Data Analytics with Python

Introduction to Python; installation and working environment; Python syntax and semantics; variables, data types, operators; input/output operations; control flow basics; introduction to data analytics and its role in business decision-making; overview of Python libraries used in analytics.

Lists, tuples, sets, and dictionaries; indexing and slicing; operations on data structures; introduction to NumPy arrays; array operations and mathematical functions; handling structured and unstructured data; reading and writing datasets (CSV, Excel); introduction to Pandas library.

DataFrames creation and operations; importing and exporting data; data cleaning and preprocessing; handling missing values; filtering, sorting, grouping, and aggregation; merging and joining datasets; data transformation techniques; business dataset preparation.

Descriptive statistics (mean, median, mode, variance, standard deviation); correlation and covariance; distribution analysis; outlier detection; pivot tables; trend analysis; basic hypothesis understanding; business interpretation of statistical results.

Data visualization using Matplotlib and Seaborn; bar charts, line charts, histograms, scatter plots, and box plots; customization of graphs; dashboard basics; storytelling with data; applications of Python analytics in marketing, finance, HR, and operations; case-based business analytics. Practical Exercises Python installation and demonstration of coding Data cleaning and manipulation using Pandas Performing EDA on business datasets Creating statistical summaries and interpreting results Building visualisations using Matplotlib and Seaborn