The course will cover the methods and tools used to work with data, such as data visualization, statistical analysis, and machine learning. The course is designed to give students a solid understanding of the data science process and to help them develop the skills necessary to extract insights and knowledge from data. Students will also get an opportunity to work on real-world projects, which will help them to apply what they have learned in a practical setting and they will earn a Certificate and Internship opportunities when they finish the course.
Who Should Attend?
Students and working professionals who wants to learn Python for data science and Machine learning techniques
Excelledia – UK – Accredited by BSNSQA – UK AND IFCE
- Basics knowledge in Programming
- Basic knowledge of Math’s (Statistics, Differential Equations, Linear algebra) Self-learn capabilities
- Hands-on project experience
- Get Industry-Recognized Course Certificate
- Guided Real-world project
- Week 1: Introduction to data science and the datascience workflow. Fundamentals of programming in Python
- Week 2: Working with data in Python, including reading and writing files, and using libraries such as NumPy and Pandas for data cleaning and analysis.
- Week 3: Data visualization in Python, including using libraries such as Matplotlib and Seaborn to create charts and plots.
- Week 4: Advanced visualization techniques and feature engineering using Seaborn
- Week 1: Introduction to machine learning and statistical modeling.
- Week 2: Supervised Machine learning techniques – Part 1, including linear regression and classification algorithms.
- Week 3: Evaluation and performance optimization of machine learning models.
- Week 4: Supervised Machine learning techniques – Part 2 and basics of Unsupervised Machine Learning
- Week 1: Introduction to Deep learning and neural networks.
- Week 2: Natural language processing and text analysis with python NLTK.
- Week 3: Text classification and Named Entity Recognition
- Week 4: Introduction to big data and distributed computing using tools such as Apache Spark (PySpark).
- Internship and Hands-on projects with assistance