Data Analysis with Python NumPy Pandas Masterclass
What you’ll learn
Master the essentials of NumPy and Pandas, two of Python’s most powerful data analysis packages
Learn how to explore, transform, aggregate and join NumPy arrays and Pandas DataFrames
Analyze and manipulate dates and times for time intelligence and time-series analysis
Visualize raw data using plot methods and common chart options like line charts, bar charts, scatter plots and histograms
Import and export flat files, Excel workbooks and SQL database tables using Pandas
Build powerful, practical skills for modern analytics and business intelligence
Requirements
We’ll use Anaconda & Jupyter Notebooks (a free, user-friendly coding environment)
Familiarity with base Python is strongly recommended, but not a strict prerequisite
Description
This is a hands-on, project-based course designed to help you master two of the most popular Python packages for data analysis: 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:Intro to NumPy & PandasIntroduce NumPy and Pandas, two critical Python libraries that help structure data in arrays & DataFrames and contain built-in functions for data analysisPandas SeriesIntroduce Pandas Series, the Python equivalent of a column of data, and cover their basic properties, creation, manipulation, and useful functions for analysisIntro to DataFramesWork with Pandas DataFrames, the Python equivalent of an Excel or SQL table, and use them to store, manipulate, and analyze data efficientlyManipulating DataFramesAggregate & reshape data in DataFrames by grouping columns, performing aggregation calculations, and pivoting & unpivoting dataBasic Data VisualizationLearn the basics of data visualization in Pandas, and use the plot method to create & customize line charts, bar charts, scatterplots, and histogramsMID-COURSE PROJECTPut 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 MegaMartAnalyzing Dates & TimesLearn how to work with the datetime data type in Pandas to extract date components, group by dates, and perform time intelligence calculations like moving averagesImporting & Exporting DataRead in data from flat files and apply processing steps during import, create DataFrames by querying SQL tables, and write data back out to its sourceJoining DataFramesCombine multiple DataFrames by joining data from related fields to add new columns, and appending data with the same fields to add new rowsFINAL COURSE PROJECTPut the finishing touches on your project by joining a new table, performing time series analysis, optimizing your workflow, and writing out your resultsJoin today and get immediate, lifetime access to the following:13+ hours of high-quality videoPython & Pandas PDF ebook (350+ pages)Downloadable project files & solutionsExpert support and Q&A forum30-day Udemy satisfaction guaranteeIf you’re a data scientist, BI analyst 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)__________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!See why our courses are among the TOP-RATED on Udemy:”Some of the BEST courses I’ve ever taken. I’ve studied several programming languages, Excel, VBA and web dev, and Maven is among the very best I’ve seen!” Russ C.”This is my fourth course from Maven Analytics and my fourth 5-star review, so I’m running out of things to say. I wish Maven was in my life earlier!” Tatsiana M.”Maven Analytics should become the new standard for all courses taught on Udemy!” Jonah M.
Overview
Section 1: Getting Started
Lecture 1 Course Structure & Outline
Lecture 2 READ ME: Important Notes for New Students
Lecture 3 DOWNLOAD: Course Resources
Lecture 4 Introducing the Course Project
Lecture 5 Setting Expectations
Lecture 6 Jupyter Installation & Launch
Section 2: NumPy Primer
Lecture 7 Pandas & NumPy Intro
Lecture 8 Numpy Arrays & Array Properties
Lecture 9 ASSIGNMENT: Array Basics
Lecture 10 Array Creation
Lecture 11 SOLUTION: Array Basics
Lecture 12 Random Number Generation
Lecture 13 ASSIGNMENT: Array Creation
Lecture 14 SOLUTION: Array Creation
Lecture 15 Indexing & Slicing Arrays
Lecture 16 ASSIGNMENT: Indexing & Slicing Arrays
Lecture 17 SOLUTION: Indexing & Slicing Arrays
Lecture 18 Array Operations
Lecture 19 ASSIGNMENT: Array Operations
Lecture 20 SOLUTION: Array Operations
Lecture 21 Filtering Arrays & Modifying Array Values
Lecture 22 The Where Function
Lecture 23 ASSIGNMENT: Filtering & Modifying Arrays
Lecture 24 SOLUTION: Filtering & Modifying Arrays
Lecture 25 Array Aggregation
Lecture 26 Array Functions
Lecture 27 Sorting Arrays
Lecture 28 ASSIGNMENT: Aggregation & Sorting
Lecture 29 SOLUTION: Aggregation & Sorting
Lecture 30 Vectorization
Lecture 31 Broadcasting
Lecture 32 ASSIGNMENT: Bringing it all together
Lecture 33 SOLUTION: Bringing it all together
Lecture 34 Key Takeaways
Section 3: Pandas Series
Lecture 35 Series Basics
Lecture 36 Pandas Data Types & Type Conversion
Lecture 37 ASSIGNMENT: Data Types & Type Conversion
Lecture 38 SOLUTION: Data Types & Type Conversion
Lecture 39 The Series Index & Custom Indices
Lecture 40 The .iloc Accessor
Lecture 41 The .loc Accessor
Lecture 42 Duplicate Index Values & Resetting The Index
Lecture 43 ASSIGNMENT: Accessing Data & Resetting The Index
Lecture 44 SOLUTION: Accessing Data & Resetting The Index
Lecture 45 Filtering Series & Logical Tests
Lecture 46 Sorting Series
Lecture 47 ASSIGNMENT: Sorting & Filtering Series
Lecture 48 SOLUTION: Sorting & Filtering Series
Lecture 49 Numeric Series Operations
Lecture 50 Text Series Operations
Lecture 51 ASSIGNMENT: Series Operations
Lecture 52 SOLUTION: Series Operations
Lecture 53 Numerical Series Aggregation
Lecture 54 Categorical Series Aggregation
Lecture 55 ASSIGNMENT: Series Aggregation
Lecture 56 SOLUTION: Series Aggregation
Lecture 57 Missing Data Representation in Pandas
Lecture 58 Identifying Missing Data
Lecture 59 Fixing Missing Data
Lecture 60 ASSIGNMENT: Missing Data
Lecture 61 SOLUTION: Missing Data
Lecture 62 Applying Custom Functions to Series
Lecture 63 Pandas Where (vs. NumPy Where)
Lecture 64 ASSIGNMENT: Apply & Where
Lecture 65 SOLUTION: Apply & Where
Lecture 66 Key Takeaways
Section 4: Intro to DataFrames
Lecture 67 DataFrame Basics
Lecture 68 Creating a DataFrame
Lecture 69 ASSIGNMENT: DataFrame Basics
Lecture 70 SOLUTION: DataFrame Basics
Lecture 71 Exploring DataFrames: Heads, Tails & Sample
Lecture 72 Exploring DataFrames: Info & Describe
Lecture 73 ASSIGNMENT: Exploring a DataFrame
Lecture 74 SOLUTION: Exploring a DataFrame
Lecture 75 Accessing DataFrame Columns
Lecture 76 Accessing DataFrame Data with .iloc & .loc
Lecture 77 ASSIGNMENT: Accessing DataFrame Data
Lecture 78 SOLUTION: Accessing DataFrame Data
Lecture 79 Dropping Columns & Rows
Lecture 80 Identifying & Dropping Duplicates
Lecture 81 ASSIGNMENT: Dropping Data
Lecture 82 SOLUTION: Dropping Data
Lecture 83 Missing Data
Lecture 84 ASSIGNMENT: Missing Data
Lecture 85 SOLUTION: Missing Data
Lecture 86 Filtering DataFrames
Lecture 87 PRO TIP: The Query Method
Lecture 88 ASSIGNMENT: Filtering DataFrames
Lecture 89 SOLUTION: Filtering DataFrames
Lecture 90 Sorting DataFrames
Lecture 91 ASSIGNMENT: Sorting DataFrames
Lecture 92 SOLUTION: Sorting DataFrames
Lecture 93 Renaming & Reordering Columns
Lecture 94 ASSIGNMENT: Renaming & Reordering Columns
Lecture 95 SOLUTION: Renaming & Reordering Columns
Lecture 96 Arithmetic & Boolean Column Creation
Lecture 97 ASSIGNMENT: Arithmetic & Boolean Columns
Lecture 98 SOLUTION: Arithmetic & Boolean Columns
Lecture 99 PRO TIP: Advanced Conditional Columns with Select
Lecture 100 ASSIGNMENT: The Select Function
Lecture 101 SOLUTION: The Select Function
Lecture 102 The Map Method
Lecture 103 PRO TIP: Multiple Column Creation with Assign
Lecture 104 ASSIGNMENT: Map & Assign
Lecture 105 SOLUTION: Map & Assign
Lecture 106 The Categorical Data Type
Lecture 107 Type Conversion
Lecture 108 PRO TIP: Memory Usage & DataTypes
Lecture 109 PRO TIP: Downcasting Numeric Data Types
Lecture 110 ASSIGNMENT: DataFrame DataTypes
Lecture 111 SOLUTION: DataFrame DataTypes
Lecture 112 Key Takeways
Section 5: Aggregating & Reshaping DataFrames
Lecture 113 Basic Aggregations
Lecture 114 The Groupby Method
Lecture 115 ASSIGNMENT: Groupby
Lecture 116 SOLUTION: Groupby
Lecture 117 Grouping By Multiple Columns
Lecture 118 ASSIGNMENT: Grouping By Multiple Columns
Lecture 119 SOLUTION: Grouping By Multiple Columns
Lecture 120 Multi-Index DataFrames
Lecture 121 Modifying Multi-Indices
Lecture 122 ASSIGNMENT: Multi-Index DataFrames
Lecture 123 SOLUTION: Multi-Index DataFrames
Lecture 124 The Agg Method & Named Aggregations
Lecture 125 ASSIGNMENT: The Agg Method
Lecture 126 SOLUTION: The Agg Method
Lecture 127 PRO TIP: Transforming DataFrames
Lecture 128 ASSIGNMENT: Transforming a DataFrame
Lecture 129 SOLUTION: Transforming a DataFrame
Lecture 130 Pivot Tables in Pandas
Lecture 131 Multiple Aggregation Pivot Tables
Lecture 132 PRO TIP: Pivot Table Heatmaps
Lecture 133 Melting DataFrames
Lecture 134 ASSIGNMENT: Pivot & Melt
Lecture 135 SOLUTION: Pivot & Melt
Lecture 136 Key Takeaways
Section 6: Basic Data Visualization in Python
Lecture 137 The matplotlib API & The .plot() Method
Lecture 138 ASSIGNMENT: Basic Line Chart
Lecture 139 SOLUTION: Basic Line Chart
Lecture 140 Chart Titles
Lecture 141 Chart Colors
Lecture 142 Line Styles
Lecture 143 Chart Legends & Gridlines
Lecture 144 Chart Styles
Lecture 145 ASSIGNMENT: Stylized Line Chart
Lecture 146 SOLUTION: Stylized Line Chart
Lecture 147 Subplots & Figure Size
Lecture 148 ASSIGNMENT: Subplots
Lecture 149 SOLUTION: Subplots
Lecture 150 Bar Charts
Lecture 151 Grouped & Stacked Bar Charts
Lecture 152 ASSIGNMENT: Bar Charts
Lecture 153 SOLUTION: Bar Charts
Lecture 154 Pie Charts & Scatterplots
Lecture 155 ASSIGNMENT: Scatterplots
Lecture 156 SOLUTION: Scatterplots
Lecture 157 Histograms
Lecture 158 ASSIGNMENT: Histograms
Lecture 159 SOLUTION: Histograms
Lecture 160 Saving Plots & Further Exploration
Lecture 161 Key Takeaways
Section 7: MID-COURSE PROJECT
Lecture 162 Mid-Course Project Intro
Lecture 163 SOLUTION: Mid-Course Project
Section 8: Analyzing Dates & Times
Lecture 164 Times in Python and Pandas
Lecture 165 Converting To Datetimes
Lecture 166 Formatting Dates
Lecture 167 Date & Time Parts
Lecture 168 ASSIGNMENT: Pandas Datetime Basics
Lecture 169 SOLUTION: Pandas Datetime Basics
Lecture 170 Time Deltas & Arithmetic
Lecture 171 ASSIGNMENT: Time Deltas
Lecture 172 SOLUTION: Time Deltas
Lecture 173 Time Series Indices
Lecture 174 Missing Time Series Data
Lecture 175 ASSIGNMENT: Missing Time Series Data
Lecture 176 SOLUTION: Missing Time Series Data
Lecture 177 Shifting Time Series
Lecture 178 PRO TIP: DIFF()
Lecture 179 ASSIGNMENT: Shift & Diff
Lecture 180 SOLUTION: Shift & Diff
Lecture 181 Aggregation & Resampling
Lecture 182 ASSIGNMENT: Resampling
Lecture 183 SOLUTION: Resampling
Lecture 184 Rolling Aggregations
Lecture 185 ASSIGNMENT: Rolling Aggregations
Lecture 186 SOLUTION: Rolling Aggregations
Lecture 187 Key Takeaways
Section 9: Importing & Exporting Data
Lecture 188 Preprocessing with read_csv
Lecture 189 Column Selection
Lecture 190 Row Selection & Missing Values
Lecture 191 Parsing Dates & Data Types
Lecture 192 PRO TIP: Converters
Lecture 193 ASSIGNMENT: Importing Data
Lecture 194 SOLUTION: Importing Data
Lecture 195 Importing from Text & Excel Files
Lecture 196 Exporting to Flat Files
Lecture 197 ASSIGNMENT: Importing & Exporting Excel Data
Lecture 198 SOLUTION: Importing & Exporting Excel Data
Lecture 199 Working With SQL Databases
Lecture 200 Other Supported File Formats
Lecture 201 Key Takeaways
Section 10: Joining DataFrames
Lecture 202 Why Multiple Tables
Lecture 203 Appending DataFrames
Lecture 204 ASSIGNMENT: Appending DataFrames
Lecture 205 SOLUTION: Appending DataFrames
Lecture 206 Joining DataFrames
Lecture 207 Join Types
Lecture 208 Inner Joins
Lecture 209 Left Joins
Lecture 210 ASSIGNMENT: Joining DataFrames
Lecture 211 SOLUTION: Joining DataFrames
Lecture 212 The Join Method
Lecture 213 Key Takeaways
Section 11: FINAL COURSE PROJECT
Lecture 214 Final Project Intro
Lecture 215 SOLUTION: Final Project
Section 12: BONUS LESSON
Lecture 216 BONUS LESSON
Analysts or BI professionals looking to learn data analysis with NumPy and Pandas,Aspiring data scientists who want to build or strengthen their Python skills,Anyone interested in learning one of the most popular open source programming languages in the world,Students looking to learn powerful, practical skills with unique, hands-on projects and course demos
Course Information:
Udemy | English | 13h 31m | 4.22 GB
Created by: Maven Analytics
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