Python data science coding test is for recruiters and hiring managers to assess a data scientist's Python skills. This test for data science helps many enterprises to identify the right fit candidates and reduce time-to-hire by 40% and hiring costs by 45%.
Data science experts have continued with their increasing development in Python for data analysis. This process mostly includes learning the Python fundamentals in the data science domain along with learning a few Python data science libraries, for example, NumPy, Pandas, Matplotlib, Scikit-Learn, etc. During this process, building a portfolio is also important, i.e., adapting Data Cleansing Projects, Data Visualization Projects, Machine Learning Projects, etc. Hence, Python is used at every step in the data science process. For e.g., data scientists can use Python and Panda’s library to clean and sort the data into a data frame (table), which is ready for analysis, exploring, and visualizing the data.
Python data science coding test helps tech recruiters and hiring managers to assess candidates’ data science with Python skills. Python data science Online test is designed by experienced Subject Matter Experts (SMEs) to evaluate and hire data scientists with Python as per the industry standards.
Python Data Science coding test helps to screen the candidates who possess traits as follows:
Python coding test for data science has a powerful reporting feature that will help you get an instant result and share this result with your recruiting team. You can use a ready-to-use assessment or ask us to custom-make the skills assessment as per your job description.
Data science with Python tests may contain MCQs (Multiple Choice Questions), MCQs (Multiple Answer Questions), Fill in the Blanks, Whiteboard Questions, Audio / Video Questions, AI-LogicBox (AI-based Pseudo-Coding Platform), Coding Simulators, True or False Questions, etc.
You are trying to plot a bar chart on a polar axis with given colors for every bar. You want to set the intensity of colors to 0.5. Which of the following is the correct way to do so?
Refer to the given sample bar chart on the polar axis with a color intensity of 0.5.
Options