Data Science with Python Training in San Jose

The prerequisites for Edureka's Python for Data Science Course include a fundamental understanding of Computer Programming Languages.

Python is one of the premier, flexible, and powerful open-source languages that is easy to learn, easy to use, and has powerful libraries for data manipulation and analysis. For over a decade, It has been used in scientific computing and highly quantitative domains such as finance, oil and gas, physics, and signal processing. It's continued to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations. Python cuts development time in half with its simple-to-read syntax and easy compilation feature. The debugging program is a breeze in Python, and it has a built-in debugger. It has evolved as the most preferred Language for Data Analytics. The increasing search trends also indicate that it is the Next Big Thing and a must for Professionals in the Data Analytics domain.

After completing this Data Science with Python Certification course, you will be able to:

Edureka offers the best online course for Python data science and ML. Enroll now in our Data Science with Python training and get a chance to learn from industry leaders.

The duration of the Data Science with Python Course is approximately 5 to 10 weeks. Depending on their experience with web development, data science, and other related fields, it may vary from person to person.

The Data Science with Python course fee is INR 21995, but you can get it at a discounted price of INR 19795. You can get 10% OFF and save 2200. No Cost EMI Starts at 6,599 / month.

Here is the list of topics covered in the Data Science with Python Course Syllabus:

• Introduction to Data Science and ML using Python
• Data Handling, Sequences and File Operations
• Deep Dive – Functions, OOPs, Modules, Errors, and Exceptions
• Introduction to NumPy, Pandas, and Matplotlib
• Data Manipulation
• Introduction to Machine Learning with Python
• Supervised Learning - I
• Dimensionality Reduction
• Supervised Learning - II
• Unsupervised Learning
• Association Rules Mining and Recommendation Systems
• Reinforcement Learning (Self-Paced)
• Time Series Analysis (Self-Paced)
• Model Selection and Boosting
• Statistical Foundations (Self-Paced)
• Database Integration with Python (Self-Paced)
• Data Connection and Visualization in Tableau (Self-Paced)
• Advanced Visualizations (Self-Paced)
• In-Class Project (Self-Paced)