Delve into foundational Python programming, explore libraries like pandas and Matplotlib, and gain insights into data management, analysis, and visualization with practical projects.

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Python is one of the most popular programming languages for data science and analytics. It’s used across a wide range of industries. It’s easy to learn, highly flexible, and its various libraries can expand functionality to natively perform statistical functions and plotting.

This course is a comprehensive introduction to statistical analysis using Python. You’ll start with a step-by-step guide to the fundamentals of programming in Python. You’ll learn to apply these functions to numerical data. You’ll first look at strings, lists, dictionaries, loops, functions, and data maps. After mastering these, you’ll take a deep dive through various Python libraries, including pandas, NumPy, Matplotlib, Seaborn, and Plotly. You’ll wrap up with guided projects to clean, analyze, and visualize unique datasets using these libraries.

By the end of this course, you will be proficient in data science, including data management, analysis, and visualization.

Introduction to Data Science with Python


## Task 1: Combining all datasets
Combine all the bank (RBC, CIBC, BMO) and oil company (Suncor, Encana, CNQ) datasets into a single dataset `bo` (b for bank and o for oil) and check the head of the dataset.


Hint: Use `tickers = ['RBC', 'CIBC', 'BMO', 'Suncor', 'Encana','CNQ']` to add a hierarchical index at the outermost level of the data with the `keys` option in `pd.concat()`.

To see the sample output, run the code segment below:


# Task 1: Combining all datasets
Combine all the bank (RBC, CIBC, BMO) and oil company (Suncor, Encana, CNQ) datasets into a single dataset `bo` (b for bank and o for oil) and check the head of the dataset.


Hint: Use `tickers = ['RBC', 'CIBC', 'BMO', 'Suncor', 'Encana','CNQ']` to add a hierarchical index at the outermost level of the data with the `keys` option in `pd.concat()`.

To see the sample output, run the code segment below:

Let’s practice what you’ve learned so far by create a data set from our CSV files and doing some modifications.

Introduction

Python Essentials

NumPy: Data Analysis

Pandas: Data Analysis

Matplotlib: Data Visualization

Seaborn: Statistical Data Visualization

Data Visualization with Seaborn for Walmart Sales Projection

Pandas: Data Visualization

Data Visualization with Seaborn for Walmart Sales Projection

Plotly: Interactive Data Visualization

Project 1: Oil vs Banks During Recession

Project 2: Emergency Calls Dataset

In Closing

Practice: Oil vs Banks During Recession

Task 1: Combining all datasets