Entry-level analysts who want to enter in the data science world will find this course very useful to get themselves acquainted with Python's data science capabilities for doing real-world data analysis.

What You Will Learn:
- Install and setup Python
- Implement objects in Python by creating classes and defining methods
- Get acquainted with NumPy to use it with arrays and array-oriented computing in data analysis
- Create effective visualizations for presenting your data using Matplotlib
- Process and analyze data using the time series capabilities of pandas
- Interact with different kind of database systems, such as file, disk format, Mongo, and Redis
- Apply data mining concepts to real-world problems
- Compute on big data, including real-time data from the Internet
- Explore how to use different machine learning models to ask different questions of your data

The Python: Real-World Data Science course will take you on a journey to become an efficient data science practitioner by thoroughly understanding the key concepts of Python. This learning path is divided into four modules and each module are a mini course in their own right, and as you complete each one, you'll have gained key skills and be ready for the material in the next module.

The course begins with getting your Python fundamentals nailed down. After getting familiar with Python core concepts, it's time that you dive into the field of data science. In the second module, you'll learn how to perform data analysis using Python in a practical and example-driven way. The third module will teach you how to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis to more complex data types including text, images, and graphs. Machine learning and predictive analytics have become the most important approaches to uncover data gold mines. In the final module, we'll discuss the necessary details regarding machine learning concepts, offering intuitive yet informative explanations on how machine learning algorithms work, how to use them, and most importantly, how to avoid the common pitfalls.