may be handy when working with JSON (JavaScript Object Notation) data. Data scientists often use Python for data analysis and R for statistical computing. Learning Python may seem challenging. However, with the right resources and motivation, it is achievable.

Skill #2: Data Visualization

Data visualization is a critical skill for data scientists. It implies the ability to take complex data sets and turn them into easy-to-understand graphs, charts, and maps. Data visualization allows analysts to communicate their findings to stakeholders clearly and concisely.

Furthermore, data visualization helps specialists identify patterns and trends that would otherwise be hidden in raw data sets. Data scientists commonly use visualization tools such as Tableau and D three.js to create interactive visualizations. However, to use them, a data scientist still needs coding proficiency.

Skill #3: Knowledge of Multivariable Calculus and Linear Algebra

Linear algebra and multivariable calculus for programmers

Data scientists are often required to understand statistics and mathematics deeply. Multivariable calculus and linear algebra are two mathematical disciplines that data scientists rely on heavily.

Multivariable calculus is the study of functions of multiple variables and their derivatives. Data scientists use it to optimize machine learning models. Linear algebra, on the other hand, is the study of vectors and matrix operations. Data scientists use it for dimensionality reduction, data transformation, and feature engineering.

A data scientist should be proficient in mathematical disciplines to build accurate models and draw sound conclusions from data sets.

Skill #4: Data Wrangling

Data munging (data wrangling) is essential for anyone working with data sets. The process involves discovering, structuring, cleaning, enriching, validating and publishing. Data wrangling is a time-consuming but necessary process that allows data scientists to perform such actions as:

  • Identify data gaps, fill or delete them
  • Convert data formats
  • Detect and correct errors
  • Add, remove, or change values
  • Aggregate data into new units
  • Create new features from existing data

Skill #5: Web Scraping

Data mining or web scraping is extracting data from sources that are not intended to be accessed or analyzed. Using web scraping, data scientists can automatically gather structured data from websites and use it for their research.

Data scientists use web scraping to get either unavailable data through an API or behind a paywall. For instance, data scientists can use web scraping to get pricing data from eCommerce websites or job postings from job boards.

Although many automated tools help users lacking coding skills collect the information their need, professionals can create unique codes to scrape data from specific sources.

Hint: Web scraping is impossible without coding proficiency. Thus, sparing time to learn Swift or any other language will upscale your professional skills and make you more marketable.

Skill #6: Machine Learning

Machine learning is a subset of artificial intelligence that allows computers to learn from data sets and improve their predictions over time. Machine learning is mainly used for predictive analytics. This implies using data to predict future trends and behaviors.

Machine learning is a vast field that covers many topics, such as regression, classification, neural networks, deep learning, and natural language processing. Data scientists use machine learning to build models that can automatically make predictions or recommendations.

To work with machine learning, data scientists need to understand the mathematical foundations of the algorithms used. Being experienced with programming languages such as Python, R, and Java will also be a great advantage.

Skill #7: Problem-Solving Skills

Data science is a problem-solving discipline. The specialists get tasks that require designing and developing algorithms, analyzing data sets, or finding new ways to achieve business goals. To solve them, data scientists need excellent problem-solving and communication skills. While most of our time is spent working with data, a significant portion is also spent discussing the findings with our colleagues. This requires sharp communication skills to explain the conclusions comprehensively.

Skill #8: Critical Thinking

Problem-solving and critical thinking go hand in hand. Data scientists need to think outside the box to find the best solution. They need a skillset to see the big picture and understand how their work will impact the business as a whole.

The Bottom Line

These are eight essential skills that every data scientist should have. While some can be learned, others require experience and natural talent. However, anyone can become a demanded specialist with the right attitude and a willingness to learn continuously.

Data science evolves at a crazy pace! The global data science platforms market is expected to grow at a CAGR of 16.43% by 2030. This is huge! Thus, staying up-to-date with the latest trends and best practices is your ace in the hole.