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Course Introduction
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Classroom Contents
Data Analysis with Python Course - Numpy, Pandas, Data Visualization
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- 1 Course Introduction
- 2 Python Programming Fundamentals
- 3 Course Curriculum
- 4 Notebook - First Steps with Python and Jupyter
- 5 Performing Arithmetic Operations with Python
- 6 Solving Multi-step problems using variables
- 7 Combining conditions with Logical operators
- 8 Adding text using Markdown
- 9 Saving and Uploading to Jovian
- 10 Variables and Datatypes in Python
- 11 Built-in Data types in Python
- 12 Further Reading
- 13 Branching Loops and Functions
- 14 Notebook - Branching using conditional statements and loops in Python
- 15 Branching with if, else, elif
- 16 Non Boolean conditions
- 17 Iteration with while loops
- 18 Iteration with for loops
- 19 Functions and scope in Python
- 20 Creating and using functions
- 21 Writing great functions in Python
- 22 Local variables and scope
- 23 Documentation functions using Docstrings
- 24 Exercise - Data Analysis for Vacation Planning
- 25 Numercial Computing with Numpy
- 26 Notebook - Numerical Computing with Numpy
- 27 From Python Lists to Numpy Arrays
- 28 Operating on Numpy Arrays
- 29 Multidimensional Numpy Arrays
- 30 Array Indexing and Slicing
- 31 Exercises and Further Reading
- 32 Assignment 2 - Numpy Array Operations
- 33 100 Numpy Exercises
- 34 Reading from and Writing to Files using Python
- 35 Analysing Tabular Data with Pandas
- 36 Notebook - Analyzing Tabular Data with Pandas
- 37 Retrieving Data from a Data Frame
- 38 Analyzing Data from Data Frames
- 39 Querying and Sorting Rows
- 40 Grouping and Aggregation
- 41 Merging Data from Multiple Sources
- 42 Basic Plotting with Pandas
- 43 Assignment 3 - Pandas Practice
- 44 Visualization with Matplotlib and Seaborn
- 45 Notebook - Data Visualization with Matplotlib and Seaborn
- 46 Line Charts
- 47 Improving Default Styles with Seaborn
- 48 Scatter Plots
- 49 Histogram
- 50 Bar Chart
- 51 Heatmap
- 52 Displaying Images with Matplotlib
- 53 Plotting multiple charts in a grid
- 54 References and further reading
- 55 Course Project - Exploratory Data Analysis
- 56 Exploratory Data Analysis - A Case Study
- 57 Notebook - Exploratory Data Analysis - A case Study
- 58 Data Preparation and Cleaning
- 59 Exploratory Analysis and Visualization
- 60 Asking and Answering Questions
- 61 Inferences and Conclusions
- 62 References and Future Work
- 63 Setting up and running Locally
- 64 Project Guidelines
- 65 Course Recap
- 66 What to do next?
- 67 Certificate of Accomplishment
- 68 What to do after this course?
- 69 Jovian Platform
