Get yourself trained on Complete Guide to with this Online Training Complete Guide to NumPy and Pandas.
Online Training Complete Guide to NumPy and Pandas
Pandas have emerged as a popular tool for analysts to solve real-world analytical problems, Performing Data Visualization, Data Ingestion, Data Wrangling & much more.This practical course gets you started with very basic of pandas such as introducing to fundamental data structures in pandas and the different data types and indexing. Then you will learn the most important Python packages used by Data Analysts by diving into Pythons NumPy package, which is Pythons powerful extension with advanced mathematical functions. Finally, you will learn how to apply Pandas to important but simple financial tasks such as modelling portfolios, calculating optimal portfolios based upon risk, and more.Contents and OverviewThis training program includes 4 complete courses, carefully chosen to give you the most comprehensive training possible.The first course, Learning Pandas will show you how you can get the most out of pandas for data analysis. The course starts with teaching you the absolute basics such as installing and setting up of the pandas library. You will be introduced to fundamental data structures in pandas and the different data types and indexing. You will then implement different kinds of data, indexing, and handling missing data. The course will also teach you how to analyze and model your data, and organize the results of your analysis in the form of plots or other visualization means. Throughout the course, you will implement simple yet highly effective examples and use-cases which are relevant in the real-world scenario, as you build on your understanding of pandas. By the end of this course, you will have a firm understanding of the basics of pandas. You will be ready to start using pandas for different data science tasks with confidence.The second course, Unpacking NumPy and Pandas you will explore two of the most important Python packages used by Data Analysts. You will start off by learning how to set up the right environment for data analysis with Python. Here, youll learn to install the right Python distribution, as well as work with the Jupyter notebook, and set up a database. After that, you will dive into Pythons NumPy package, Pythons powerful extension with advanced mathematical functions. You will learn to create NumPy arrays, as well as employ different array methods and functions. Then, you will explore Pythons Pandas extension, where you will learn to subset your data, as well as dive into data mapping using Pandas. Youll also learn to manage your data sets by sorting and ranking them. Finally, you will learn to index and group your data for sophisticated data analysis and manipulation.The third course, Modeling and Visualization of Data in Pandas will support users as they work through a typical real-world data analysis project step-by-step using Pandas. It develops the deep knowledge and skills that will enable students to immediately tackle their own projects with Pandas at work. This product demonstrates how to make financial models using Python’s software library for data manipulation and analysis.The fourth course, Mastering Python Data Analysis with Pandas you will learn how to apply Pandas to important but simple financial tasks such as modelling portfolios, calculating optimal portfolios based upon risk, and more. This video 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 this video, you will be an expert in using the Pandas library for any data analysis problem, especially related to finance.About the Authors:Harish Garg is a Data Analyst, author, and Software Developer who is really passionate about Data Science and the Python programming language. He is a graduate from Udacity’s Data Analyst Nanodegree program. He has 17 years of industry experience, which includes data analysis using Python, developing and testing enterprise and consumer software, managing projects and software teams, and creating training material and tutorials. Harish also worked for 11 years for Intel Security (previously McAfee, Inc.). He regularly contributes articles and tutorials on data analysis and Python. He is also active in the open data community and is a contributing member of the Data4Democracy open data initiative. He has written data analysis pieces for think tan takshashila.Curtis Miller is a graduate student at the University of Utah, seeking an Masters in Statistics (MSTAT) and a Big Data Certificate. In the past, Curtis has worked as a Math Tutor, and has a double major adding mathematics with an emphasis in statistics as a second major. He has studied the gender pay gap, and presented his paper or Gender Pay Disparity in Utah, which grabbed the attention of local media outlets. He currently teaches Basic Statistics at the University of Utah. He enjoys writing and is an avid reader, and enjoys studying politics, economics, history, and psychology and sociology.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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As a society, we spend hundreds of billions of dollars measuring the return on our financial assets. Yet, at the same time, we still haven’t found convincing ways of measuring the return on our investments in developing people.
And I get it: If my bank account pays me 1% a year, I can measure it to the penny. We’ve been collectively trained to expect neat and precise ROI calculations on everything, so when it’s applied to something as seemingly squishy as how effectively people are learning in the workplace, the natural inclination is to throw up our hands and say it can’t be done. But we need to figure this out. In a world where skills beat capital, the winners and losers of the next 30 years will be determined by their ability to attract and develop great talent.
Fortunately, corporate learning & development (L&D), like most business functions, is evolving quickly. We can embrace some level of ambiguity and have rigor when measuring the ROI of learning. It just might look a little different than an M.B.A. would expect to see in an Excel model.