Most coveted competencies to assess in 2023: Python for data science

Python for data science is an indispensable programming language that has surpassed the efficacy and popularity of several primary programming languages. It offers incredible simplicity and readability, thereby serving as an ideal choice for beginner data scientists. Furthermore, the language grants the advantage of using fewer lines of code to accomplish tasks that would have otherwise required extensive effort with older languages. Over the recent years, Python for data science has become the most preferable tool for scientific and research communities, primarily because of its simple syntax.

Following are a few on-the-job responsibilities for a data scientist.

Python is among the core programming languages in data science and the following reasons contribute to its popularity.

Resources to accelerate statistical and mathematical calculations

Python has several features to develop statistical models, and carry out mathematical calculations. It also supports simple mathematical operations like addition (+), subtraction (-), division (/), and multiplication (*). Furthermore, there is a math module for complex exponential, logarithmic, trigonometric, and power functions.

Python packages like NumPy, statistics, SciPy, and Pandas support various statistical tools. When dealing with linear and logistic regressions and other statistical models, the scikit-learn package comes in handy.

Outstanding data visualization

Python’s data visualization offers incredible data insights. The latter aids in comprehending trends, outliers, and correlations. Furthermore, the Matplotlib data visualization library provides plots and their adaptability. In addition, tools like Plotly and Bokeh enable seamless creation of complex plots with Python.

Vast data library

Python provides open-source libraries with features in addition to those for arithmetic, statistics, and data visualization. Various data modules can be loaded from different sources, including CSV files, Excel, etc. Additionally, there are programs for structuring and processing data from multiple sources (e.g., NLTK for unstructured data processing and Scrapy and Beautiful Soup for extracting structured website data).

Many deep learning models are built using PyTorch and TensorFlow for various cases, including facial recognition, language synthesis, object detection, etc.

Scalability

Regarding scalability, Python is among the most in-demand data scientist skills. It helps manage databases with hundreds of thousands or millions of records. Also, Python models are simple to deploy in a production environment.

In the following section, we will learn about the crucial data scientist skills that are necessary along with Python.

Although data science is a field that is constantly changing, knowing its fundamentals gives the baseline knowledge that is needed to tackle more complex ideas like deep learning and artificial intelligence. Although Python for data science is among the most vital abilities today, a few other skills are equally significant.

Data visualization reveals confidential information and helps the data “talk”. It facilitates speedy analysis that leads to good decision-making. However, before drawing any conclusions, a complete comprehension of the evidence is necessary. Techniques for data visualization provide the quickest way to browse the vast amount of currently available data.

SQL makes it easier to extract the data’s true meaning. The dataset can be visualized by using SQL to generate correct results. It handles missing numbers, outliers and other data abnormalities more easily. SQL organizes and provides insight into the dataset.

Data scientists must communicate their findings to the team members of an organization. SQL makes this simple by integrating with widely used programming languages like R and Python for data science. Complex procedures can be carried out using SQL with less time and coding. NoSQL databases give flexibility and scalability when working with large amounts of data.

Analysis, data collection, organization and interpretation are all topics covered by statistics. It allows data scientists to build statistical data models and fully comprehend the data they will be analyzing. Additionally, statistical principles like probability, regression and hypothesis testing must be understood by data scientists.

Massive amounts of data may be automatically analyzed thanks to machine learning technology. Due to the previous statistical methods being replaced, data extraction has progressed. Therefore, vast volumes of data may be analyzed using machine learning. Furthermore, creating practical, quick algorithms and data-driven models for real-time data processing can produce reliable findings.

Success in the field of data science requires knowledge of Microsoft Excel. VBA’s programming language provides valuable data analysis and statistical modeling tools. For example, using VBA, typical administrative operations like project management or accounting can be automated using macros, which are pre-recorded commands. Excel also enables data analysts to evaluate findings from unprocessed data.

Many businesses favor data scientists that have a solid grasp of complex mathematical concepts. The mathematics underlying machine learning algorithms must be understood. Introductory linear algebra and multivariable calculus are required to do the computations for a career as a data scientist.

The process of removing data from numerous platforms is known as data mining. Data scientists can utilize this data to find trends and learn information that will help businesses understand the outcomes of their analysis.

Data wrangling refers to preparing the data for analysis, transforming and mapping raw data from one form to another to prepare the data for generating insights. The acquired data combine in the relevant fields and then cleansed.

Cloud computing is used to manage and process data. A data scientist needs to analyze and visualize the data stored in the cloud. Using Cloud computing, data scientists can use platforms such as AWS (Amazon Web Services), Azure and Google Cloud to provide database access, frameworks, programming languages and tools.

Whereas DevOps combines software development and IT (Information Technology) operations that shorten the development life cycle and provide uninterrupted delivery with high software quality. DevOps teams work closely with the development teams to manage the application lifecycle. In addition, the DevOps team provides the available clusters of Apache Hadoop, Kafka, Spark, and Airflow to handle data extraction and transformation.

Which Python data science skills are most essential?

Python functionalities are used throughout the data preparation and for data aggregation before plotting. Following are the top Python skills to assess during data scientist hiring.

i. Lambda function

Lambda functions are used for cleaning multiple columns in the same way. Each attribute requires its logic behind cleaning.

ii. List comprehension

Data scientists can create lists using comprehensions using different notations. Lists can be built by leveraging strings and tuples. In addition, list comprehensions can limit the number of lines in your program.

iii. Zip

This is one of the built-in Python methods. It iterates over two or more lists simultaneously.

Mercer | Mettl’s suite of domain assessments helps recruiters understand the professional competency of applicants through online evaluation.

Mercer | Mettl also offers a comprehensive candidates assessment for judging their coding skills in Python language that helps recruiters decide on the applicants’ working skills. This test assesses a developer’s ability to focus on the core application functionality. The candidate’s skills are evaluated on three levels: basic, intermediate, and advanced.

Competencies tested in Python assessment

Programming skills

It evaluates the candidate’s coding skills on a coding simulator with the help of real-world problems and gauges their hands-on experience and coding skills.

Advanced concepts

The advanced concepts of Python skills, such as file handling, data classes and OOPs (Object Oriented Programming), are tested.

Basics of Python for data science

The test helps evaluate a candidate’s subskills, such as Strings, Lists and Functions.

Advanced topics

It evaluates the following skills in a candidate: Regex and Classes and Operators on basic and intermediate levels.

Related links:

Customizable Python online test for recruitment

Mercer | Mettl’s programming skills test

Mercer | Mettl’s technical assessments

Mercer | Mettl’s coding tests

Django test to hire Python Django programmer

Python GIT skills assessment

Data science is among the most popular fields for industrial application. Several data scientist skills covered in this article must be taught to process these applications.

This post offers insights into the qualifications candidates should possess and what helps improve data scientist hiring.