
Learn to sort dictionaries by keys and by values in Python using sorted with a lambda, and apply map, filter, and reduce to manipulate sequences.
Explore object oriented programming concepts in Python, including classes, objects, attributes, methods, inheritance, and polymorphism, then dive into numpy for multi-dimensional arrays and key operations.
Explore inheritance and polymorphism through a school example in Python. Learn to implement parent and child classes, and use super or parent class calls to access base attributes.
Explore numpy arrays, including 1d to 3d structures, slicing, indexing, reshaping, and element-wise operations, with practical examples on shape, random data, and basic functions.
Explore numpy array operations and practical module usage in Python, including os for directory handling, listing files, renaming and removing files, and date time for runtime measurements and custom formatting.
learn to build pandas dataframes from lists or dictionaries. access data with loc, iloc, or bracket notation, and handle missing values with isna or isnull, and simple operations like sum.
Learn to convert unstructured text to structured data by reading with pandas, using read_clipboard, read_csv, read_excel, and read_json, and clean data for data frames.
Learn practical data cleaning and preprocessing in python using pandas, including date parsing, handling nulls, trimming spaces, regex-based string cleaning, and outlier removal with quantiles for reliable time series analysis.
Analyze a data assignment by cleaning e-commerce order data, computing expected courier charges with weight slabs and zone charges in a jupyter notebook, and flagging undercharged or overcharged orders.
Explore assignment solution for data analysis with python: clean data, remove duplicates, merge tables, calculate shipment weights, slabs and charges, and export results to multiple excel sheets.
Explore exploratory data analysis techniques beyond visualization, including sampling, univariate and multivariate analyses, time series, bar and pie charts, and correlation heatmaps.
In this comprehensive course, "Data Analysis with Python," you will embark on a journey to become a proficient data analyst equipped with the essential skills and tools needed to analyze, visualize, and interpret data effectively. This course is designed for beginners and professionals alike, providing a solid foundation in data analysis using Python.
Throughout the course, you will:
Learn the fundamentals of Python programming and its application in data analysis.
Explore key libraries such as pandas and NumPy for data manipulation and analysis.
Gain expertise in data cleaning, preprocessing, and handling missing values.
Develop skills in exploratory data analysis (EDA) and create insightful visualizations using Matplotlib and Seaborn.
Understand the principles of file handling and data importing from various sources including CSV, JSON, and Excel.
Apply advanced techniques such as object-oriented programming (OOP) and work on real-world data analysis projects.
Learn to gather data from APIs, perform linear algebra operations with NumPy, and execute a comprehensive capstone project.
By the end of this course, you will have the confidence and skills to tackle complex data analysis tasks, making you a valuable asset in any data-driven organization.
Whether you are an aspiring data analyst, a professional looking to enhance your data skills, or a student interested in data science, this course will provide you with the knowledge and hands-on experience needed to excel in the field of data analysis.