A Python-Based Datascience Roadmap
Ready to advance your Python skills? Our easy-to-follow Advanced Python course is tailored for learners of all levels, This course is crafted for students aspiring to master Python and dedicated to pursuing careers as data analysts or data scientists. It comprehensively covers advanced Python concepts, providing students with a strong foundation in programming and data analysis, focusing on data analysis, visualization, and machine learning.
What you’ll learn
- The course is designed to provide students with a strong foundation in advanced Python programming, data analysis, and machine learning..
- Students will learn advanced programming concepts, including list comprehensions, file I/O operations, exception handling, and lot more advance python concepts..
- Data manipulation and analysis using the NumPy and Pandas libraries, covering data cleaning, preprocessing, and transformation techniques..
- Data visualization using Matplotlib, Seaborn, and Plotly for creating informative and visually appealing plots and charts..
- Implementation and evaluation of various machine learning algorithms, such as supervised and unsupervised learning, using the Scikit-learn library..
- Optional exploration of advanced topics like natural language processing, web scraping, time series analysis, and recommender systems for a more comprehensive u.
Course Content
- Introduction to Python –> 2 lectures • 56min.
- Advanced Python Concepts –> 5 lectures • 1hr 40min.
- NumPy (expand on the basic library coverage) –> 5 lectures • 2hr 13min.
- Pandas (expand on the basic library coverage) –> 4 lectures • 1hr 40min.
- Data Visualization –> 4 lectures • 1hr 44min.
- Supervised Learning Algorithms –> 3 lectures • 1hr 4min.
- Unsupervised Learning Algorithms –> 1 lecture • 27min.
Requirements
Ready to advance your Python skills? Our easy-to-follow Advanced Python course is tailored for learners of all levels, This course is crafted for students aspiring to master Python and dedicated to pursuing careers as data analysts or data scientists. It comprehensively covers advanced Python concepts, providing students with a strong foundation in programming and data analysis, focusing on data analysis, visualization, and machine learning.
Discover the power of Python in handling complex data, creating engaging visuals, and building intelligent machine-learning models.
Course Curriculum: —
- Introduction to Python
- Python syntax and basic programming concepts
- Variables, data types, and operators
- Control flow (conditionals and loops)
- Functions and modules
- Advanced Python Concepts
- List comprehensions and generators
- File I/O operations
- Exception handling
- Object-oriented programming (classes, objects, inheritance)
- Decorators and metaclasses
- NumPy (expand on the basic library coverage)
- Arrays and array operations
- Array indexing and slicing
- Broadcasting and vectorization
- Mathematical functions and linear algebra
- Array manipulation and reshaping
- Pandas (expand on the basic library coverage)
- Series and DataFrame data structures
- Data cleaning and preprocessing techniques
- Data manipulation and transformation
- Handling missing data and outliers
- Merging, joining, and reshaping datasets
- Data Visualization
- Advanced Matplotlib techniques
- Seaborn for statistical data visualization
- Plotly and interactive visualizations
- Customizing plots and aesthetics
- Visualizing geospatial data
- Machine Learning with Scikit-learn (expand on the basic library coverage)
- Supervised learning algorithms (linear regression, logistic regression, support vector machines, decision trees, random forests, etc.)
- Unsupervised learning algorithms (clustering, dimensionality reduction)
- Model evaluation and validation techniques
- Hyperparameter tuning and model selection
- Feature selection and feature engineering
- Deep Learning with TensorFlow or PyTorch (optional, if time permits)
- Introduction to neural networks and deep learning
- Building and training neural networks
- Convolutional neural networks for image classification
- Recurrent neural networks for sequence data
- Transfer learning and pre-trained models
- Additional Topics (optional, based on available time and student interests)
- Natural Language Processing (NLP) with NLTK or SpaCy
- Web scraping and data collection
- Time series analysis and forecasting
- Recommender systems
- Introduction to Big Data and distributed computing with PySpark
- Case Studies and Projects
- Apply the learned concepts and libraries to real-world datasets
- Work on data science projects with varying complexities
- Practice problem-solving and critical thinking
With hands-on practice and expert guidance, you’ll be prepared for rewarding opportunities in data science and analytics.
** Join us now to become a proficient Python data analyst and unlock a world of possibilities! **