Introduction
Business analysts use Python libraries to streamline data analysis, enhance their capabilities, and automate repetitive tasks. Python’s versatility and extensive ecosystem of libraries make it a powerful tool for various aspects of business analysis. Any business analyst course will include topics on how Python libraries are used in analytics.
This write-up describes some key Python libraries and how business analysts utilise them.
Python Libraries in Business Analytics
The following sections describe how various Python libraries are used for various purposes in business analytics.
Data Manipulation and Cleaning
Pandas:
Purpose: Data manipulation and analysis.
Usage: Business analysts use Pandas to load, clean, transform, and analyse large datasets. It provides data structures like DataFrames, which are ideal for handling and manipulating structured data.
NumPy:
Purpose: Numerical computing.
Usage: NumPy is used for handling large arrays and matrices of numerical data. It provides mathematical functions to perform operations on these arrays efficiently.
Data Visualisation
Matplotlib:
Purpose: Creating static, interactive, and animated visualisations.
Usage: Business analysts use Matplotlib to create various charts and plots (for example, line plots, bar charts, histograms) to visualise data trends and patterns.
Seaborn:
Purpose: Statistical data visualisation.
Usage: Built on top of Matplotlib, Seaborn provides a high-level interface for drawing attractive and informative statistical graphics. It is used for visualising complex relationships in data. The data visualisation topics in a business analysis course generally cover Seaborn and its usage in detail.
Plotly:
Purpose: Interactive web-based visualisations.
Usage: Plotly allows business analysts to create interactive plots, dashboards, and web applications, which are useful for presentations and sharing insights with stakeholders.
Statistical Analysis
SciPy:
Purpose: Scientific and technical computing.
Usage: SciPy builds on NumPy and provides additional modules for optimisation, integration, interpolation, eigenvalue problems, and other advanced mathematical functions. It is used for performing statistical tests and advanced data analysis.
Statsmodels:
Purpose: Statistical modelling.
Usage: Statsmodels allows business analysts to explore data, estimate statistical models, and perform hypothesis tests. It is commonly used for regression analysis and time series analysis.
Machine Learning
Scikit-Learn:
Purpose: Machine learning.
Usage: Scikit-Learn provides simple and efficient tools for data mining and data analysis, including classification, regression, clustering, and dimensionality reduction. Business analysts use it to build and evaluate predictive models.
TensorFlow and Keras:
Purpose: Deep learning.
Usage: TensorFlow and Keras are used for building and training deep learning models. While these are more advanced tools, business analysts may use them for complex predictive modelling tasks.
Natural Language Processing (NLP)
NLTK (Natural Language Toolkit):
Purpose: Text processing and linguistic data analysis.
Usage: NLTK is used for text classification, tokenisation, stemming, tagging, parsing, and semantic reasoning. Business analysts use it to analyse text data from customer reviews, social media, and other sources. With NLP assuming immense popularity, usage of these libraries is a common topic in any business analyst course.
SpaCy:
Purpose: Industrial-strength NLP.
Usage: SpaCy is used for advanced NLP tasks such as named entity recognition, part-of-speech tagging, and dependency parsing. It is known for its performance and efficiency.
Data Extraction and Web Scraping
BeautifulSoup:
Purpose: Web scraping.
Usage: BeautifulSoup is used to parse HTML and XML documents and extract data from web pages. Business analysts who have gained practical knowledge of its usage by attending a business analyst course can easily gather data from websites for analysis using BeautifulSoup.
Scrapy:
Purpose: Web scraping framework.
Usage: Scrapy is an open-source and collaborative web crawling framework for Python. It is used to extract data from websites and process it as per business needs.
Automation and Workflow Management
Airflow:
Purpose: Workflow automation and scheduling.
Usage: Airflow is used to programmatically author, schedule, and monitor workflows. Business analysts use it to automate data pipelines and ensure data is processed and analysed on schedule.
API Interaction
Requests:
Purpose: HTTP requests.
Usage: Requests is used to interact with web services and APIs. Business analysts use it to retrieve data from external sources, such as financial data from APIs, social media data, and more.
Database Interaction
SQLAlchemy:
Purpose: SQL toolkit and Object-Relational Mapping (ORM).
Usage: SQLAlchemy is used to interact with databases in a more Pythonic way. It allows business analysts to query databases, perform CRUD operations, and manage database connections efficiently.
Conclusion
By leveraging these Python libraries, business analysts can perform comprehensive data analysis, create insightful visualisations, build predictive models, automate workflows, and interact with data sources and APIs effectively. This enhances their ability to derive actionable insights and support data-driven decision-making processes. The skills to use Python libraries are highly in demand in job markets. Business professionals can acquire the expertise in using these libraries with the systematic learning from a quality business analysis course.
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