This is a complete course for the formation of Data Scientist, with more than 250 exercises from A-Z, covering from the most basic to the most advanced concepts. It focuses on active methodologies, where the student is the protagonist in this process, thus, we bring several solved exercises, notebooks of contents summary and much more, with a focus on learning programming based on practice and simulation of real problems (such as data cleaning, treatment of missings, separation of data in training and testing, grouping and joining of datasets, among others).
In this sense, the course has exercises solved on the main Python libraries for Data Science: NumPy, Pandas, Matplotlib and Seaborn. In addition, it seeks to rescue elementary concepts of Linear Algebra, through the NumPy library.
In general, the course presents exercises that encompass the main functions of NumPy for Data Science, such as aggregation functions, matrix definition, matrix operations, among others. As for Pandas, we seek to offer an overview from the definition of Series and DataFrames, inspection of datasets, boolean selection, filtering of rows of columns, removal of rows and columns, treatment of missing data, grouping and joining functions, opening and writing files, descriptive statistics functions, among other topics.
Finally, there are several problems related to data visualization, with the libraries Matplotlib and Seaborn, from classic datasets. Notions of time series and finance are also introduced. There are also examples of how to prepare a dataset for a Machine Learning project.
NumPy
Linear Algebra with NumPy
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6Introduction
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7Exercise 1
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8Solution - Exercise 1
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9Exercise 2
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10Solution - Exercise 2
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11Exercise 3
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12Solution - Exercise 3
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13Exercise 4
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14Solution - Exercise 4
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15Exercise 5
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16Solution - Exercise 5
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17Exercise 6
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18Solution - Exercise 6
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19Exercise 7
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20Solution - Exercise 7
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21Exercise 8
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22Solution - Exercise 8
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23Exercise 9
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24Solution - Exercise 9
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25Exercise 10
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26Solution - Exercise 10
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27Exercise 11
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28Solution - Exercise 11
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29Exercise 12
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30Solution - Exercise 12
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31Exercise 13
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32Exercise 14
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33Solution - Exercise 14
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34Exercise 15
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35Solution - Exercise 15
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36Exercise 16
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37Solution - Exercise 16
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38Exercise 17
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39Solution - Exercise 17
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40Exercise 18
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41Solution - Exercise 18
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42Exercise 19
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43Solution - Exercise 19
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44Exercise 20
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45Solution - Exercise 20
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46Exercise 21
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47Solution - Exercise 21
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48Exercise 22
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49Solution - Exercise 22
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50Exercise 23
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51Solution - Exercise 23
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52Exercise 24
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53Solution - Exercise 24
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54Exercise 25
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55Solution - Exercise 25
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56Exercise 26
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57Solution - Exercise 26
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58Exercise 27
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59Solution - Exercise 27
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60NumPy - Recap