Notable data science skills for 2023

Published by Analyttica

The whole profession of data science that we so admire today is a perfect combination of analytical expertise and problem-solving abilities. It has proven to be an invaluable career path in a competitive job market. However, breaking into this field is no easy feat and demands certain notable skills, a few of which are discussed below. 

  1. Knowing the basics

Nowadays, understanding the basic principles of data science, machine learning, and artificial intelligence is one of the most crucial skills for data scientists. This will include focusing on topics such as –

  • The difference between data science, business analytics, and data engineering.
  • Thorough knowledge of popular terminologies and tools.
  • How supervised and unsupervised learning will make a difference in business.
  1. Focusing on data cleaning

One of the significant aspects determining the reliability of outcomes and algorithms is the accuracy of the dataset. This makes it integral to possess the ability to clean data after the merging of multiple data sources. Data cleaning, in the most generic sense, refers to the act of detecting and rectifying inaccurate records. It aims at finding loopholes such as missing values that arise due to human error or nonresponses to optional features. This may impact the model’s performance and should be treated accordingly. Another objective of data cleaning is to eliminate any inconsistencies like outliers or data objects that are entirely deviated from the rest of the set.

  1. Grasping data manipulation

More often than not, the collected raw data fails to make any sense. During such circumstances, the data scientist must be able to manipulate the data by organizing, rearranging, modifying, and shifting. This, along with the process of combining and eliminating redundancies, can help create a comprehensive database to meet corporate demands.

  1. Understanding mathematical concepts

It is obvious that while writing sentences, one needs to be familiar with grammar to construct proper sentences. Similarly, understanding mathematical concepts like statistics and probability can come in handy when creating high-quality models. This, combined with the concept of linear regression, is a must-have skill for data scientists in 2022.

  1. Knowledge of Programming Languages

These days programming languages have become the life-juice of enterprises. Its ability to solve technological concerns and modify sales has made it leverage for data scientists. Some of these programming languages include:

  • Python – It can handle everything from data mining to website development to running embedded systems in a single language.
  • R Programming – R is a software package that can help with data manipulation, calculation, and graphical display functions.
  1. Mastering data visualization

Nowadays, hardly anyone has the time and patience to go through vast paragraphs of data. Instead, they find visual information much easier to grasp and analyze. This makes it increasingly essential for data scientists to be proficient in the concept of data visualization. It refers to portraying data and information using graphical components such as diagrams, charts, and maps. In this age of big data, this pleasant-to-the-eye skill can undoubtedly come in handy for making data-driven decisions.

  1. Proficiency in Machine Learning

When it comes to data science skills, comprehensive knowledge of machine learning can certainly be helpful. It can aid in crafting predictive models. For instance, if you want to forecast how many clients you’ll have in the upcoming month based on the previous month’s data, you’ll need to employ machine learning techniques.

Final takeaway

Having a career in data science can be a rewarding endeavor, both personally and professionally. However, enhancing these required skill sets demands a robust education platform. LEAPS is one such space that offers aspirants and professionals a chance to work with pre-loaded data cases and experiment with their datasets. Its innovative assessment framework, collaborative problem-solving environment, and numerous other features make it an exclusive platform for upskilling in data, analytics, ML & Ai like none other.