Course Information
Course Overview
Embark on a Data Scientist Journey with the 100 Days of Code Challenge - Master Data Analysis and Machine Learning!
This course is an intensive, practical-oriented program that aims to transform learners into proficient data scientists within 100 days. This course follows the recognized #100DaysOfCode challenge, inviting participants to engage in data science coding tasks for a minimum of an hour daily for 100 consecutive days. This course allows students to take a hands-on approach in learning data science, featuring a multitude of practical exercises spanning 100 days.
Each day of the challenge presents a fresh set of tasks, each tailored to explore various facets of data science including data extraction, preprocessing, modeling, analysis, and visualization. These exercises are set within the context of real-world scenarios, and range from simple tasks to more complex problems, covering topics such as data cleaning, exploratory data analysis, machine learning, deep learning, and more.
This course covers a wide range of Python libraries like Pandas, NumPy, Matplotlib, Seaborn, and Scikit-Learn, and it does not shy away from introducing the students to more advanced concepts such as Natural Language Processing (NLP), Time-Series Analysis, and Neural Networks.
With over 100 hands-on exercises, the students will be able to solidify their understanding of data science theory, develop practical coding skills and problem-solving abilities that will be crucial in a real job setting.
This course encourages a "learn by doing" approach, where students will be coding and solving problems each day, thus reinforcing the concepts learned. By the end of the 100 days, students will have built a robust portfolio showcasing their ability to tackle a variety of data science problems, proving to potential employers their readiness for the data science industry.
100 Days of Code: Your Data Science Journey in Python
Embark on a transformative 100-day coding challenge designed to build and sharpen your data science skills using Python. From foundational programming and data manipulation to machine learning and real-world projects, each day offers hands-on exercises, practical applications, and guided learning. Whether you're a beginner or looking to upskill, this journey will equip you with the tools and confidence to thrive as a data scientist.
Course Content
- 10 section(s)
- 332 lecture(s)
- Section 1 Tips
- Section 2 Data Scientist
- Section 3 Starter
- Section 4 Day 1 - np.all() & np.any()
- Section 5 Day 2 - np.isnan(), np.allclose() & np.equal()
- Section 6 Day 3 - np.greater(), np.zeros(), np.ones() & np.full()
- Section 7 Day 4 - np.arange() & np.eye()
- Section 8 Day 5 - np.random.rand(), np.random.randn() & np.sqrt()
- Section 9 Day 6 - np.nditer(), np.linspace() & np.random.choice()
- Section 10 Day 7 - np.diag(), np.save(), np.load(), np.savetxt() & np.loadtxt()
What You’ll Learn
- solve over 300 exercises in Python
- deal with real programming problems
- work with documentation
- guaranteed instructor support
Skills covered in this course
Reviews
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JJonathan Scholz
Many of the exercises use features of NumPy and Pandas that have already been removed. The statement "Last updated May 2024" is misleading, as it does not necessarily mean that the code is up to date. As a new learner, I do not want to invest time in studying deprecated concepts.
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BBartłomiej Mandziuk
Ten kurs zawiera ogólny przegląd funkcjonalności około Data Science. Na plus przegląd wielu różnych funkcji, czasem przydatnych by mieć z tyłu głowy czego można szukać gdy będą potrzebne. Na minus powierzchowność ćwiczeń (wczytanie biblioteki to nieraz całe ćwiczenie), konsola (nie pokazuje wyniku, tylko info czy zaliczone więc trzeba pisać w innym miejscu), powtarzalność (w niektórych ćwiczeniach trzeba było zrobić dokładnie to samo), błędy (niektóre ćwiczenia mi się wczytały z zakomentowanym rozwiązaniem w konsoli).
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SSatabdi Nandi
it was great
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MMichael Gora
Lots of practice and tricky exercises, I recommend!