100 Days Of Code: Data Scientist Challenge 2022

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

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 arraysgenerating numpy arraysgenerating numpy arrays with random valuesiterating through arraysdealing with missing valuesworking with matricesreading/writing filesjoining arraysreshaping arrayscomputing basic array statisticssorting arraysfiltering arraysimage as an arraylinear algebramatrix multiplicationdeterminant of the matrixeigenvalues and eignevectorsinverse matrixshuffling arraysworking with polynomialsworking with datesworking with strings in arraysolving systems of equationsworking with Seriesworking with DatetimeIndexworking with DataFramesreading/writing filesworking with different data types in DataFramesworking with indexesworking with missing valuesfiltering datasorting datagrouping datamapping columnscomputing correlationconcatenating DataFramescalculating cumulative statisticsworking with duplicate valuespreparing data to machine learning modelsdummy encodingworking with csv and json fillesmerging DataFramespivot tablespreparing data to machine learning modelsworking with missing values, SimpleImputer classclassification, regression, clusteringdiscretizationfeature extractionPolynomialFeatures classLabelEncoder classOneHotEncoder classStandardScaler classdummy encodingsplitting data into train and test setLogisticRegression classconfusion matrixclassification reportLinearRegression classMAE - Mean Absolute ErrorMSE - Mean Squared Errorsigmoid() functionentorpyaccuracy scoreDecisionTreeClassifier classGridSearchCV classRandomForestClassifier classCountVectorizer classTfidfVectorizer classKMeans classAgglomerativeClustering classHierarchicalClustering classDBSCAN classdimensionality reduction, PCA analysisAssociation RulesLocalOutlierFactor classIsolationForest classKNeighborsClassifier classMultinomialNB classGradientBoostingRegressor classThis 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 SurveyAccording 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.

Overview

Section 1: Tips

Lecture 1 A few words from the author

Lecture 2 Configuration

Section 2: Starter

Lecture 3 Solution 0

Section 3: Day 1 - np.all() & np.any()

Lecture 4 Solution 1

Lecture 5 Solution 2

Lecture 6 Solution 3

Lecture 7 Solution 4

Section 4: Day 2 - np.isnan(), np.allclose() & np.equal()

Lecture 8 Solution 1

Lecture 9 Solution 2

Lecture 10 Solution 3

Section 5: Day 3 - np.greater(), np.zeros(), np.ones() & np.full()

Lecture 11 Solution 1

Lecture 12 Solution 2

Lecture 13 Solution 3

Section 6: Day 4 - np.arange() & np.eye()

Lecture 14 Solution 1

Lecture 15 Solution 2

Lecture 16 Solution 3

Section 7: Day 5 - np.random.rand(), np.random.randn() & np.sqrt()

Lecture 17 Solution 1

Lecture 18 Solution 2

Lecture 19 Solution 3

Section 8: Day 6 - np.nditer(), np.linspace() & np.random.choice()

Lecture 20 Solution 1

Lecture 21 Solution 2

Lecture 22 Solution 3

Section 9: Day 7 - np.diag(), np.save(), np.load(), np.savetxt() & np.loadtxt()

Lecture 23 Solution 1

Lecture 24 Solution 2

Lecture 25 Solution 3

Section 10: Day 8 - np.reshape(), np.tolist() & np.pad()

Lecture 26 Solution 1

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Lecture 28 Solution 3

Section 11: Day 9 - np.zeros(), np.append() & np.intersect1d()

Lecture 29 Solution 1

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Section 12: Day 10 - np.unique(), np.argmax() & np.sort()

Lecture 32 Solution 1

Lecture 33 Solution 2

Lecture 34 Solution 3

Lecture 35 Solution 4

Section 13: Day 11 - np.where(), np.ravel() & np.zeros_like()

Lecture 36 Solution 1

Lecture 37 Solution 2

Lecture 38 Solution 3

Section 14: Day 12 - np.full_like(), np.tri() & np.random.randint()

Lecture 39 Solution 1

Lecture 40 Solution 2

Lecture 41 Solution 3

Lecture 42 Solution 4

Section 15: Day 13 - np.sort() & np.expand_dims()

Lecture 43 Solution 1

Lecture 44 Solution 2

Lecture 45 Solution 3

Lecture 46 Solution 4

Section 16: Day 14 - np.append() & np.squeeze()

Lecture 47 Solution 1

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Lecture 49 Solution 3

Section 17: Day 15 - slicing

Lecture 50 Solution 1

Lecture 51 Solution 2

Lecture 52 Solution 3

Lecture 53 Solution 4

Section 18: Day 16 - np.concatenate() & np.column_stack()

Lecture 54 Solution 1

Lecture 55 Solution 2

Lecture 56 Solution 3

Section 19: Day 17 - np.split(), np.count_nonzero(), np.set_printoptions()

Lecture 57 Solution 1

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Lecture 59 Solution 3

Lecture 60 Solution 4

Section 20: Day 18 - np.delete() & np.linalg.norm()

Lecture 61 Solution 1

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Lecture 63 Solution 3

Section 21: Day 19 - np.divide(), np.multiply() & np.sqrt()

Lecture 64 Solution 1

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Lecture 66 Solution 3

Section 22: Day 20 - np.allclose(), np.dot() & np.linalg.det()

Lecture 67 Solution 1

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Lecture 69 Solution 3

Section 23: Day 21 - np.lingalg.ein(), np.lingalg.inv() & np.trace()

Lecture 70 Solution 1

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Lecture 72 Solution 3

Lecture 73 Solution 4

Section 24: Day 22 - np.random.shuffle(), np.argsort(), np.round() & np.roots()

Lecture 74 Solution 1

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Lecture 76 Solution 3

Lecture 77 Solution 4

Section 25: Day 23 - np.roots, np.polyadd() & np.sign()

Lecture 78 Solution 1

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Lecture 80 Solution 3

Section 26: Day 24 - dates

Lecture 81 Solution 1

Lecture 82 Solution 2

Lecture 83 Solution 3

Section 27: Day 25 - np.char.add(), np.char.rjust(), np.char.zfill() & np.char.split()

Lecture 84 Solution 1

Lecture 85 Solution 2

Lecture 86 Solution 3

Section 28: Day 26 - np.char.strip(), np.char.replace() & np.char.count()

Lecture 87 Solution 1

Lecture 88 Solution 2

Lecture 89 Solution 3

Section 29: Day 27 - np.char.replace() & np.char.startswith()

Lecture 90 Solution 1

Lecture 91 Solution 2

Lecture 92 Solution 3

Section 30: Day 28 - np.char.replace(), np.delete(), np.savetxt() & np.loadtxt()

Lecture 93 Solution 1

Lecture 94 Solution 2

Lecture 95 Solution 3

Section 31: Day 29 - data processing

Lecture 96 Solution 1

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Lecture 98 Solution 3

Section 32: Day 30 - data analysis

Lecture 99 Solution 1

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Lecture 102 Solution 4

Section 33: Day 31 - pd.Series()

Lecture 103 Solution 1

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Lecture 105 Solution 3

Section 34: Day 32 - pd.Series() & pd.DataFrame()

Lecture 106 Solution 1

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Lecture 108 Solution 3

Section 35: Day 33 - pd.DataFrame()

Lecture 109 Solution 1

Lecture 110 Solution 2

Lecture 111 Solution 3

Section 36: Day 34 - pd.DataFrame() & pd.data_range()

Lecture 112 Solution 1

Lecture 113 Solution 2

Lecture 114 Solution 3

Section 37: Day 35 - pd.DataFrame() & pd.data_range()

Lecture 115 Solution 1

Lecture 116 Solution 2

Lecture 117 Solution 3

Section 38: Day 36 - pd.DataFrame() & pd.date_range()

Lecture 118 Solution 1

Lecture 119 Solution 2

Lecture 120 Solution 3

Section 39: Day 37 - pd.DataFrame.to_csv() & pd.read_csv()

Lecture 121 Solution 1

Lecture 122 Solution 2

Lecture 123 Solution 3

Section 40: Day 38 - pd.read_csv()

Lecture 124 Solution 1

Lecture 125 Solution 2

Lecture 126 Solution 3

Section 41: Day 39 - pd.DataFrame.groupby() & pd.DataFrame.iloc

Lecture 127 Solution 1

Lecture 128 Solution 2

Lecture 129 Solution 3

Section 42: Day 40 - pd.DataFrame.set_index() & pd.DataFrame.drop()

Lecture 130 Solution 1

Lecture 131 Solution 2

Lecture 132 Solution 3

Section 43: Day 41 - data processing

Lecture 133 Solution 1

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Section 44: Day 42 - data processing & data types

Lecture 136 Solution 1

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Section 45: Day 43 - grouping & mapping

Lecture 139 Solution 1

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Section 46: Day 44 - concatenating & exporting

Lecture 142 Solution 1

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Section 47: Day 45 - mapping & clipping

Lecture 145 Solution 1

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Lecture 147 Solution 3

Section 48: Day 46 - concatenating & querying

Lecture 148 Solution 1

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Section 49: Day 47 - filtering & exporting

Lecture 151 Solution 1

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Section 50: Day 48 - filtering & missing values

Lecture 154 Solution 1

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Section 51: Day 49 - missing values

Lecture 157 Solution 1

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Lecture 159 Solution 3

Section 52: Day 50 - missing values & random

Lecture 160 Solution 1

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Section 53: Day 51 - data preprocessing

Lecture 163 Solution 1

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Section 54: Day 52 - data preprocessing

Lecture 166 Solution 1

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Section 55: Day 53 - data preprocessing

Lecture 169 Solution 1

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Section 56: Day 54 - grouping & mapping

Lecture 172 Solution 1

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Section 57: Day 55 - data exploring

Lecture 175 Solution 1

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Section 58: Day 56 - data preprocessing

Lecture 178 Solution 1

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Section 59: Day 57 - grouping & querying

Lecture 181 Solution 1

Lecture 182 Solution 2

Lecture 183 Solution 3

Section 60: Day 58 - querying

Lecture 184 Solution 1

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Section 61: Day 59 - duplicated data, data types

Lecture 187 Solution 1

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Lecture 189 Solution 3

Section 62: Day 60 - data types

Lecture 190 Solution 1

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Lecture 192 Solution 3

Section 63: Day 61 - categorical data

Lecture 193 Solution 1

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Lecture 195 Solution 3

Section 64: Day 62 - categorical data & dummies

Lecture 196 Solution 1

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Lecture 198 Solution 3

Section 65: Day 63 - data analysis

Lecture 199 Solution 1

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Section 66: Day 64 - data preprocessing

Lecture 202 Solution 1

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Section 67: Day 65 - JSON files

Lecture 205 Solution 1

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Lecture 207 Solution 3

Section 68: Day 66 - JSON files

Lecture 208 Solution 1

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Lecture 210 Solution 3

Section 69: Day 67 - CSV files

Lecture 211 Solution 1

Lecture 212 Solution 2

Lecture 213 Solution 3

Section 70: Day 68 - data processing

Lecture 214 Solution 1

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Lecture 216 Solution 3

Section 71: Day 69 - data preprocessing

Lecture 217 Solution 1

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Section 72: Day 70 - merging

Lecture 220 Solution 1

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Section 73: Day 71 - merging

Lecture 223 Solution 1

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Section 74: Day 72 - merging

Lecture 226 Solution 1

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Lecture 228 Solution 3

Section 75: Day 73 - pivot tables

Lecture 229 Solution 1

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Lecture 232 Solution 4

Section 76: Day 74 - imputing missing values

Lecture 233 Solution 1

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Lecture 235 Solution 3

Section 77: Day 75 - imputing missing values

Lecture 236 Solution 1

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Lecture 238 Solution 3

Section 78: Day 76 - continuous to categorical variable

Lecture 239 Solution 1

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Lecture 242 Solution 4

Section 79: Day 77 - data preprocessing

Lecture 243 Solution 1

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Section 80: Day 78 - data preprocessing

Lecture 246 Solution 1

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Section 81: Day 79 - data exploring

Lecture 249 Solution 1

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Lecture 251 Solution 3

Section 82: Day 80 - train-test split, logistic regression & prediction

Lecture 252 Solution 1

Lecture 253 Solution 2

Lecture 254 Solution 3

Lecture 255 Solution 4

Section 83: Day 81 - LabelEncoder & OneHotEncoder

Lecture 256 Solution 1

Lecture 257 Solution 2

Lecture 258 Solution 3

Section 84: Day 82 - data preprocessing

Lecture 259 Solution 1

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Section 85: Day 83 - data preprocessing

Lecture 262 Solution 1

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Section 86: Day 84 - linear regression & polynomial features

Lecture 265 Solution 1

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Lecture 268 Solution 4

Section 87: Day 85 - metrics

Lecture 269 Solution 1

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Lecture 271 Solution 3

Section 88: Day 86 - StandardScaler & entropy

Lecture 272 Solution 1

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Lecture 274 Solution 3

Section 89: Day 87 - accuracy, confusion matrix & decision tree

Lecture 275 Solution 1

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Lecture 277 Solution 3

Section 90: Day 88 - decision tree & grid search

Lecture 278 Solution 1

Lecture 279 Solution 2

Lecture 280 Solution 3

Section 91: Day 89 - random forest, grid search & CountVectorizer

Lecture 281 Solution 1

Lecture 282 Solution 2

Lecture 283 Solution 3

Section 92: Day 90 - CountVectorizer & TfidfVectorizer

Lecture 284 Solution 1

Lecture 285 Solution 2

Lecture 286 Solution 3

Lecture 287 Solution 4

Section 93: Day 91 - KMeans, AgglomerativeClustering & DBSCAN

Lecture 288 Solution 1

Lecture 289 Solution 2

Lecture 290 Solution 3

Lecture 291 Solution 4

Lecture 292 Solution 5

Section 94: Day 92 - PCA

Lecture 293 Solution 1

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Lecture 296 Solution 4

Section 95: Day 93 - LocalOutlierFactor & IsolationForest

Lecture 297 Solution 1

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Lecture 299 Solution 3

Lecture 300 Solution 4

Section 96: Day 94 - KNeighborsClassifier & Logisticregression

Lecture 301 Solution 1

Lecture 302 Solution 2

Lecture 303 Solution 3

Lecture 304 Solution 4

Section 97: Day 95 - association rules

Lecture 305 Solution 1

Lecture 306 Solution 2

Lecture 307 Solution 3

Section 98: Day 96 - CountVectorizer

Lecture 308 Solution 1

Lecture 309 Solution 2

Lecture 310 Solution 3

Section 99: Day 97 - classification & MultinomialNB

Lecture 311 Solution 1

Lecture 312 Solution 2

Lecture 313 Solution 3

Section 100: Day 98 - data preprocessing

Lecture 314 Solution 1

Lecture 315 Solution 2

Lecture 316 Solution 3

Section 101: Day 99 - LinearRegression & R^2 score

Lecture 317 Solution 1

Lecture 318 Solution 2

Section 102: Day 100 - LinearRegression & GradientBoostingRegressor

Lecture 319 Solution 1

Lecture 320 Solution 2

Lecture 321 Solution 3

Section 103: Configuration (optional)

Lecture 322 Info

Lecture 323 Google Colab + Google Drive

Lecture 324 Google Colab + GitHub

Lecture 325 Google Colab - Intro

Lecture 326 Anaconda installation - Windows 10

Lecture 327 Introduction to Spyder

Lecture 328 Anaconda installation - Linux

Lecture 329 Spyder

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