Get yourself trained on Data Analysis with with this Online Training Data Analysis with Python – A Complete Guide!: 4-in-1.
Online Training Data Analysis with Python – A Complete Guide!: 4-in-1
Microservices are a popular new approach to building maintainable, scalable, cloud-based applications. AWS is the perfect platform for hosting Microservices. Recently, there has been a growing interest in Serverless computing due to the increase in developer productivity, built in auto-scaling abilities, and reduced operational costs. Building a microservices platform using virtual machines or containers, involves a lot of initial and ongoing effort. There is a cost associated with having idle services running, maintenance of the boxes and a configuration complexity involved in scaling up and down. In combining both microservices and serverless computing, organizations will benefit from having the servers and capacity planning managed by the cloud provider, making them much easier to deploy and run at scale.This comprehensive 4-in-1 course is a step-by-step tutorial which is a perfect course to understanding the different disciplines of data analysis using hands-on examples where you actually solve real-world problems in Python. Learn the foundations for doing Data Science and Predictive Analytics with Python through real-world examples. Enhance your knowledge as a data analyst by diving into the necessary tools for data acquisition and manipulation. Real-world, practical examples that help you wrap your head around the essential know-how for data management. Contents and OverviewThis training program includes 4 complete courses, carefully chosen to give you the most comprehensive training possible.The first course, Learning Python Data Analysis, covers an in-depth understanding of data analysis with various Python packages. In this course you will learn all the necessary libraries that make data analytics with Python a joy. Get into hands-on data analysis and machine learning by coding in Python. Learn the Numpy library used for numerical and scientific computation. Employ useful libraries for visualization, Matplotlib and Seaborn, to provide insights into data. Further you will learn various steps involved in building an end-to-end machine learning solution. The video course is prepared with applications in mind. You will explore coding on real-life datasets, and implement your knowledge on projects. This video course will prepare you to the world of data science. Welcome to our journey!The second course, Data Analysis with Python, covers practical steps to become a Data Science practitioner using Python. This course introduces the audience to the field of Data Science using Python tools to manage and analyze data. Learn fundamental tools of the trade and apply them to real data problems. Explore the use of Python stack for data analysis and scientific computing, and expands on concepts of data acquisition, data cleaning, data analysis and machine learning.The third course, Become a Python Data Analyst, covers taking your data analytics and predictive modeling skills to the next level using the popular tools and libraries in Python. This is a practical course where the viewer will learn through real-world examples how to use the most popular tools for doing Data Science and Analytics with Python.The third course, Data Acquisition and Manipulation with Python, covers data in different formats with the help of Python data analysis tools. In this course, youll start by learning how to acquire data from the web in its already clean format, such as in a .csv file, or a database. Learn to transform this data so its in its most useful format for analysis. After that, dive into data aggregation and grouping, where youll learn to group similar data for easier analysis purposes. From there, youll be shown different methods of web scraping using Python. Finally, youll learn to extract large amounts of data using BeautifulSoup, as well as work with Selenium and Scrapy.By the end of the course, youll gain an in-depth understanding of data analysis with various Python packages to start your journey to become a Data Science practitioner using Python!About the AuthorsIlyas Ustun is a data scientist. He is passionate about creating data-driven analytical solutions that are of outstanding merit. Visualization is his favorite. After all, a picture is worth a thousand words. He has over 5 years of data analytics experience in various fields like transportation, vehicle re-identification, smartphone sensors, motion detection, and digital agriculture. His Ph.D. dissertation focused on developing robust machine learning models in detecting vehicle motion from smartphone accelerometer data (without using GPS). In his spare time, he loves to swim and enjoy the nature. He loves gardening and his dream is to have a house with a small garden so he can fill it in with all kind of flowers. Marco Bonzanini is a data scientist based in London, United Kingdom. He holds a Ph.D. in information retrieval from the Queen Mary University of London. He specializes in text analytics and search applications, and over the years, he has enjoyed working on a variety of information management and data science problems. He maintains a personal blog, where he discusses different technical topics, mainly around Python, text analytics, and data science. When not working on Python projects, he likes to engage with the community at PyData conferences and meetups, and he also enjoys brewing homemade beer.Alvaro Fuentes is a Data Scientist with an M.S. in Quantitative Economics and a M.S. in Applied Mathematics with more than 10 years’ experience in analytical roles. He worked in the Central Bank of Guatemala as an Economic Analyst, building models for economic and financial data. He founded Quant Company to provide consulting and training services in Data Science topics and has been a consultant for many projects in fields such as: Business, Education, Psychology and Mass Media. He also has taught many (online and on-site) courses to students from around the World in topics such as Data Science, Mathematics, Statistics, R programming, and Python. Alvaro Fuentes is a big Python fan; he has been working with Python for about 4 years and uses it routinely to analyze data and make predictions. He also has used it in a couple of software projects. He is also a big R fan, and doesn’t like the controversy between what is the best R or Python; he uses them both. He is also very interested in the Spark approach to big data, and likes the way it simplifies complicated topics. He is not a software engineer or a developer but is generally interested in web technologies. He also has technical skills in R programming, Spark, SQL (PostgreSQL), MS Excel, machine learning, statistical analysis, econometrics, and mathematical modeling. Predictive Analytics is a topic in which he has both professional and teaching experience. He has solved practical problems in his consulting practice using Python tools for predictive analytics and the topics of predictive analytics are part of a more general course on Data Science with Python that he teaches online.Curtis Miller is a graduate student at the University of Utah, seeking a 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. Curtis 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.
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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.