Data Analytics – Python / Pandas / NumPy

Requirements:

• Have intermediate experience in Python for data analysis and processing.
• Proficient in using Pandas for data manipulation, cleansing, transformation, and aggregation.
• Familiar with NumPy for numerical analysis and data processing.
• Able to perform data cleansing, including handling missing values, duplicates, inconsistent formats, and invalid data.
• Able to perform data transformation and normalization to prepare datasets for further analysis.
• Experienced in performing data aggregation and grouping using Pandas.
• Able to perform correlation analysis to identify relationships between different variables or datasets.
• Familiar with Exploratory Data Analysis (EDA) to identify trends, patterns, anomalies, and data distributions.
• Able to work with large and multi-source datasets, preferably including network or telecommunications data.
• Familiar with handling time-series data using Python/Pandas.
• Able to create basic scripts for simple data processing and automation, such as recurring data cleansing, transformation, aggregation, or reporting tasks.
• Familiar with integrating data from CSV, Excel, SQL databases, or other structured data sources.
• Able to document data-processing steps and analytical findings clearly.
• Strong attention to detail and problem-solving skills.
• Experience with Matplotlib, Seaborn, or other visualization libraries is an advantage.

Key Competency:

Python / Pandas / NumPy — Intermediate proficiency in using Python for data cleansing, transformation, aggregation, correlation analysis, exploratory data analysis, and simple automation, with the ability to process and derive insights from structured and multi-source datasets.