Dash is an open-source framework from the company Plotly for building interactive web applications for data analysis in Python, without writing JavaScript yourself. Charts, filters and sliders are linked directly to Python code. Typical examples are dashboards on which business teams filter sales by region or try out a machine learning model's predictions with their own values.
pandas is an open-source Python library for analysing and manipulating tabular data, developed from 2008 onwards by Wes McKinney. Its central object, the DataFrame, resembles a table with named columns. It can be used to load data from CSV files, Excel or databases and to clean, filter, group and merge it. pandas is built on NumPy and is a standard tool in data analysis, reporting and training data preparation.
Jupyter Notebook is an open-source, browser-based application from Project Jupyter that combines executable code, results, charts and explanatory text in a single document. Notebooks are widely used in data analysis, machine learning, research and teaching because each step can be documented and shared in a traceable way. Python is the most common language. Example: an analyst loads sales data, cleans it and shows the results as a chart directly below.
Exploratory data analysis (EDA) is the first, open-ended look at a dataset: you calculate summary figures, examine distributions and charts and look for patterns, outliers and missing values – without a fixed hypothesis yet. It helps assess data quality and find sensible questions for further analysis or modelling. Example: before a sales forecast, EDA reveals that one month was recorded incompletely because of a system migration.
NumPy is the fundamental open-source Python library for numerical computing. It provides multidimensional arrays and fast functions for linear algebra, statistics and random numbers, with its core running in compiled code. Many data analysis and machine learning tools, such as pandas, scikit-learn and numerous visualisation libraries, are built on NumPy. Anyone working with data in Python therefore almost always uses it, directly or indirectly.
