100 Days of Code: Data Scientist Challenge 2023

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

Table of Contents

This course includes:

28 mins on-demand video
324 articles
319 coding exercises
Full lifetime access
Access on mobile and TV
Certificate of completion

4.8

(9 ratings)

Created by Paweł Krakowiak

What you’ll learn

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

Course content

104 sections • 330 lectures • 1h 25m total lengthCollapse all sections

Tips3 lectures • 1min

A few words from the author00:22
Configuration00:13
Requirements00:03

Starter1 lecture • 1min

Exercise 01 question
Solution 000:01

Day 1 – np.all() & np.any()4 lectures • 1min

Exercise 11 question
Solution 100:12
Exercise 21 question
Solution 200:11
Exercise 31 question
Solution 300:11
Exercise 41 question
Solution 400:11

Day 2 – np.isnan(), np.allclose() & np.equal()3 lectures • 1min

Exercise 11 question
Solution 100:04
Exercise 21 question
Solution 200:05
Exercise 31 question
Solution 300:11

Day 3 – np.greater(), np.zeros(), np.ones() & np.full()3 lectures • 1min

Exercise 11 question
Solution 100:13
Exercise 21 question
Solution 200:02
Exercise 31 question
Solution 300:04

Day 4 – np.arange() & np.eye()3 lectures • 1min

Exercise 11 question
Solution 100:01
Exercise 21 question
Solution 200:07
Exercise 31 question
Solution 300:01

Day 5 – np.random.rand(), np.random.randn() & np.sqrt()3 lectures • 1min

Exercise 11 question
Solution 100:02
Exercise 21 question
Solution 200:02
Exercise 31 question
Solution 300:04

Day 6 – np.nditer(), np.linspace() & np.random.choice()3 lectures • 1min

Exercise 11 question
Solution 100:04
Exercise 21 question
Solution 200:02
Exercise 31 question
Solution 300:03

Day 7 – np.diag(), np.save(), np.load(), np.savetxt() & np.loadtxt()3 lectures • 1min

Exercise 11 question
Solution 100:01
Exercise 21 question
Solution 200:04
Exercise 31 question
Solution 300:05

Day 8 – np.reshape(), np.tolist() & np.pad()3 lectures • 1min

Exercise 11 question
Solution 100:03
Exercise 21 question
Solution 200:02
Exercise 31 question
Solution 300:03

Day 9 – np.zeros(), np.append() & np.intersect1d()3 lectures • 1min

Exercise 11 question
Solution 100:04
Exercise 21 question
Solution 200:05
Exercise 31 question
Solution 300:05

Day 10 – np.unique(), np.argmax() & np.sort()4 lectures • 1min

Exercise 11 question
Solution 100:04
Exercise 21 question
Solution 200:06
Exercise 31 question
Solution 300:07
Exercise 41 question
Solution 400:06

Day 11 – np.where(), np.ravel() & np.zeros_like()3 lectures • 1min

Exercise 11 question
Solution 100:07
Exercise 21 question
Solution 200:06
Exercise 31 question
Solution 300:06

Day 12 – np.full_like(), np.tri() & np.random.randint()4 lectures • 1min

Exercise 11 question
Solution 100:07
Exercise 21 question
Solution 200:01
Exercise 31 question
Solution 300:07
Exercise 41 question
Solution 400:03

Day 13 – np.sort() & np.expand_dims()4 lectures • 1min

Exercise 11 question
Solution 100:04
Exercise 21 question
Solution 200:03
Exercise 31 question
Solution 300:04
Exercise 41 question
Solution 400:04

Day 14 – np.append() & np.squeeze()3 lectures • 1min

Exercise 11 question
Solution 100:09
Exercise 21 question
Solution 200:03
Exercise 31 question
Solution 300:04

Day 15 – slicing4 lectures • 1min

Exercise 11 question
Solution 100:03
Exercise 21 question
Solution 200:03
Exercise 31 question
Solution 300:05
Exercise 41 question
Solution 400:04

Day 16 – np.concatenate() & np.column_stack()3 lectures • 1min

Exercise 11 question
Solution 100:06
Exercise 21 question
Solution 200:05
Exercise 31 question
Solution 300:08

Day 17 – np.split(), np.count_nonzero(), np.set_printoptions()4 lectures • 1min

Exercise 11 question
Solution 100:07
Exercise 21 question
Solution 200:04
Exercise 31 question
Solution 300:03
Exercise 41 question
Solution 400:03

Day 18 – np.delete() & np.linalg.norm()3 lectures • 1min

Exercise 11 question
Solution 100:04
Exercise 21 question
Solution 200:03
Exercise 31 question
Solution 300:04

Day 19 – np.divide(), np.multiply() & np.sqrt()3 lectures • 1min

Exercise 11 question
Solution 100:14
Exercise 21 question
Solution 200:13
Exercise 31 question
Solution 300:04

Day 20 – np.allclose(), np.dot() & np.linalg.det()3 lectures • 1min

Exercise 11 question
Solution 100:12
Exercise 21 question
Solution 200:11
Exercise 31 question
Solution 300:04

Day 21 – np.lingalg.ein(), np.lingalg.inv() & np.trace()4 lectures • 1min

Exercise 11 question
Solution 100:05
Exercise 21 question
Solution 200:04
Exercise 31 question
Solution 300:04
Exercise 41 question
Solution 400:07

Day 22 – np.random.shuffle(), np.argsort(), np.round() & np.roots()4 lectures • 1min

Exercise 11 question
Solution 100:07
Exercise 21 question
Solution 200:05
Exercise 31 question
Solution 300:03
Exercise 41 question
Solution 400:02

Day 23 – np.roots, np.polyadd() & np.sign()3 lectures • 1min

Exercise 11 question
Solution 100:05
Exercise 21 question
Solution 200:07
Exercise 31 question
Solution 300:04

Day 24 – dates3 lectures • 1min

Exercise 11 question
Solution 100:02
Exercise 21 question
Solution 200:03
Exercise 31 question
Solution 300:01

Day 25 – np.char.add(), np.char.rjust(), np.char.zfill() & np.char.split()3 lectures • 1min

Exercise 11 question
Solution 100:05
Exercise 21 question
Solution 200:11
Exercise 31 question
Solution 300:05

Day 26 – np.char.strip(), np.char.replace() & np.char.count()3 lectures • 1min

Exercise 11 question
Solution 100:03
Exercise 21 question
Solution 200:04
Exercise 31 question
Solution 301:15

Day 27 – np.char.replace() & np.char.startswith()3 lectures • 2min

Exercise 11 question
Solution 101:14
Exercise 21 question
Solution 200:49
Exercise 31 question
Solution 300:19

Day 28 – np.char.replace(), np.delete(), np.savetxt() & np.loadtxt()3 lectures • 2min

Exercise 11 question
Solution 100:42
Exercise 21 question
Solution 200:33
Exercise 31 question
Solution 300:24

Day 29 – data processing3 lectures • 2min

Exercise 11 question
Solution 100:45
Exercise 21 question
Solution 200:44
Exercise 31 question
Solution 300:57

Day 30 – data analysis4 lectures • 5min

Exercise 11 question
Solution 101:00
Exercise 21 question
Solution 201:09
Exercise 31 question
Solution 301:09
Exercise 41 question
Solution 401:11

Day 31 – pd.Series()3 lectures • 1min

Exercise 11 question
Solution 100:02
Exercise 21 question
Solution 200:05
Exercise 31 question
Solution 300:06

Day 32 – pd.Series() & pd.DataFrame()3 lectures • 1min

Exercise 11 question
Solution 100:06
Exercise 21 question
Solution 200:05
Exercise 31 question
Solution 300:07

Day 33 – pd.DataFrame()3 lectures • 1min

Exercise 11 question
Solution 100:07
Exercise 21 question
Solution 200:08
Exercise 31 question
Solution 300:06

Day 34 – pd.DataFrame() & pd.data_range()3 lectures • 1min

Exercise 11 question
Solution 100:07
Exercise 21 question
Solution 200:04
Exercise 31 question
Solution 300:05

Day 35 – pd.DataFrame() & pd.data_range()3 lectures • 1min

Exercise 11 question
Solution 100:05
Exercise 21 question
Solution 200:04
Exercise 31 question
Solution 300:09

Day 36 – pd.DataFrame() & pd.date_range()3 lectures • 1min

Exercise 11 question
Solution 100:10
Exercise 21 question
Solution 200:10
Exercise 31 question
Solution 300:10

Day 37 – pd.DataFrame.to_csv() & pd.read_csv()3 lectures • 1min

Exercise 11 question
Solution 100:10
Exercise 21 question
Solution 200:02
Exercise 31 question
Solution 300:02

Day 38 – pd.read_csv()3 lectures • 1min

Exercise 11 question
Solution 100:03
Exercise 21 question
Solution 200:03
Exercise 31 question
Solution 300:06

Day 39 – pd.DataFrame.groupby() & pd.DataFrame.iloc3 lectures • 1min

Exercise 11 question
Solution 100:06
Exercise 21 question
Solution 200:04
Exercise 31 question
Solution 300:04

Day 40 – pd.DataFrame.set_index() & pd.DataFrame.drop()3 lectures • 1min

Exercise 11 question
Solution 100:03
Exercise 21 question
Solution 200:06
Exercise 31 question
Solution 300:04

Day 41 – data processing3 lectures • 1min

Exercise 11 question
Solution 100:04
Exercise 21 question
Solution 200:04
Exercise 31 question
Solution 300:11

Day 42 – data processing & data types3 lectures • 1min

Exercise 11 question
Solution 100:15
Exercise 21 question
Solution 200:15
Exercise 31 question
Solution 300:04

Day 43 – grouping & mapping3 lectures • 1min

Exercise 11 question
Solution 100:04
Exercise 21 question
Solution 200:05
Exercise 31 question
Solution 300:04

Day 44 – concatenating & exporting3 lectures • 1min

Exercise 11 question
Solution 100:06
Exercise 21 question
Solution 200:07
Exercise 31 question
Solution 300:08

Day 45 – mapping & clipping3 lectures • 1min

Exercise 11 question
Solution 100:10
Exercise 21 question
Solution 200:11
Exercise 31 question
Solution 300:08

Day 46 – concatenating & querying3 lectures • 1min

Exercise 11 question
Solution 100:08
Exercise 21 question
Solution 200:16
Exercise 31 question
Solution 300:16

Day 47 – filtering & exporting3 lectures • 1min

Exercise 11 question
Solution 100:16
Exercise 21 question
Solution 200:16
Exercise 31 question
Solution 300:05

Day 48 – filtering & missing values3 lectures • 1min

Exercise 11 question
Solution 100:06
Exercise 21 question
Solution 200:06
Exercise 31 question
Solution 300:07

Day 49 – missing values3 lectures • 1min

Exercise 11 question
Solution 100:08
Exercise 21 question
Solution 200:08
Exercise 31 question
Solution 300:07

Day 50 – missing values & random3 lectures • 1min

Exercise 11 question
Solution 100:08
Exercise 21 question
Solution 200:06
Exercise 31 question
Solution 300:05

Day 51 – data preprocessing3 lectures • 1min

Exercise 11 question
Solution 100:03
Exercise 21 question
Solution 200:04
Exercise 31 question
Solution 300:05

Day 52 – data preprocessing3 lectures • 1min

Exercise 11 question
Solution 100:05
Exercise 21 question
Solution 200:04
Exercise 31 question
Solution 300:06

Day 53 – data preprocessing3 lectures • 1min

Exercise 11 question
Solution 100:06
Exercise 21 question
Solution 200:08
Exercise 31 question
Solution 300:09

Day 54 – grouping & mapping3 lectures • 1min

Exercise 11 question
Solution 100:06
Exercise 21 question
Solution 200:08
Exercise 31 question
Solution 300:06

Day 55 – data exploring3 lectures • 1min

Exercise 11 question
Solution 100:04
Exercise 21 question
Solution 200:06
Exercise 31 question
Solution 300:06

Day 56 – data preprocessing3 lectures • 1min

Exercise 11 question
Solution 100:07
Exercise 21 question
Solution 200:08
Exercise 31 question
Solution 300:08

Day 57 – grouping & querying3 lectures • 1min

Exercise 11 question
Solution 100:08
Exercise 21 question
Solution 200:08
Exercise 31 question
Solution 300:08

Day 58 – querying3 lectures • 1min

Exercise 11 question
Solution 100:08
Exercise 21 question
Solution 200:09
Exercise 31 question
Solution 300:03

Day 59 – duplicated data, data types3 lectures • 1min

Exercise 11 question
Solution 100:03
Exercise 21 question
Solution 200:04
Exercise 31 question
Solution 300:03

Day 60 – data types3 lectures • 1min

Exercise 11 question
Solution 100:06
Exercise 21 question
Solution 200:04
Exercise 31 question
Solution 300:08

Day 61 – categorical data3 lectures • 1min

Exercise 11 question
Solution 100:07
Exercise 21 question
Solution 200:07
Exercise 31 question
Solution 300:04

Day 62 – categorical data & dummies3 lectures • 1min

Exercise 11 question
Solution 100:04
Exercise 21 question
Solution 200:08
Exercise 31 question
Solution 300:09

Day 63 – data analysis3 lectures • 1min

Exercise 11 question
Solution 100:09
Exercise 21 question
Solution 200:11
Exercise 31 question
Solution 300:12

Day 64 – data preprocessing3 lectures • 1min

Exercise 11 question
Solution 100:16
Exercise 21 question
Solution 200:17
Exercise 31 question
Solution 300:18

Day 65 – JSON files3 lectures • 1min

Exercise 11 question
Solution 100:04
Exercise 21 question
Solution 200:06
Exercise 31 question
Solution 300:07

Day 66 – JSON files3 lectures • 1min

Exercise 11 question
Solution 100:05
Exercise 21 question
Solution 200:07
Exercise 31 question
Solution 300:07

Day 67 – CSV files3 lectures • 1min

Exercise 11 question
Solution 100:04
Exercise 21 question
Solution 200:09
Exercise 31 question
Solution 300:12

Day 68 – data processing3 lectures • 1min

Exercise 11 question
Solution 100:13
Exercise 21 question
Solution 200:16
Exercise 31 question
Solution 300:17

Day 69 – data preprocessing3 lectures • 1min

Exercise 11 question
Solution 100:19
Exercise 21 question
Solution 200:16
Exercise 31 question
Solution 300:17

Day 70 – merging3 lectures • 1min

Exercise 11 question
Solution 100:18
Exercise 21 question
Solution 200:19
Exercise 31 question
Solution 300:21

Day 71 – merging3 lectures • 1min

Exercise 11 question
Solution 100:11
Exercise 21 question
Solution 200:11
Exercise 31 question
Solution 300:11

Day 72 – merging3 lectures • 1min

Exercise 11 question
Solution 100:11
Exercise 21 question
Solution 200:12
Exercise 31 question
Solution 300:13

Day 73 – pivot tables4 lectures • 1min

Exercise 11 question
Solution 100:08
Exercise 21 question
Solution 200:08
Exercise 31 question
Solution 300:08
Exercise 41 question
Solution 400:09

Day 74 – imputing missing values3 lectures • 1min

Exercise 11 question
Solution 100:17
Exercise 21 question
Solution 200:19
Exercise 31 question
Solution 300:19

Day 75 – imputing missing values3 lectures • 1min

Exercise 11 question
Solution 100:20
Exercise 21 question
Solution 200:19
Exercise 31 question
Solution 300:16

Day 76 – continuous to categorical variable4 lectures • 1min

Exercise 11 question
Solution 100:18
Exercise 21 question
Solution 200:05
Exercise 31 question
Solution 300:06
Exercise 41 question
Solution 400:07

Day 77 – data preprocessing3 lectures • 1min

Exercise 11 question
Solution 100:09
Exercise 21 question
Solution 200:05
Exercise 31 question
Solution 300:08

Day 78 – data preprocessing3 lectures • 1min

Exercise 11 question
Solution 100:06
Exercise 21 question
Solution 200:07
Exercise 31 question
Solution 300:04

Day 79 – data exploring3 lectures • 1min

Exercise 11 question
Solution 100:02
Exercise 21 question
Solution 200:02
Exercise 31 question
Solution 300:03

Day 80 – train-test split, logistic regression & prediction4 lectures • 1min

Exercise 11 question
Solution 100:10
Exercise 21 question
Solution 200:10
Exercise 31 question
Solution 300:10
Exercise 41 question
Solution 400:12

Day 81 – LabelEncoder & OneHotEncoder3 lectures • 1min

Exercise 11 question
Solution 100:18
Exercise 21 question
Solution 200:18
Exercise 31 question
Solution 300:05

Day 82 – data preprocessing3 lectures • 1min

Exercise 11 question
Solution 100:06
Exercise 21 question
Solution 200:10
Exercise 31 question
Solution 300:15

Day 83 – data preprocessing3 lectures • 1min

Exercise 11 question
Solution 100:16
Exercise 21 question
Solution 200:16
Exercise 31 question
Solution 300:16

Day 84 – linear regression & polynomial features4 lectures • 1min

Exercise 11 question
Solution 100:11
Exercise 21 question
Solution 200:07
Exercise 31 question
Solution 300:06
Exercise 41 question
Solution 400:06

Day 85 – metrics3 lectures • 1min

Exercise 11 question
Solution 100:06
Exercise 21 question
Solution 200:06
Exercise 31 question
Solution 300:07

Day 86 – StandardScaler & entropy3 lectures • 1min

Exercise 11 question
Solution 100:07
Exercise 21 question
Solution 200:09
Exercise 31 question
Solution 300:11

Day 87 – accuracy, confusion matrix & decision tree3 lectures • 1min

Exercise 11 question
Solution 100:05
Exercise 21 question
Solution 200:05
Exercise 31 question
Solution 300:13

Day 88 – decision tree & grid search3 lectures • 1min

Exercise 11 question
Solution 100:13
Exercise 21 question
Solution 200:13
Exercise 31 question
Solution 300:19

Day 89 – random forest, grid search & CountVectorizer3 lectures • 1min

Exercise 11 question
Solution 100:13
Exercise 21 question
Solution 200:18
Exercise 31 question
Solution 300:10

Day 90 – CountVectorizer & TfidfVectorizer4 lectures • 1min

Exercise 11 question
Solution 100:10
Exercise 21 question
Solution 200:13
Exercise 31 question
Solution 300:13
Exercise 41 question
Solution 400:07

Day 91 – KMeans, AgglomerativeClustering & DBSCAN5 lectures • 1min

Exercise 11 question
Solution 100:28
Exercise 21 question
Solution 200:07
Exercise 31 question
Solution 300:08
Exercise 41 question
Solution 400:09
Exercise 51 question
Solution 500:07

Day 92 – PCA4 lectures • 1min

Exercise 11 question
Solution 100:17
Exercise 21 question
Solution 200:19
Exercise 31 question
Solution 300:14
Exercise 41 question
Solution 400:12

Day 93 – LocalOutlierFactor & IsolationForest4 lectures • 1min

Exercise 11 question
Solution 100:08
Exercise 21 question
Solution 200:08
Exercise 31 question
Solution 300:08
Exercise 41 question
Solution 400:08

Day 94 – KNeighborsClassifier & Logisticregression4 lectures • 1min

Exercise 11 question
Solution 100:11
Exercise 21 question
Solution 200:13
Exercise 31 question
Solution 300:13
Exercise 41 question
Solution 400:13

Day 95 – association rules3 lectures • 1min

Exercise 11 question
Solution 100:09
Exercise 21 question
Solution 200:23
Exercise 31 question
Solution 300:29

Day 96 – CountVectorizer3 lectures • 1min

Exercise 11 question
Solution 100:04
Exercise 21 question
Solution 200:05
Exercise 31 question
Solution 300:09

Day 97 – classification & MultinomialNB3 lectures • 1min

Exercise 11 question
Solution 100:19
Exercise 21 question
Solution 200:10
Exercise 31 question
Solution 300:19

Day 98 – data preprocessing3 lectures • 1min

Exercise 11 question
Solution 100:09
Exercise 21 question
Solution 200:09
Exercise 31 question
Solution 300:11

Day 99 – LinearRegression & R^2 score2 lectures • 1min

Exercise 11 question
Solution 100:17
Exercise 21 question
Solution 200:17

Day 100 – LinearRegression & GradientBoostingRegressor3 lectures • 1min

Exercise 11 question
Solution 100:16
Exercise 21 question
Solution 200:20
Exercise 31 question
Solution 300:20

Configuration (optional)7 lectures • 29min

Info00:17
Google Colab + Google DrivePreview02:55
Google Colab + GitHubPreview03:47
Google Colab – Intro09:57
Anaconda installation – Windows 10Preview04:07
Introduction to Spyder02:56
Anaconda installation – Linux04:39

Bonus1 lecture • 1min

Bonus00:08

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

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