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100 Days of Code: Data Scientist Challenge

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Overview

Embark on a Data Scientist Journey with the 100 Days of Code Challenge - Master Data Analysis and Machine Learning!

What you'll learn:
  • solve over 300 exercises in Python
  • deal with real programming problems
  • work with documentation
  • guaranteed instructor support

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.

Syllabus

  • Tips
  • Data Scientist
  • Starter
  • Day 1 - np.all() & np.any()
  • Day 2 - np.isnan(), np.allclose() & np.equal()
  • Day 3 - np.greater(), np.zeros(), np.ones() & np.full()
  • Day 4 - np.arange() & np.eye()
  • Day 5 - np.random.rand(), np.random.randn() & np.sqrt()
  • Day 6 - np.nditer(), np.linspace() & np.random.choice()
  • Day 7 - np.diag(), np.save(), np.load(), np.savetxt() & np.loadtxt()
  • Day 8 - np.reshape(), np.tolist() & np.pad()
  • Day 9 - np.zeros(), np.append() & np.intersect1d()
  • Day 10 - np.unique(), np.argmax() & np.sort()
  • Day 11 - np.where(), np.ravel() & np.zeros_like()
  • Day 12 - np.full_like(), np.tri() & np.random.randint()
  • Day 13 - np.sort() & np.expand_dims()
  • Day 14 - np.append() & np.squeeze()
  • Day 15 - slicing
  • Day 16 - np.concatenate() & np.column_stack()
  • Day 17 - np.split(), np.count_nonzero(), np.set_printoptions()
  • Day 18 - np.delete() & np.linalg.norm()
  • Day 19 - np.divide(), np.multiply() & np.sqrt()
  • Day 20 - np.allclose(), np.dot() & np.linalg.det()
  • Day 21 - np.lingalg.ein(), np.lingalg.inv() & np.trace()
  • Day 22 - np.random.shuffle(), np.argsort(), np.round() & np.roots()
  • Day 23 - np.roots, np.polyadd() & np.sign()
  • Day 24 - dates
  • Day 25 - np.char.add(), np.char.rjust(), np.char.zfill() & np.char.split()
  • Day 26 - np.char.strip(), np.char.replace() & np.char.count()
  • Day 27 - np.char.replace() & np.char.startswith()
  • Day 28 - np.char.replace(), np.delete(), np.savetxt() & np.loadtxt()
  • Day 29 - data processing
  • Day 30 - data analysis
  • Day 31 - pd.Series()
  • Day 32 - pd.Series() & pd.DataFrame()
  • Day 33 - pd.DataFrame()
  • Day 34 - pd.DataFrame() & pd.data_range()
  • Day 35 - pd.DataFrame() & pd.data_range()
  • Day 36 - pd.DataFrame() & pd.date_range()
  • Day 37 - pd.DataFrame.to_csv() & pd.read_csv()
  • Day 38 - pd.read_csv()
  • Day 39 - pd.DataFrame.groupby() & pd.DataFrame.iloc
  • Day 40 - pd.DataFrame.set_index() & pd.DataFrame.drop()
  • Day 41 - data processing
  • Day 42 - data processing & data types
  • Day 43 - grouping & mapping
  • Day 44 - concatenating & exporting
  • Day 45 - mapping & clipping
  • Day 46 - concatenating & querying
  • Day 47 - filtering & exporting

Taught by

Paweł Krakowiak

Reviews

4.5 rating at Udemy based on 19 ratings

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