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.
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English
language
Content
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
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Google Colab + Google Drive
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Introduction to Spyder
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