Data Science With Python course will learn how to use Python to perform data analysis and manipulate data. This may include learning how to use Python libraries such as Pandas and NumPy to work with data. Additionally, participants will learn how to use Python to build and evaluate machine learning models, and how to use data visualization techniques to communicate findings. Overall, the goal of a Data Science With Python course is to provide participants with the skills and knowledge they need to use Python effectively for data science tasks.
Training Duration: 3 Days
Minimum private group training class: 5 Participants
- Certificate Of Completion Available
- Group Private Class
- VILT Class Available
- SBL-Khas Claimable
Related training:
- Python Essentials
- Python Programming
- PTN-107: Artificial Intelligence, Data Science and Machine Learning with Python
- PTN-108: Advanced Python Scripting
For Technical Finance:
For more details, may check out the entire series and blogs at Learn Python with GemRain. Learn Data Science with Python today.
You will learn how to use programming, mathematics, and domain expertise in data science with Python to extract insights and knowledge from data. You will gain skills in data manipulation, data visualization, statistical analysis, machine learning, and deep learning, and learn how to apply these techniques to real-world problems. You will learn how to load, manipulate, and clean data using Python libraries such as NumPy and Pandas, and how to create static, animated, and interactive visualizations using libraries such as Matplotlib, Seaborn, and Plotly. You will also learn how to perform statistical analysis using NumPy and Pandas and apply machine learning techniques using the Scikit-learn library in Python.
In addition, you will learn about deep learning, a type of machine learning that uses neural networks to process large amounts of data and make predictions, and how to build and train deep learning models using the TensorFlow library in Python. Throughout the course, you will work on real-world data sets and case studies to apply the concepts and techniques you are learning, and learn best practices for working with data, such as how to handle large data sets, choose the right tools and techniques for a given problem, and communicate your findings effectively. By the end of the course, you will have a solid foundation in data science and the skills to apply these techniques to a wide range of data-driven problems.