Python Data Science Complete Guide

Course details

This course is designed for anyone who wants to learn Python for data science. No prior experience with Python is required. The course covers all the essential topics in a clear and concise way. You’ll learn by doing, with plenty of exercises and projects to give you real-life examples of what you’ve learned. This course unlocks the potential of Python, Pandas, and NumPy, equipping you with the tools to reshape, analyse, and visualise data. From the fundamentals of strings and numbers to the intricacies of data frames and arrays, you’ll traverse the landscape of data manipulation.
Explore Pandas and NumPy’s dynamic functionalities, enabling you to conquer missing data, aggregate insights, and navigate complex datasets. You’ll delve into CSV and JSON interactions, merge and pivot data, and craft compelling visualisations using Matplotlib.
As you traverse the course’s labyrinth, you’ll encounter the dynamic duo of Pandas and NumPy, unlocking their potential to sculpt and refine data. From unravelling the mysteries of data frames to orchestrating multi-dimensional arrays, you’ll ascend to new heights of data prowess. With the power to manage missing data and navigate intricate hierarchies, you’ll forge a path towards insights that others overlook.
Learning outcome:

• Master Python's core concepts and syntax.
• Harness Pandas for data manipulation, including summarisation and grouping.
• Utilise NumPy arrays and multi-dimensional arrays for efficient data handling.
• Manage missing data and explore hierarchical indexing strategies.
• Excel in data import/export with CSV, JSON, and Excel sheets.
• Create insightful visualisations with Matplotlib, from graphs to histograms.

Course Curriculum:

• Course Introduction and Table of Contents
• Introduction to Python, Pandas and Numpy
• System and Environment Setup
• Python Strings
• Python Numbers and Operators
• Python Lists
• Tuples in Python
• Sets in Python
• Python Dictionary
• NumPy Library - Introduction
• NumPy Array Operations and Indexing
• NumPy Multi-Dimensional Arrays
• Introduction to Pandas Series
• Introduction to Pandas Dataframes
• Pandas Dataframe conversion and drop
• Pandas Dataframe summary and selection
• Pandas Missing Data Management and Sorting
• Pandas Hierarchical-Multi Indexing
• Pandas CSV File Read Write
• Pandas JSON File Read Write
• Pandas Concatenation Merging and Joining
• Pandas Stacking and Pivoting
• Pandas Duplicate Data Management
• Pandas Mapping
• Pandas Grouping
• Pandas Aggregation
• Pandas Binning or Bucketing
• Pandas Re-index and Rename
• Pandas Replace Values
• Pandas Dataframe Metrics
• Pandas Random Permutation
• Pandas Excel sheet Import
• Pandas Condition Selection and Lambda Function
• Pandas Ranks Min Max
• Pandas Cross Tabulation
• Matplotlib Graphs and plots
• Matplotlib Histograms

Updated on 17 October, 2023