100 Days of Code: Data Scientist Challenge

March 1, 2025 / FreeCourse-10 / By



Improve your Python programming and data science skills and solve over 300 exercises!

What you will learn

solve over 300 exercises in Python

deal with real programming problems

work with documentation

guaranteed instructor support

Description

Take the 100 days of code challenge! Welcome to the 100 Days of Code: Data Scientist Challenge course where you can test your Python programming and data science skills.

Topics you will find in the exercises:


  • working with numpy arrays
  • generating numpy arrays
  • generating numpy arrays with random values
  • iterating through arrays
  • dealing with missing values
  • working with matrices
  • reading/writing files
  • joining arrays
  • reshaping arrays
  • computing basic array statistics
  • sorting arrays
  • filtering arrays
  • image as an array
  • linear algebra
  • matrix multiplication
  • determinant of the matrix
  • eigenvalues and eignevectors
  • inverse matrix
  • shuffling arrays
  • working with polynomials
  • working with dates
  • working with strings in array
  • solving systems of equations
  • working with Series
  • working with DatetimeIndex
  • working with DataFrames
  • reading/writing files
  • working with different data types in DataFrames
  • working with indexes
  • working with missing values
  • filtering data
  • sorting data
  • grouping data
  • mapping columns
  • computing correlation
  • concatenating DataFrames
  • calculating cumulative statistics
  • working with duplicate values
  • preparing data to machine learning models
  • dummy encoding
  • working with csv and json filles
  • merging DataFrames
  • pivot tables
  • preparing data to machine learning models
  • working with missing values, SimpleImputer class
  • classification, regression, clustering
  • discretization
  • feature extraction
  • PolynomialFeatures class
  • LabelEncoder class
  • OneHotEncoder class
  • StandardScaler class
  • dummy encoding
  • splitting data into train and test set
  • LogisticRegression class
  • confusion matrix
  • classification report
  • LinearRegression class
  • MAE – Mean Absolute Error
  • MSE – Mean Squared Error
  • sigmoid() function
  • entorpy
  • accuracy score
  • DecisionTreeClassifier class
  • GridSearchCV class
  • RandomForestClassifier class
  • CountVectorizer class
  • TfidfVectorizer class
  • KMeans class
  • AgglomerativeClustering class
  • HierarchicalClustering class
  • DBSCAN class
  • dimensionality reduction, PCA analysis
  • Association Rules
  • LocalOutlierFactor class
  • IsolationForest class
  • KNeighborsClassifier class
  • MultinomialNB class
  • GradientBoostingRegressor class

This course is designed for people who have basic knowledge in Python and data science. It consists of 300 exercises with solutions. This is a great test for people who want to become a data scientist and are looking for new challenges. Exercises are also a good test before the interview.

‘;
}});



If you’re wondering if it’s worth taking a step towards data science, don’t hesitate any longer and take the challenge today.

Stack Overflow Developer Survey

According to the Stack Overflow Developer Survey 2021, Python is the most wanted programming language. Python passed SQL to become our third most popular technology. Python is the language developers want to work with most if they aren’t already doing so.

Tips

A few words from the author
Configuration

Starter

Exercise 0
Solution 0

Day 1 – np.all() & np.any()

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3
Exercise 4
Solution 4

Day 2 – np.isnan(), np.allclose() & np.equal()

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 3 – np.greater(), np.zeros(), np.ones() & np.full()

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 4 – np.arange() & np.eye()

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 5 – np.random.rand(), np.random.randn() & np.sqrt()

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 6 – np.nditer(), np.linspace() & np.random.choice()

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 7 – np.diag(), np.save(), np.load(), np.savetxt() & np.loadtxt()

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 8 – np.reshape(), np.tolist() & np.pad()

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 9 – np.zeros(), np.append() & np.intersect1d()

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 10 – np.unique(), np.argmax() & np.sort()

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3
Exercise 4
Solution 4

Day 11 – np.where(), np.ravel() & np.zeros_like()

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 12 – np.full_like(), np.tri() & np.random.randint()

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3
Exercise 4
Solution 4

Day 13 – np.sort() & np.expand_dims()

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3
Exercise 4
Solution 4

Day 14 – np.append() & np.squeeze()

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 15 – slicing

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3
Exercise 4
Solution 4

Day 16 – np.concatenate() & np.column_stack()

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 17 – np.split(), np.count_nonzero(), np.set_printoptions()

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3
Exercise 4
Solution 4

Day 18 – np.delete() & np.linalg.norm()

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 19 – np.divide(), np.multiply() & np.sqrt()

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 20 – np.allclose(), np.dot() & np.linalg.det()

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 21 – np.lingalg.ein(), np.lingalg.inv() & np.trace()

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3
Exercise 4
Solution 4

Day 22 – np.random.shuffle(), np.argsort(), np.round() & np.roots()

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3
Exercise 4
Solution 4

Day 23 – np.roots, np.polyadd() & np.sign()

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 24 – dates

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 25 – np.char.add(), np.char.rjust(), np.char.zfill() & np.char.split()

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 26 – np.char.strip(), np.char.replace() & np.char.count()

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 27 – np.char.replace() & np.char.startswith()

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 28 – np.char.replace(), np.delete(), np.savetxt() & np.loadtxt()

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 29 – data processing

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 30 – data analysis

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3
Exercise 4
Solution 4

Day 31 – pd.Series()

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 32 – pd.Series() & pd.DataFrame()

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 33 – pd.DataFrame()

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 34 – pd.DataFrame() & pd.data_range()

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 35 – pd.DataFrame() & pd.data_range()

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 36 – pd.DataFrame() & pd.date_range()

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 37 – pd.DataFrame.to_csv() & pd.read_csv()

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 38 – pd.read_csv()

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 39 – pd.DataFrame.groupby() & pd.DataFrame.iloc

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 40 – pd.DataFrame.set_index() & pd.DataFrame.drop()

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 41 – data processing

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 42 – data processing & data types

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 43 – grouping & mapping

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 44 – concatenating & exporting

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 45 – mapping & clipping

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 46 – concatenating & querying

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 47 – filtering & exporting

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 48 – filtering & missing values

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 49 – missing values

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 50 – missing values & random

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 51 – data preprocessing

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 52 – data preprocessing

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 53 – data preprocessing

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 54 – grouping & mapping

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 55 – data exploring

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 56 – data preprocessing

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 57 – grouping & querying

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 58 – querying

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 59 – duplicated data, data types

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 60 – data types

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 61 – categorical data

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 62 – categorical data & dummies

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 63 – data analysis

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 64 – data preprocessing

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 65 – JSON files

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 66 – JSON files

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 67 – CSV files

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 68 – data processing

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 69 – data preprocessing

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 70 – merging

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 71 – merging

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 72 – merging

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 73 – pivot tables

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3
Exercise 4
Solution 4

Day 74 – imputing missing values

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 75 – imputing missing values

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 76 – continuous to categorical variable

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3
Exercise 4
Solution 4

Day 77 – data preprocessing

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 78 – data preprocessing

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 79 – data exploring

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 80 – train-test split, logistic regression & prediction

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3
Exercise 4
Solution 4

Day 81 – LabelEncoder & OneHotEncoder

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 82 – data preprocessing

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 83 – data preprocessing

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 84 – linear regression & polynomial features

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3
Exercise 4
Solution 4

Day 85 – metrics

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 86 – StandardScaler & entropy

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 87 – accuracy, confusion matrix & decision tree

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 88 – decision tree & grid search

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 89 – random forest, grid search & CountVectorizer

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 90 – CountVectorizer & TfidfVectorizer

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3
Exercise 4
Solution 4

Day 91 – KMeans, AgglomerativeClustering & DBSCAN

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3
Exercise 4
Solution 4
Exercise 5
Solution 5

Day 92 – PCA

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3
Exercise 4
Solution 4

Day 93 – LocalOutlierFactor & IsolationForest

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3
Exercise 4
Solution 4

Day 94 – KNeighborsClassifier & Logisticregression

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3
Exercise 4
Solution 4

Day 95 – association rules

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 96 – CountVectorizer

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 97 – classification & MultinomialNB

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 98 – data preprocessing

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Day 99 – LinearRegression & R^2 score

Exercise 1
Solution 1
Exercise 2
Solution 2

Day 100 – LinearRegression & GradientBoostingRegressor

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3

Configuration (optional)

Info
Google Colab + Google Drive
Google Colab + GitHub
Google Colab – Intro
Anaconda installation – Windows 10
Introduction to Spyder
Anaconda installation – Linux
Spyder

Enroll for Free

💠 Follow this Video to Get Free Courses on Every Udemy Topics! 💠

Ads Blocker Image Powered by Code Help Pro

Ads Blocker Detected!!!

We have detected that you are using extensions to block ads. Please support us by disabling these ads blocker.

Powered By
100% Free SEO Tools - Tool Kits PRO

Check Today's 30+ Free Courses on Telegram!

Join Channel
X