Felicitas Schulze-Steinen

Data Science with Python Journey at LSET

Congratulations, Felicitas Schulze-Steinen, on Completing your Data Science with Python course with LSET. Best Of Luck For Your Future In Technology Driven World !

Introduction

Meet Felicitas Schulze-Steinen, a dedicated data science professional who has utilised her education and hands-on experience at the London School of Emerging Technology (LSET) to gain expertise in data science with Python. Her journey reflects her commitment to continuous learning and excellence in the field of data analysis and technology.

Education and Training

Felicitas Schulze-Steinen completed the Data Science with Python course at LSET, where she received extensive training in advanced data science methodologies and technologies. The curriculum equipped her with both in-depth theoretical knowledge and practical expertise, preparing her to tackle complex, real-world data challenges with confidence.

Key Skills and Technologies

Throughout her studies at LSET, Felicitas Schulze-Steinen developed a strong foundation in the following key areas essential for data science with Python:

Python Programming

Proficient in Python, utilising libraries such as NumPy, Pandas, and Matplotlib for data manipulation, analysis, and visualisation.

Data Analysis and Visualisation

Skilled in analysing complex datasets and presenting insights using tools like Matplotlib and Seaborn for effective data visualisation.

Machine Learning

Experienced in applying machine learning algorithms, such as regression, classification, and clustering, using Scikit-learn to build predictive models.

Data Wrangling

Expertise in cleaning, transforming, and preparing large datasets for analysis, ensuring data integrity and accuracy.

Statistical Analysis

Solid understanding of statistical concepts and methods, using Python to perform hypothesis testing, probability calculations, and inferential statistics.

Big Data Processing

Experienced in working with large datasets, employing tools such as Apache Spark for efficient big data processing and analysis.

Data Visualisation Tools

Proficient in using tools like Tableau and Power BI for creating interactive and dynamic visualisations to communicate data-driven insights.

SQL and Databases

Experienced in SQL for querying and managing relational databases, ensuring efficient data retrieval and management.

Natural Language Processing (NLP)

Familiar with applying NLP techniques in Python using libraries like NLTK and SpaCy for text processing and sentiment analysis.

Version Control

Skilled in using Git for version control, enabling effective collaboration and project management in data science projects.

Video Assignments

Outlier Detection and Treatment in Python

Here is a comprehensive assignment on how to detect and treat outliers in Python. With the help of Python, we will explore the concept of outlier detection and learn how to identify and handle outliers.

Certifications

To further validate her expertise, Felicitas Schulze-Steinen has earned several certifications, including:

Felicitas Schulze-Steinen’s journey at the London School of Emerging Technology has equipped her with a solid foundation in data science with Python. Her commitment to learning and practical experience with industry-standard tools and methodologies have prepared her to excel in the ever-evolving field of data science. Felicitas continues to bridge the gap between data analysis and real-world application, driving data-driven decision-making and operational efficiency.

For more information or to connect with Felicitas Schulze-Steinen, visit her LinkedIn profile

Felicitas Schulze-Steinen

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