Congratulations, Felicitas Schulze-Steinen, on Completing your Data Science with Python course with LSET. Best Of Luck For Your Future In Technology Driven World !
Proficient in Python, utilising libraries such as NumPy, Pandas, and Matplotlib for data manipulation, analysis, and visualisation.
Skilled in analysing complex datasets and presenting insights using tools like Matplotlib and Seaborn for effective data visualisation.
Experienced in applying machine learning algorithms, such as regression, classification, and clustering, using Scikit-learn to build predictive models.
Expertise in cleaning, transforming, and preparing large datasets for analysis, ensuring data integrity and accuracy.
Solid understanding of statistical concepts and methods, using Python to perform hypothesis testing, probability calculations, and inferential statistics.
Experienced in working with large datasets, employing tools such as Apache Spark for efficient big data processing and analysis.
Proficient in using tools like Tableau and Power BI for creating interactive and dynamic visualisations to communicate data-driven insights.
Experienced in SQL for querying and managing relational databases, ensuring efficient data retrieval and management.
Familiar with applying NLP techniques in Python using libraries like NLTK and SpaCy for text processing and sentiment analysis.
Skilled in using Git for version control, enabling effective collaboration and project management in data science projects.
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
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