Data Science with Python Course - Classroom
2.995,00 EUR
- 35 hours
Master the art of turning data into powerful business insights with the Data Science with Python Certification Course. This immersive, hands-on program is designed to take you from foundational Python skills to advanced data science techniques—equipping you to analyze large datasets, build predictive models, and communicate insights that drive real-world decisions.
Key Features
Language
Course material in English
Level
Beginner - advanced structured curriculum
35+ hours of instructor-led training
70+ hours recommended study time
60 hours of assignments and assessments
36 hours of hands-on practice sessions
6 real-world projects for applied learning
Code reviews and feedback from industry experts
Ask for date confirmation!
Program completion certification included
Learning Outcomes
At the end of this program, you will be able to:
Python Fundamentals
Work with Anaconda and understand core Python concepts including basic data types, strings, regular expressions, data structures, loops, and control flow.
Functions and OOP in Python
Create user-defined functions, use lambda expressions, and apply object-oriented programming concepts such as classes and objects.
Data Handling and Manipulation
Import and export datasets, and perform data analysis using the Pandas library.
Probability and Statistics
Explore key statistical concepts including data distribution, conditional probability, and hypothesis testing.
Advanced Statistical Techniques
Learn methods such as ANOVA, linear regression, model development, and dimensionality reduction.
Predictive Modeling
Understand how to evaluate models, measure performance, and solve classification problems.
Time Series Forecasting
Work with time series data, its components, and commonly used forecasting techniques.
Course timeline
Introduction to Data Science
Lesson 1
- What is Data Science
- Data analytics landscape
- Data science lifecycle
- Tools and technologies
Mastering Python
Lesson 2
- Python setup (Anaconda)
- Data types, strings, loops, control statements
- Regular expressions and data structures
- User-defined functions and lambda functions
- Object-oriented programming basics
- Importing datasets
- Data manipulation with Pandas
- Data visualization with Matplotlib, Seaborn, ggplot
Probability and Statistics
Lesson 3
- Data distribution and statistical concepts
- Conditional probability
- Hypothesis testing
Advanced Statistics
Lesson 4
- Analysis of Variance (ANOVA)
- Linear regression
- Model building techniques
- Dimensionality reduction
Predictive Modelling
Lesson 5
- Model evaluation metrics
- Classification techniques
- Performance optimization
Time Series Forecasting
Lesson 6
- Time series data and components
- Forecasting techniques
- Exponential smoothing
Capstone & Real-World Projects
Lesson 7
- Build ML models for real business problems
- Deploy solutions into production environments
- Portfolio-ready projects
Who Should Enroll in this Program?
Prerequisites
No prior requirements are needed. Basic programming knowledge is helpful. Familiarity with mathematics and statistics is beneficial but not required
Intermediate Python Developers
Software Developers
Data Analysts & Data Scientists
Automation & Scripting Professionals
Students & Tech Enthusiasts
Professionals Transitioning Roles
Statements
Licensing and accreditation
The course is offered according to Partner Program Agreement and complies with the License Agreement requirements.
Equity Policy
Candidates are encouraged to reach out to AVC for guidance and support throughout the accommodation process.
Frequently Asked Question
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