Python Data Analysis: NumPy & Pandas Masterclass
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This is a hands-on, project-based course designed to help you master two of the most popular Python packages for data analysis and business intelligence: NumPy and Pandas.
We’ll start with a NumPy primer to introduce arrays and array properties, practice common operations like indexing, slicing, filtering and sorting, and explore important concepts like vectorization and broadcasting.
From there we’ll dive into Pandas, and focus on the essential tools and methods to explore, analyze, aggregate and transform series and dataframes. You’ll practice plotting dataframes with charts and graphs, manipulating time-series data, importing and exporting various file types, and combining dataframes using common join methods.
Throughout the course you’ll play the role of Data Analyst for Maven Mega Mart, a large, multinational corporation that operates a chain of retail and grocery stores. Using the Python skills you learn throughout the course, you’ll work with members of the Maven Mega Mart team to analyze products, pricing, transactions, and more.
COURSE OUTLINE:
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Intro to NumPy & Pandas
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Introduce NumPy and Pandas, two critical Python libraries that help structure data in arrays & DataFrames and contain built-in functions for data analysis
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Pandas Series
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Introduce Pandas Series, the Python equivalent of a column of data, and cover their basic properties, creation, manipulation, and useful functions for analysis
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Intro to DataFrames
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Work with Pandas DataFrames, the Python equivalent of an Excel or SQL table, and use them to store, manipulate, and analyze data efficiently
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Manipulating Python DataFrames
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Aggregate & reshape data in DataFrames by grouping columns, performing aggregation calculations, and pivoting & unpivoting data
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Basic Python Data Visualization
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Learn the basics of data visualization in Pandas, and use the plot method to create & customize line charts, bar charts, scatterplots, and histograms
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MID-COURSE PROJECT
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Put your skills to the test with a brand new dataset, and use your Python skills to analyze and evaluate a new retailer as a potential acquisition target for Maven MegaMart
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Analyzing Dates & Times
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Learn how to work with the datetime data type in Pandas to extract date components, group by dates, and perform time intelligence calculations like moving averages
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Importing & Exporting Data
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Read in data from flat files and apply processing steps during import, create DataFrames by querying SQL tables, and write data back out to its source
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Joining Python DataFrames
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Combine multiple DataFrames by joining data from related fields to add new columns, and appending data with the same fields to add new rows
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FINAL COURSE PROJECT
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Put the finishing touches on your project by joining a new table, performing time series analysis, optimizing your workflow, and writing out your results
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Join today and get immediate, lifetime access to the following:
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13+ hours of high-quality video
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Python NumPy & Pandas PDF ebook (350+ pages)
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Downloadable project files & solutions
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Expert support and Q&A forum
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30-day Udemy satisfaction guarantee
If you’re a data analyst, data scientist, business intelligence professional or data engineer looking to add Pandas to your Python skill set, this course is for you.
Happy learning!
-Chris Bruehl (Python Expert & Lead Python Instructor, Maven Analytics)
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Looking for our full business intelligence stack? Search for “Maven Analytics“ to browse our full course library, including Excel, Power BI, MySQL, Tableau and Machine Learning courses!
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7Pandas & NumPy IntroVídeo Aula
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8Numpy Arrays & Array PropertiesVídeo Aula
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9NumPy ArraysQuestionário
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10ASSIGNMENT: Array BasicsVídeo Aula
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11SOLUTION: Array BasicsVídeo Aula
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12Array CreationVídeo Aula
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13Random Number GenerationVídeo Aula
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14Random Number GenerationQuestionário
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15ASSIGNMENT: Array CreationVídeo Aula
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16SOLUTION: Array CreationVídeo Aula
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17Indexing & Slicing ArraysVídeo Aula
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18Indexing & Slicing ArraysQuestionário
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19ASSIGNMENT: Indexing & Slicing ArraysVídeo Aula
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20SOLUTION: Indexing & Slicing ArraysVídeo Aula
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21Array OperationsVídeo Aula
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22Array OperationsQuestionário
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23ASSIGNMENT: Array OperationsVídeo Aula
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24SOLUTION: Array OperationsVídeo Aula
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25Filtering Arrays & Modifying Array ValuesVídeo Aula
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26The Where FunctionVídeo Aula
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27Where FunctionQuestionário
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28ASSIGNMENT: Filtering & Modifying ArraysVídeo Aula
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29SOLUTION: Filtering & Modifying ArraysVídeo Aula
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30Array AggregationVídeo Aula
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31Array FunctionsVídeo Aula
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32Sorting ArraysVídeo Aula
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33Sorting ArraysQuestionário
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34ASSIGNMENT: Aggregation & SortingVídeo Aula
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35SOLUTION: Aggregation & SortingVídeo Aula
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36VectorizationVídeo Aula
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37BroadcastingVídeo Aula
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38BroadcastingQuestionário
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39ASSIGNMENT: Bringing it all togetherVídeo Aula
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40SOLUTION: Bringing it all togetherVídeo Aula
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41Key TakeawaysVídeo Aula
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42QUIZ: NumPy PrimerQuestionário
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43Series BasicsVídeo Aula
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44Pandas Data Types & Type ConversionVídeo Aula
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45Series BasicsQuestionário
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46ASSIGNMENT: Data Types & Type ConversionVídeo Aula
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47SOLUTION: Data Types & Type ConversionVídeo Aula
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48The Series Index & Custom IndicesVídeo Aula
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49The .iloc AccessorVídeo Aula
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50The .loc AccessorVídeo Aula
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51Duplicate Index Values & Resetting The IndexVídeo Aula
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52Duplicate Index Values & Resetting The IndexQuestionário
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53ASSIGNMENT: Accessing Data & Resetting The IndexVídeo Aula
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54SOLUTION: Accessing Data & Resetting The IndexVídeo Aula
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55Filtering Series & Logical TestsVídeo Aula
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56Sorting SeriesVídeo Aula
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57Sorting SeriesQuestionário
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58ASSIGNMENT: Sorting & Filtering SeriesVídeo Aula
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59SOLUTION: Sorting & Filtering SeriesVídeo Aula
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60Numeric Series OperationsVídeo Aula
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61Text Series OperationsVídeo Aula
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62Text Series OperationsQuestionário
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63ASSIGNMENT: Series OperationsVídeo Aula
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64SOLUTION: Series OperationsVídeo Aula
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65Numerical Series AggregationVídeo Aula
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66Categorical Series AggregationVídeo Aula
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67Categorical Series AggregationQuestionário
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68ASSIGNMENT: Series AggregationVídeo Aula
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69SOLUTION: Series AggregationVídeo Aula
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70Missing Data Representation in PandasVídeo Aula
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71Identifying Missing DataVídeo Aula
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72Fixing Missing DataVídeo Aula
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73Fixing Missing DataQuestionário
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74ASSIGNMENT: Missing DataVídeo Aula
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75SOLUTION: Missing DataVídeo Aula
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76Applying Custom Functions to SeriesVídeo Aula
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77Pandas Where (vs. NumPy Where)Vídeo Aula
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78Pandas Where (vs. NumPy Where)Questionário
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79ASSIGNMENT: Apply & WhereVídeo Aula
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80SOLUTION: Apply & WhereVídeo Aula
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81Key TakeawaysVídeo Aula
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82QUIZ: Pandas SeriesQuestionário
