Data Science Bootcamp in Python: 250+ Exercises to Master
What you'll learn
- solve over 250 exercises in data science in Python
- deal with real programming problems
- deal with real problems in data science
- work with libraries numpy, pandas, seaborn, plotly, scikit-learn, opencv, tensorflow
- work with documentation
- guaranteed instructor support
Requirements
- Completion of all courses in the Python Developer learning path
- Completion of all courses in the Data Scientist learning path
Description
This is a highly comprehensive course designed to catapult learners into the exciting field of data science using Python. This bootcamp-style course allows participants to gain hands-on experience through extensive problem-solving exercises covering a wide range of data science topics.
The course is structured into multiple sections that cover core areas of data science. These include data manipulation and analysis using Python libraries like Pandas and NumPy, data visualization with matplotlib and seaborn, and machine learning techniques using scikit-learn.
Each exercise within the course is designed to reinforce a particular data science concept or skill, challenging participants to apply what they've learned in a practical context. Detailed solutions for each problem are provided, allowing learners to compare their approach and gain insights into best practices and efficient methods.
The "Data Science Bootcamp in Python: 250+ Exercises to Master" course is ideally suited for anyone interested in data science, whether you're a beginner aiming to break into the field, or an experienced professional looking to refresh and broaden your skillset. This course emphasizes practical skills and applications, making it a valuable resource for aspiring data scientists and professionals looking to apply Python in their data science endeavours.
Data Scientist: Turning Data into Actionable Insights
A Data Scientist analyzes large volumes of structured and unstructured data to uncover patterns, trends, and valuable insights that drive strategic decision-making. By combining expertise in statistics, programming, and domain knowledge, data scientists build predictive models, design experiments, and communicate results through visualizations and reports. Their work bridges the gap between raw data and real-world impact across various industries.
The following packages will be utilized throughout the exercises:
numpy
pandas
seaborn
plotly
scikit-learn
opencv
tensorflow
Who this course is for:
- Aspiring Data Scientists
- Python Developers Transitioning into Data Science
- Data Analysts and Business Analysts
- Students and Recent Graduates
- Machine Learning and AI Enthusiasts
- Professionals Preparing for Data Science Interviews
- Freelancers and Independent Consultants
Instructor
- 4.3 Instructor Rating
- 11,143 Reviews
- 431,232 Students
- 164 Courses
EN
Python Developer/AI Enthusiast/Data Scientist/Stockbroker
Enthusiast of new technologies, particularly in the areas of artificial intelligence, the Python language, big data and cloud solutions. Graduate of postgraduate studies at the Polish-Japanese Academy of Information Technology in the field of Computer Science and Big Data specialization. Master's degree graduate in Financial and Actuarial Mathematics at the Faculty of Mathematics and Computer Science at the University of Lodz. Former PhD student at the faculty of mathematics. Since 2015, a licensed Securities Broker with the right to provide investment advisory services (license number 3073). Lecturer at the GPW Foundation, conducting training for investors in the field of technical analysis, behavioral finance, and principles of managing a portfolio of financial instruments.
Founder at e-smartdata
PL
Data Scientist, Securities Broker
Jestem miłośnikiem nowych technologii, szczególnie w obszarze sztucznej inteligencji, języka Python big data oraz rozwiązań chmurowych. Posiadam stopień absolwenta podyplomowych studiów na kierunku Informatyka, specjalizacja Big Data w Polsko-Japońskiej Akademii Technik Komputerowych oraz magistra z Matematyki Finansowej i Aktuarialnej na wydziale Matematyki i Informatyki Uniwersytetu Łódzkiego. Od 2015 roku posiadam licencję Maklera Papierów Wartościowych z uprawnieniami do czynności doradztwa inwestycyjnego (nr 3073). Jestem również wykładowcą w Fundacji GPW prowadzącym szkolenia dla inwestorów z zakresu analizy technicznej, finansów behawioralnych i zasad zarządzania portfelem instrumentów finansowych. Mam doświadczenie w prowadzeniu zajęć dydaktycznych na wyższej uczelni z przedmiotów związanych z rachunkiem prawdopodobieństwa i statystyką. Moje główne obszary zainteresowań to język Python, sztuczna inteligencja, web development oraz rynki finansowe.
Założyciel platformy e-smartdata