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
The "Data Science Bootcamp in Python: 250+ Exercises to Master" 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 - Unveiling Insights from Data Universe!
A data scientist is a skilled professional who leverages their expertise in mathematics, statistics, programming, and domain knowledge to extract meaningful insights and valuable knowledge from complex datasets. They utilize various analytical techniques, statistical models, and machine learning algorithms to discover patterns, trends, and correlations within the data.
The role of a data scientist involves tasks such as data collection, data cleaning, exploratory data analysis, feature engineering, and building predictive or prescriptive models. They work closely with stakeholders to understand business needs, formulate data-driven strategies, and communicate findings effectively to support decision-making processes.
Data scientists possess strong analytical and problem-solving skills, as well as a deep understanding of statistical concepts and programming languages such as Python or R. They are proficient in data manipulation, data visualization, and machine learning techniques.
In addition to technical skills, data scientists possess strong communication and storytelling abilities. They can translate complex data findings into actionable insights and effectively communicate them to both technical and non-technical audiences.
Data scientists play a crucial role in various industries, including finance, healthcare, marketing, technology, and more. They help organizations make informed decisions, optimize processes, identify new opportunities, and solve complex problems by harnessing the power of data.
Packages that you will use in the exercises:
numpy
pandas
seaborn
plotly
scikit-learn
opencv
tensorflow
Who this course is for:
- aspiring data scientists who want to learn and practice data science concepts and techniques using Python
- students or individuals with a background in statistics, mathematics, or related fields who want to apply their knowledge to real-world data analysis and gain practical experience in Python
- programmers or software developers who want to expand their skillset to include data science and machine learning using Python
- professionals working in data-related roles who want to enhance their data analysis and machine learning skills using Python for better decision-making and insights
- data analysts or business analysts who want to upgrade their skills to perform more advanced data analysis, visualization, and modeling using Python
- self-learners who are interested in data science and want to acquire practical experience by solving a variety of data-related exercises in Python
Instructor
- 4.5 Instructor Rating
- 7,853 Reviews
- 320,785 Students
- 119 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
IG: e_smartdata