Udemy - Complete Data Wrangling & Data Visualisation With Python [DC]

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[DesireCourse.Net] Udemy - Complete Data Wrangling & Data Visualisation With Python 1. INTRODUCTION TO THE COURSE The Key Concepts and Software Tools
  • 1. Welcome to the Course.mp4 (12.4 MB)
  • 1. Welcome to the Course.vtt (2.9 KB)
  • 2. Data & Script For the Course.html (0.1 KB)
  • 2.1 Data and Code.zip.zip (123.3 MB)
  • 3. Python Data Science Environment.mp4 (105.1 MB)
  • 3. Python Data Science Environment.vtt (10.2 KB)
  • 4. For Mac Users.mp4 (50.1 MB)
  • 4. For Mac Users.vtt (3.8 KB)
  • 5. Introduction to IPythonJupyter.mp4 (102.7 MB)
  • 5. Introduction to IPythonJupyter.vtt (17.1 KB)
  • 6. ipython in Browser.mp4 (40.5 MB)
  • 6. ipython in Browser.vtt (3.5 KB)
2. Read in Data From Different Sources With Pandas
  • 1. What are Pandas.mp4 (85.0 MB)
  • 1. What are Pandas.vtt (9.8 KB)
  • 2. Read CSV Data.mp4 (53.9 MB)
  • 2. Read CSV Data.vtt (5.7 KB)
  • 3. Read Excel Data.mp4 (42.4 MB)
  • 3. Read Excel Data.vtt (3.6 KB)
  • 4. Read in HTML Data.mp4 (129.6 MB)
  • 4. Read in HTML Data.vtt (11.1 KB)
3. Data Cleaning
  • 1. Remove NA Values.mp4 (56.0 MB)
  • 1. Remove NA Values.vtt (6.5 KB)
  • 2. Missing Values in a Real Dataset.mp4 (36.9 MB)
  • 2. Missing Values in a Real Dataset.vtt (6.3 KB)
  • 3. Data Imputation.mp4 (56.4 MB)
  • 3. Data Imputation.vtt (9.0 KB)
  • 4. Imputing Qualitative Values.mp4 (21.0 MB)
  • 4. Imputing Qualitative Values.vtt (3.3 KB)
  • 5. Theory Behind k-NN Algorithm.mp4 (96.2 MB)
  • 5. Theory Behind k-NN Algorithm.vtt (6.5 KB)
  • 6. Use k-NN for Data Imputation.mp4 (44.2 MB)
  • 6. Use k-NN for Data Imputation.vtt (6.1 KB)
4. Basic Data Wrangling
  • 1. Basic Principles.mp4 (26.5 MB)
  • 1. Basic Principles.vtt (4.6 KB)
  • 2. Preliminary Data Explorations.mp4 (64.5 MB)
  • 2. Preliminary Data Explorations.vtt (7.8 KB)
  • 3. Basic Data Handling With Conditional Statements.mp4 (49.4 MB)
  • 3. Basic Data Handling With Conditional Statements.vtt (4.1 KB)
  • 4. Drop ColumnRow.mp4 (47.6 MB)
  • 4. Drop ColumnRow.vtt (4.3 KB)
  • 5. Change Column Name.mp4 (25.2 MB)
  • 5. Change Column Name.vtt (3.6 KB)
  • 6. Change the Column Type.mp4 (22.7 MB)
  • 6. Change the Column Type.vtt (3.9 KB)
  • 7. Explore Date Related Data.mp4 (25.1 MB)
  • 7. Explore Date Related Data.vtt (3.6 KB)
  • 8. Simple Date Related Computations.mp4 (25.3 MB)
  • 8. Simple Date Related Computations.vtt (3.8 KB)
5. More Data Wrangling
  • 1. Data Grouping.mp4 (97.9 MB)
  • 1. Data Grouping.vtt (8.3 KB)
  • 2. Data Subsetting and Indexing.mp4 (102.0 MB)
  • 2. Data Subsetting and Indexing.vtt (7.8 KB)
  • 3. More Data Subsetting.mp4 (69.4 MB)
  • 3. More Data Subsetting.vtt (8.0 KB)
  • 4. Extract Information From Strings.mp4 (38.3 MB)
  • 4. Extract Information From Strings.vtt (4.2 KB)
  • 5. (Fuzzy) String Matching.mp4 (18.6 MB)
  • 5. (Fuzzy) String Matching.vtt (2.7 KB)
  • 6. Ranking & Sorting.mp4 (82.3 MB)
  • 6. Ranking & Sorting.vtt (7.4 KB)
  • 7. Concatenate.mp4 (70.1 MB)
  • 7. Concatenate.vtt (7.9 KB)
  • 8. Merging and Joining.mp4 (96.8 MB)
  • 8. Merging and Joining.vtt (10.7 KB)
6. Feature Selection and Transformation
  • 1. Correlation Analysis.mp4 (56.4 MB)
  • 1. Correlation Analysis.vtt (8.6 KB)
  • 2. Using Correlation to Decide Which Features to Retain.mp4 (34.1 MB)
  • 2. Using Correlation to Decide Which Features to Retain.vtt (5.0 KB)
  • 3. Univariate Feature Selection.mp4 (39.2 MB)
  • 3. Univariate Feature Selection.vtt (4.6 KB)
  • 4. Recursive Feature Elimination (RFE).mp4 (36.5 MB)
  • 4. Recursive Feature Elimination (RFE).vtt (4.1 KB)
  • 5. Theory Behind PCA.mp4 (23.9 MB)
  • 5. Theory Behind PCA.vtt (2.9 KB)
  • 6. Implement PCA.mp4 (26.7 MB)
  • 6. Implement PCA.vtt (4.1 KB)
  • 7. Data Standardisation.mp4 (32.5 MB)
  • 7. Data Standardisation.vtt (4.1 KB)
  • 8. Create a New Feature.mp4 (40.0 MB)
  • 8. Create a New Feature.vtt (5.8 KB)
7. Theory Behind Data Visualisation
  • 1. What is Data Visualisation.mp4 (68.3 MB)
  • 1. What is Data Visualisation.vtt (9.9 KB)
  • 2. Some Theoretical Principles Behind Data Visualisation.mp4 (66.1 MB)
  • 2. Some Theoretical Principles Behind Data Visualisation.vtt (7.1 KB)
8. Most Common Data Visualizations
  • 1. Histograms-Visualize the Distribution of Continuous Numerical Variables.mp4 (99.1 MB)
  • 1. Histograms-Visualize the Distribution of Continuous Numerical Variables.vtt (11.7 KB)
  • 2. Boxplots-Visualize the Distribution of Continuous Numerical Variables.mp4 (40.5 MB)
  • 2. Boxplots-Visualize the Distribution of Continuous Numerical Variables.vtt (5.5 KB)
  • 3. Scatter plot-Relationship Between Two Numerical Variables.mp4 (106.8 MB)
  • 3. Scatter plot-Relationship Between Two Numerical Variables.vtt (12.1 KB)
  • 4. Barplot.mp4 (170.7 MB)
  • 4. Barplot.vtt (22.5 KB)
  • 5. Pie Chart.mp4 (37.9 MB)
  • 5. Pie Chart.vtt (5.8 KB)
  • 6. Line Charts.mp4 (116.8 MB)
  • 6. Line Charts.vtt (12.0 KB)
  • 7. More Line Charts.mp4 (18.9 MB)
  • 7. More Line Charts.vtt (2.3 KB)
  • 8. Some More Plot Types.mp4 (76.0 MB)
  • 8. Some More Plot Types.vtt (10.6 KB)
  • 9. And Some More.mp4 (78.5 MB)
  • 9. And Some More.vtt (8.3 KB)
9. Miscallaneous Information
  • 1. Using Colabs as an Online Jupyter Notebook.mp4 (54.8 MB)
  • 1. Using Colabs as an Online Jupyter Notebook.vtt (6.5 KB)

Description

Complete Data Wrangling & Data Visualisation With Python

Learn to Preprocess, Wrangle and Visualise Data For Practical Data Science Applications in Python

For More Courses Visit: https://desirecourse.net

For More Courses Visit: https://courseclub.me



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Udemy - Complete Data Wrangling & Data Visualisation With Python [DC]


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Udemy - Complete Data Wrangling & Data Visualisation With Python [DC]


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