Data Analysis with Pandas: 3-in-1 | Database Design & Development Online Course by Udemy

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Learn financial data analysis, modeling, and visualization with Pandas

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Data Analysis with Pandas: 3-in-1

Pandas is a popular open-source Python package that provides fast, high performance data structures for performing efficient data manipulation and analysis. It has quickly emerged as a popular choice of tool for analysts to solve real-world analytical problems. The Pandas library is massive, and it’s common for frequent users to be unaware of many of its more impressive features. This comprehensive 3-in-1 course is a step-by-step, a highly practical course showing you the whys and hows of applying Pandas for your data analysis tasks. Solve most complex scientific computing problems with ease using the power of Pandas. Manipulate, analyze and visualize your data using the popular Pandas library. Enhance your data exploration and machine learning skills by gaining surprising insights from Pandas and using expert tips and tricks. This training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible. The first course, Exploratory Data Analysis with Pandas and Python 3.x, will take you from Python basics to explore many different types of data. Throughout the course, you will be working with real-world datasets to retrieve insights from data. You’ll be exposed to different kinds of data structure and data-related problems. You’ll learn how to prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, predict future trends from data, and more! The second course, Modeling and Visualization of Data in Pandas, will support you as you work through a typical real-world data analysis project step-by-step using Pandas. It develops the deep knowledge and skills that will enable you to immediately tackle your own projects with Pandas at work. It also demonstrates how to make financial models using Python’s software library for data manipulation and analysis. The third course, Mastering Python Data Analysis with Pandas, starts off with covering the basics of Pandas which includes data structures and various data manipulation tasks such as grouping, merging, and reshaping data. You will then learn how to manipulate, analyze, and visualize your time-series financial data. You will also learn how to apply Pandas to important but simple financial tasks such as modeling portfolios, calculating optimal portfolios based upon risk, and more. This course not only teaches you why Pandas is a great tool for solving real-world problems in quantitative finance, but it also takes you meticulously through every step of the way, with practical, real-world examples, especially from the financial domain where Pandas is a popular choice. By the end of the course, youll get insights and solutions to common data problems while working on real-world datasets using Pandas library. Meet Your Expert(s):We have the best work of the following esteemed author(s) to ensure that your learning journey is smooth: Mohammed Kashif works as a Data Scientist at Nineleaps, India, dealing mostly with graph data analysis. Prior to this, he worked as a Python developer at Qualcomm. He completed his master’s degree in Computer Science from IIT Delhi, with a specialization in data engineering. His areas of interests include recommender systems, NLP, and graph analytics. In his spare time, he likes to solve questions on Stack Overflow and help debug other people out of their misery. He is also an experienced teaching assistant with a demonstrated history of working in the Higher Education industry. Prabhat Ranjan has extensive industry experience in Python, R, and machine learning. He has a passion for using Python, Pandas and R for various real-time, new-project scenarios. As a trainer, he also has a passion for teaching concepts and advanced scenarios in Python, R, Data Science, and Big Data Hadoop. Thus, his teaching experience and strong industry exposure make him one of the best in this domain.

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