In this nearly 50 hours course, we will walk through the complete Python for starting the career in data science and cloud computing!
This is so far the most comprehensive guide to mastering data science, business analytics, statistical tests & modelling, data visualization, machine learning, cloud computing, Big data analysis and real world use cases with Python.
Data science career is not just a traditional IT or pure technical game – this is a comprehensive area, and above all, you must know why you conduct data analysis and how to deploy your results to generate values for the company you are working for or your own business. Therefore, this course not only covers all aspects of practical data science, but also the necessary data engineering skills and business model & knowledge you need in different industries.
Whether you are working in financing, marketing, health companies, or you are running start-up, knowing the complete application of Python for data science and cloud computing is the must to achieving various business objective and looking insights into data. Yes, this complete course introduces you to a solid foundation based on the following contents and features
· Python programming for data analytics, including Python fundamentals, Numpy array, Pandas Data Frames and Scipy functions.
· How big data are collected and analyzed based on many real world examples. such as using Python scraping web data, communicating with flat files, parquet files, SAS data, SQLite, MongoDB and Redshift on AWS
· Statistics and its application into various types of business use cases, such as the most useful statistical techniques you’ll need for banking, risk, marketing, pricing, social medium, fraud detection, customers churn & life value analysis and more.
· Machine learning algorithms in each use case – all necessary theories and usages for real world applications. Note, this part is taught by both business analyst and PHD mathematician with more than 20 years experience, we teach you ‘why’ from the root, rather than just ‘model.fit() model.predict()’ instructed in many other courses.
· Data visualization combined with statistical analysis use cases to help students develop a working familiarity to understand data by graph. We will teach you how to apply all famous graphics tools such as matplotlib, plotly online and offline, seaborn and ggplot into many practical cases.
· Many hands-on real world projects to review and improve what you have learned in the lectures. For example, we have provided the following typical use cases along with the business backgrounds: Pricing retail products by checking elasticity; Online sales forecasting using time course data; Recommender system by transaction segmentation; Consumer credit score system; Fraud detection and performance tracking; Natural Language Processing for sentimental analysis and more.
· Spark for big data analysis, cloud computing, machine learning on AWS and Azure. We provide detailed technical explanation and real word uses cases on the real cloud environments including the specific process of system configuration.
· Features for listening by doing: the best way to become an expert is to practice while learning. This course is not an exception. Not only we’ll each programming codes and theories, but also need your involvement into reviewing you have learned.
· Hundreds to thousands exercises, projects and homework along with detailed solutions. You can hardly find any other similar course with so many hands-on opportunities to solve so many practical problems
· Our experts team will provide comprehensive online support. The course will also be on-going updated with announcement
Upon completing this course, you’ll be able to apply Python to solve various data science, machine learning, statistical analysis and business problems under different environments and interfaces. You can answer different job interview questions and integrate Python and cloud computing into complete applications.
Want to be successful? then join this course and follow each learning-practicing step! You’ll learn by doing and meet various challenges to become a real data scientist!