PyDataSci
| Formats: | Asynchronous |
| Blended | |
| Online | |
| Onsite | |
| Part-time | |
| Level: | Intermediate |
Formats: We offer our training content in a flexible format to suit your needs. Contact Us if you wish to know if we can accommodate your unique requirements.
Level: We are happy to customise course content to suit your skill level and learning goals. Contact us for a customised learning path.
Python for Data Science PyDataSci
Unleash the power of machine learning and artificial intelligence with the key python libraries and projects ones needs to know to become a data science master. Conquer data visiaulisation with Pandas, Matplotlib and Seaborn. Quickly analyse and present data with Jupyter Notebooks and learn how to use the key algorithms of scikit-learn.
Module 1: Introduction to Data Science
- Overview of Data Science
- The Data Science Process
- Roles and Responsibilities of a Data Scientist
- Introduction to Python for Data Science
Module 2: Python Basics
- Python Environment Setup
- Basic Python Syntax
- Data Types and Variables
- Control Structures (if statements, loops)
- Functions and Modules
Module 3: Data Manipulation with Pandas
- Introduction to Pandas
- DataFrames and Series
- Reading and Writing Data
- Data Cleaning and Preparation
- Handling Missing Data
Module 4: Data Analysis with NumPy
- Introduction to NumPy
- Arrays and Array Operations
- Mathematical and Statistical Operations
- Working with Multidimensional Data
Module 5: Data Visualization
- Introduction to Data Visualization
- Matplotlib Basics
- Plotting with Pandas
- Advanced Visualization with Seaborn
- Interactive Plots with Plotly
Module 6: Exploratory Data Analysis (EDA)
- Understanding Data Distributions
- Descriptive Statistics
- Identifying Patterns and Relationships
- Data Transformation and Feature Engineering
Module 7: Introduction to Machine Learning
- Overview of Machine Learning
- Supervised vs. Unsupervised Learning
- Introduction to Scikit-learn
- Building and Evaluating Machine Learning Models
Module 8: Practical Data Science Projects
- End-to-End Data Science Project
- Data Collection and Cleaning
- Model Building and Evaluation
- Reporting and Presentation of Results
Module 9: Capstone Project
- Real-World Data Science Problem
- Applying Techniques Learned
- Presenting Findings and Solutions
Module 10: Advanced Topics (Optional)
- Introduction to Deep Learning with TensorFlow/Keras
- Time Series Analysis
- Natural Language Processing (NLP)
- Big Data Tools and Technologies (e.g., Spark)