What you'll learn:
- Learn about the most important libraries for doing Data Science with Python and how they can be easily installed with the Anaconda distribution.
- Understand the basics of Numpy which is the foundation of all the other analytical tools in Python.
- Produce informative, useful and beautiful visualizations for analyzing data.
- Analyze, answer questions and derive conclusions from real world data sets using the Pandas library.
- Perform common statistical calculations and use the results to reach conclusions about the data.
- Learn how to build predictive models and understand the principles of Predictive Analytics
The Python programming language has become a major player in the world of Data Science and Analytics. This course introduces Python’s most important tools and libraries for doing Data Science; they are known in the community as “Python’s Data Science Stack”.
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.
About the author :
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 of 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 in-site) courses to students from around the world in topics like Data Science, Mathematics, Statistics, R programming and Python.
Alvaro Fuentes is a big Python fan and has been working with Python for about 4 years and uses it routinely for analyzing data and producing 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 things. 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, mathematical modeling.
Predictive Analytics is a topic in which he has both professional and teaching experience. Having solved practical problems in his consulting practice using the 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.
