Data Science With Python
Instructor-led training in Data Science With Python. Delivered live online by senior practitioners, with hands-on labs and a final assessment. Includes an Skilvi course-completion certificate.
Overview
This intermediate course in Data Science with Python dives into the essential techniques and tools for analyzing data and building predictive models. By leveraging Python's powerful libraries, you will gain the skills necessary to transform raw data into actionable insights, a critical capability in today's data-driven world.
What you'll learn
- You will be able to clean and preprocess data using Python libraries such as Pandas and NumPy.
- You will be able to visualize data effectively with Matplotlib and Seaborn.
- You will be able to implement statistical analysis and hypothesis testing.
- You will be able to build and evaluate machine learning models using Scikit-learn.
- You will be able to perform exploratory data analysis to uncover trends and patterns.
- You will be able to work with real-world datasets to solve practical data science problems.
- You will be able to apply best practices in model selection and validation.
- You will be able to communicate your findings through effective storytelling with data.
Curriculum
8 modules · outline is indicative and can be tailored to your team.
1Introduction to Data Science
- Overview of Data Science and its importance
- Key concepts and terminology
- Python environment setup
2Data Manipulation with Pandas
- DataFrames and Series
- Data cleaning techniques
- Filtering and transforming data
3Data Visualization
- Creating plots with Matplotlib
- Advanced visualizations with Seaborn
- Best practices for data storytelling
4Statistical Analysis
- Descriptive statistics
- Inferential statistics and hypothesis testing
- Correlation and regression analysis
5Machine Learning Fundamentals
- Understanding supervised vs unsupervised learning
- Introduction to Scikit-learn
- Building and evaluating models
6Exploratory Data Analysis
- Techniques for data exploration
- Identifying trends and patterns
- Feature engineering
7Model Validation and Selection
- Cross-validation techniques
- Hyperparameter tuning
- Model performance metrics
8Real-World Applications
- Case studies in various industries
- Working with large datasets
- Ethics in data science
Prerequisites
Basic knowledge of Python programming is recommended.
Who should attend
This course is ideal for data analysts, aspiring data scientists, and professionals looking to enhance their data skills.
Certification
Frequently asked questions
What is the delivery format of the course?
The course is delivered live online with interactive sessions.
How long is the course?
The course spans 8 weeks, with one session each week.
Will I receive a certificate upon completion?
Yes, you will receive an Skilvi course-completion certificate.
Are exam vouchers included?
Exam vouchers are available through authorized channels on request.
What are the prerequisites for this course?
Basic knowledge of Python programming is recommended.
Pricing on request
- Live online (VILT)
- 24–32 hours
- Hands-on labs & assignments
- Skilvi completion certificate
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