Your complete guide to data science. Unleash the true potential of Python by learning basic programming and high-end data science techniques.
Python has become the language of choice for most data analysts/data scientists to perform various tasks of data science. If you’re looking forward to implementing Python in your data science projects to enhance data discovery, then this is the perfect Learning Path is for you. Starting out at the basic level, this Learning Path will take you through all the stages of data science in a step-by-step manner.
Packt’s Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it.
We begin this journey with nailing down the fundamentals of Python. You’ll be introduced to basic and advanced programming concepts of Python before moving on to data science topics. Then, you’ll learn how to perform data analysis by taking advantage of the core data science libraries in the Python ecosystem. You’ll also understand the data visualization concepts better, learn how to apply them and overcome any challenges that you might face while implementing them. Moving ahead, you’ll learn to use a wide variety of machine learning algorithms to solve real-world problems. Finally, you’ll learn deep learning along with a brief introduction to TensorFlow.
By the end of the Learning Path, you’ll be able to improve the efficiency of your data science projects using Python.
Meet Your Experts:
We have combined the best works of the following esteemed authors to ensure that your learning journey is smooth:
Daniel Arbuckle got his Ph.D. in Computer Science from the University of Southern California.
Benjamin Hoff spent 3 years working as a software engineer and team leader doing graphics processing, desktop application development, and scientific facility simulation using a mixture of C++ and Python.
Dimitry Foures is a data scientist with a background in applied mathematics and theoretical physics.
Giuseppe Vettigli is a data scientist who has worked in the research industry and academia for many years.
Igor Milovanović is an experienced developer, with strong background in Linux system knowledge and software engineering education.
Prateek Joshi is an artificial intelligence researcher, published author of five books, and TEDx speaker.
Eder Santana is a PhD candidate on Electrical and Computer Engineering. His thesis topic is on Deep and Recurrent neural networks.
- Basic knowledge of any programming language (preferably Python)
- Some knowledge of linear algebra and statistics would be helpful, but is not mandatory
- Familiarize yourself with Python
- Learn data analysis using modern processing techniques with NumPy, SciPy, and Pandas
- Determine different approaches to data visualization, and how to choose the most appropriate one for your needs
- Make 3D visualizations mainly using mplot3d
- Work with image data and build systems for image recognition and biometric face recognition
- Grasp how to use deep neural networks to build an optical character recognition system
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