100 Days of Code: Data Scientist Challenge 2022

100 Days of Code: Data Scientist Challenge 2022

100 Days of Code: Data Scientist Challenge 2022

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

Language: english

Note: 4.9/5 (8 notes) 8,228 students

Instructor(s): Paweł Krakowiak

Last update: 2022-08-31

What you’ll learn

  • solve over 300 exercises in Python
  • deal with real programming problems
  • work with documentation
  • guaranteed instructor support

 

Requirements

  • basic knowledge of Python
  • basic knowledge of data science
  • I have courses which can assist in obtaining all the necessary skills for this course

 

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.

 

Who this course is for

  • everyone who wants to learn by doing
  • everyone who wants to improve their Python programming skills
  • everyone who wants to improve their data science skills
  • everyone who wants to prepare for an interview

 

Course content

  • Tips
    • A few words from the author
    • Configuration
    • Requirements
  • 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
  • Bonus
    • Bonus

 

100 Days of Code: Data Scientist Challenge 2022

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