Author: Usman Malik

Python is one of the fastest growing programming languages in the world. It is becoming equally popular among novice and experienced programmers and taking over rival languages such as C++ and Java. One of the biggest reasons why Python's popularity has skyrocketed is its simplicity and flexibility. Even people with no prior programming knowledge can learn Python within weeks. In fact, Python has all the characteristics to become the lingua franca of coding in the near future.

Excel spreadsheets are quickly becoming obsolete thanks to the emergence of the latest data analytics tools and languages such as Python, Java, R, and Microsoft HDInsight. However, a large number of companies still use digital spreadsheets, creating a lot of problems for modern business data analysts. Analyzing data through excel is a poor choice because of reasons like errors in data validation, a poor shared workbook feature, no multi-user editing, inaccurate data, and safety concerns, making it necessary for you to switch to better and advanced alternatives.

Ever wondered how you can use Python to create stunning data visualizations? In the first part of this series, we saw how to draw line plots and histograms using the matplotlib library. We also saw how to change the default size of a plot and how to add titles, axes, and legends to a plot. In this article, we’ll see a few more types of plots that can be drawn using the matplot library with pro tips on how to improve them.

Want to know how Python is used for plotting and data visualization? Interested in learning one of the most commonly used data visualization libraries in Python? If so, you’re in the right place. In this installment of a two-part tutorial, we’ll learn how to use matplotlib, one of the most commonly used data visualization libraries in Python. Over the course of both articles, we’ll create different types of graphs, including:

Python is one of the most frequently used programming languages for financial data analysis, with plenty of useful libraries and built-in functionality. In this article, you’ll see how Python’s machine learning libraries can be used for customer churn prediction. Customer churn is a financial term that refers to the loss of a client or customer—that is, when a customer ceases to interact with a company or business. Similarly, the churn rate is the rate at which customers or clients are leaving a company within a specific period of time.

How can Python’s pandas library be used to analyze time series data? Let’s find out. The pandas library is frequently used to import, manage, and analyze datasets in a variety of formats. In this article, we’ll use it to analyze Microsoft’s stock prices for previous years. We’ll also see how to perform basic tasks, such as time resampling and time shifting, with pandas. What is Time Series Data? Time series data contains values dependent on some sort of time unit.

Regression analysis is one of the most fundamental tasks in data-oriented industries. In simple words, it involves finding a relationship between independent and dependent variables (attributes) in a given dataset. Consider the example of a house price prediction problem—given the size and number of bedrooms, we want to predict the price of a house. This is a simple regression problem where the size of the house and the number of bedrooms are the independent variables and the price of the house is the dependent variable.

An increasing number of fintech companies are using Python for data analysis. But what makes Python so special? And why is it a better language for data analysis compared to traditional software? Python is quickly becoming the most popular coding language in the world. Currently, it’s perching comfortably in the fourth spot after Java, C, and C++ on the Tiobe Index of Language Popularity. And the Popularity of Programming Language Index ranks Python as the most popular programming language in the world in October 2018.