0% completed

All LessonsFree Lessons (5)

Getting Started

What is Data Analysis?

Numpy for Python

What is NumPy?NumPy Array Creation NumPy Array Indexing Transposing of NumPy Array Processing NumPy Arrays Challenge: Find Min and Max from a 2-D NumPy Array Solution: Find Min and Max from a 2-D NumPy Array Quiz

Pandas for Python

What is Pandas?Series in Pandas DataFrame in Pandas Reindex Objects Select and Drop Entries in Series Select and Drop Entries in DataFrame Sort and Rank in Pandas Dealing with Missing Data Additional Functions and Features Challenge: Summing and Swapping Solution: Summing and Swapping Quiz

Statistics for Data Analysis

Statistical Features Probability Distributions Central Limit Theorem

Data Wrangling

Dealing with CSV Files Merging Data Reshaping Data Mapping Data and Finding Duplicates Replace and Rename Finding Outliers in Data Data Grouping Data Aggregation Split-Apply-Combine Technique Challenge: Clean the Data Solution: Clean the Data Quiz

Visualizing the Data

Visualization Tools Histogram Plots Box Plots Regression Plots Heatmaps Scatter and KDE Plots Challenge: Visualizing Seaborn Datasets Solution: Visualizing Seaborn Datasets Quiz

Data Scraping

What is Scraping?Exploring Selenium Scraping Domains Quiz

Project #1

Stock Market Analysis Section 1: Price Trends Section 2: Daily Returns Section 3: Correlation in Stocks Section 4: Risk Estimation Section 5: Predicting Future Stock Behavior

Project #2

Customer Behavior Analysis Section 1: Brand Analysis Section 2: Customer Activity Analysis Section 3: Identifying Famous Brands and Categories Section 4: RFM Analysis

Project

Stock Market Data Visualization Using Python

Conclusion

Summary What's Next?

Course Assessment

Predictive Data Analysis Exam

Predictive Data Analysis with Python/

Stock Market Data Visualization Using Python

Stock Market Data Visualization Using Python

Stock markets are constantly changing. It is difficult to predict the stock market’s fluctuations because of the many factors at play. Similarly, it is difficult to create models to consider such variability. However, due to recent advances in machine learning and computing, machines can now process large amounts of data. This enables us to use stock exchange data from the past to analyze trends and predict future changes.

This project will leverage Python to analyze stock data from the last 13 years. Our focus will be on the NIFTY-50 stock market (2008–2021) dataset publicly available on Kaggle. However, because there are around 50 different files and stocks available in the dataset, we’ll only select one file to visualize in this project.