100 Days of Code: Data Scientist Challenge 2022, Improve your Python programming and data science skills and solve over 300 exercises!
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, PCAÂ analysis
- Association Rules
- LocalOutlierFactor class
- IsolationForest class
- KNeighborsClassifier class
- MultinomialNBÂ 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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