[FTUForum.com] [UDEMY] Complete Data Wrangling & Data Visualisation With Python [FTU]

Creation Time	Jan. 30, 2026, 3:28 p.m.
Last Access Time	Feb. 1, 2026, 1:10 a.m.
File Size	3.1 GB
Keywords	FTU Complete Wrangling com FTUForum With UDEMY Python Visualisation Data
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Total Files	110
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8. Most Common Data Visualizations/4. Barplot.mp4 170.7 MB
2. Read in Data From Different Sources With Pandas/4. Read in HTML Data.mp4 129.6 MB
1. INTRODUCTION TO THE COURSE The Key Concepts and Software Tools/2.1 Data and Code.zip.zip 123.3 MB
8. Most Common Data Visualizations/6. Line Charts.mp4 116.8 MB
8. Most Common Data Visualizations/3. Scatter plot-Relationship Between Two Numerical Variables.mp4 106.8 MB
1. INTRODUCTION TO THE COURSE The Key Concepts and Software Tools/3. Python Data Science Environment.mp4 105.1 MB
1. INTRODUCTION TO THE COURSE The Key Concepts and Software Tools/5. Introduction to IPythonJupyter.mp4 102.7 MB
5. More Data Wrangling/2. Data Subsetting and Indexing.mp4 102.0 MB
8. Most Common Data Visualizations/1. Histograms-Visualize the Distribution of Continuous Numerical Variables.mp4 99.1 MB
5. More Data Wrangling/1. Data Grouping.mp4 97.9 MB
5. More Data Wrangling/8. Merging and Joining.mp4 96.8 MB
3. Data Cleaning/5. Theory Behind k-NN Algorithm.mp4 96.2 MB
2. Read in Data From Different Sources With Pandas/1. What are Pandas.mp4 85.0 MB
5. More Data Wrangling/6. Ranking & Sorting.mp4 82.3 MB
8. Most Common Data Visualizations/9. And Some More.mp4 78.5 MB
8. Most Common Data Visualizations/8. Some More Plot Types.mp4 76.0 MB
5. More Data Wrangling/7. Concatenate.mp4 70.1 MB
5. More Data Wrangling/3. More Data Subsetting.mp4 69.4 MB
7. Theory Behind Data Visualisation/1. What is Data Visualisation.mp4 68.3 MB
7. Theory Behind Data Visualisation/2. Some Theoretical Principles Behind Data Visualisation.mp4 66.1 MB
4. Basic Data Wrangling/2. Preliminary Data Explorations.mp4 64.5 MB
6. Feature Selection and Transformation/1. Correlation Analysis.mp4 56.4 MB
3. Data Cleaning/3. Data Imputation.mp4 56.4 MB
3. Data Cleaning/1. Remove NA Values.mp4 56.0 MB
9. Miscallaneous Information/1. Using Colabs as an Online Jupyter Notebook.mp4 54.8 MB
2. Read in Data From Different Sources With Pandas/2. Read CSV Data.mp4 53.9 MB
1. INTRODUCTION TO THE COURSE The Key Concepts and Software Tools/4. For Mac Users.mp4 50.1 MB
4. Basic Data Wrangling/3. Basic Data Handling With Conditional Statements.mp4 49.4 MB
4. Basic Data Wrangling/4. Drop ColumnRow.mp4 47.6 MB
3. Data Cleaning/6. Use k-NN for Data Imputation.mp4 44.2 MB
2. Read in Data From Different Sources With Pandas/3. Read Excel Data.mp4 42.4 MB
8. Most Common Data Visualizations/2. Boxplots-Visualize the Distribution of Continuous Numerical Variables.mp4 40.5 MB
1. INTRODUCTION TO THE COURSE The Key Concepts and Software Tools/6. ipython in Browser.mp4 40.5 MB
6. Feature Selection and Transformation/8. Create a New Feature.mp4 40.0 MB
6. Feature Selection and Transformation/3. Univariate Feature Selection.mp4 39.2 MB
5. More Data Wrangling/4. Extract Information From Strings.mp4 38.3 MB
8. Most Common Data Visualizations/5. Pie Chart.mp4 37.9 MB
3. Data Cleaning/2. Missing Values in a Real Dataset.mp4 36.9 MB
6. Feature Selection and Transformation/4. Recursive Feature Elimination (RFE).mp4 36.5 MB
6. Feature Selection and Transformation/2. Using Correlation to Decide Which Features to Retain.mp4 34.1 MB
6. Feature Selection and Transformation/7. Data Standardisation.mp4 32.5 MB
6. Feature Selection and Transformation/6. Implement PCA.mp4 26.7 MB
4. Basic Data Wrangling/1. Basic Principles.mp4 26.5 MB
4. Basic Data Wrangling/8. Simple Date Related Computations.mp4 25.3 MB
4. Basic Data Wrangling/5. Change Column Name.mp4 25.2 MB
4. Basic Data Wrangling/7. Explore Date Related Data.mp4 25.1 MB
6. Feature Selection and Transformation/5. Theory Behind PCA.mp4 23.9 MB
4. Basic Data Wrangling/6. Change the Column Type.mp4 22.7 MB
3. Data Cleaning/4. Imputing Qualitative Values.mp4 21.0 MB
8. Most Common Data Visualizations/7. More Line Charts.mp4 18.9 MB
5. More Data Wrangling/5. (Fuzzy) String Matching.mp4 18.6 MB
1. INTRODUCTION TO THE COURSE The Key Concepts and Software Tools/1. Welcome to the Course.mp4 12.4 MB
8. Most Common Data Visualizations/4. Barplot.vtt 22.5 KB
1. INTRODUCTION TO THE COURSE The Key Concepts and Software Tools/5. Introduction to IPythonJupyter.vtt 17.1 KB
8. Most Common Data Visualizations/3. Scatter plot-Relationship Between Two Numerical Variables.vtt 12.1 KB
8. Most Common Data Visualizations/6. Line Charts.vtt 12.0 KB
8. Most Common Data Visualizations/1. Histograms-Visualize the Distribution of Continuous Numerical Variables.vtt 11.7 KB
2. Read in Data From Different Sources With Pandas/4. Read in HTML Data.vtt 11.1 KB
5. More Data Wrangling/8. Merging and Joining.vtt 10.7 KB
8. Most Common Data Visualizations/8. Some More Plot Types.vtt 10.6 KB
1. INTRODUCTION TO THE COURSE The Key Concepts and Software Tools/3. Python Data Science Environment.vtt 10.2 KB
7. Theory Behind Data Visualisation/1. What is Data Visualisation.vtt 9.9 KB
2. Read in Data From Different Sources With Pandas/1. What are Pandas.vtt 9.8 KB
3. Data Cleaning/3. Data Imputation.vtt 9.0 KB
6. Feature Selection and Transformation/1. Correlation Analysis.vtt 8.6 KB
5. More Data Wrangling/1. Data Grouping.vtt 8.3 KB
8. Most Common Data Visualizations/9. And Some More.vtt 8.3 KB
5. More Data Wrangling/3. More Data Subsetting.vtt 8.0 KB
5. More Data Wrangling/7. Concatenate.vtt 7.9 KB
4. Basic Data Wrangling/2. Preliminary Data Explorations.vtt 7.8 KB
5. More Data Wrangling/2. Data Subsetting and Indexing.vtt 7.8 KB
5. More Data Wrangling/6. Ranking & Sorting.vtt 7.4 KB
7. Theory Behind Data Visualisation/2. Some Theoretical Principles Behind Data Visualisation.vtt 7.1 KB
3. Data Cleaning/5. Theory Behind k-NN Algorithm.vtt 6.5 KB
9. Miscallaneous Information/1. Using Colabs as an Online Jupyter Notebook.vtt 6.5 KB
3. Data Cleaning/1. Remove NA Values.vtt 6.5 KB
3. Data Cleaning/2. Missing Values in a Real Dataset.vtt 6.3 KB
3. Data Cleaning/6. Use k-NN for Data Imputation.vtt 6.1 KB
8. Most Common Data Visualizations/5. Pie Chart.vtt 5.8 KB
6. Feature Selection and Transformation/8. Create a New Feature.vtt 5.8 KB
2. Read in Data From Different Sources With Pandas/2. Read CSV Data.vtt 5.7 KB
8. Most Common Data Visualizations/2. Boxplots-Visualize the Distribution of Continuous Numerical Variables.vtt 5.5 KB
6. Feature Selection and Transformation/2. Using Correlation to Decide Which Features to Retain.vtt 5.0 KB
4. Basic Data Wrangling/1. Basic Principles.vtt 4.6 KB
6. Feature Selection and Transformation/3. Univariate Feature Selection.vtt 4.6 KB
4. Basic Data Wrangling/4. Drop ColumnRow.vtt 4.3 KB
5. More Data Wrangling/4. Extract Information From Strings.vtt 4.2 KB
6. Feature Selection and Transformation/7. Data Standardisation.vtt 4.1 KB
4. Basic Data Wrangling/3. Basic Data Handling With Conditional Statements.vtt 4.1 KB
6. Feature Selection and Transformation/6. Implement PCA.vtt 4.1 KB
6. Feature Selection and Transformation/4. Recursive Feature Elimination (RFE).vtt 4.1 KB
4. Basic Data Wrangling/6. Change the Column Type.vtt 3.9 KB
1. INTRODUCTION TO THE COURSE The Key Concepts and Software Tools/4. For Mac Users.vtt 3.8 KB
4. Basic Data Wrangling/8. Simple Date Related Computations.vtt 3.8 KB
4. Basic Data Wrangling/7. Explore Date Related Data.vtt 3.6 KB
4. Basic Data Wrangling/5. Change Column Name.vtt 3.6 KB
2. Read in Data From Different Sources With Pandas/3. Read Excel Data.vtt 3.6 KB
1. INTRODUCTION TO THE COURSE The Key Concepts and Software Tools/6. ipython in Browser.vtt 3.5 KB
3. Data Cleaning/4. Imputing Qualitative Values.vtt 3.3 KB
6. Feature Selection and Transformation/5. Theory Behind PCA.vtt 2.9 KB
1. INTRODUCTION TO THE COURSE The Key Concepts and Software Tools/1. Welcome to the Course.vtt 2.9 KB
5. More Data Wrangling/5. (Fuzzy) String Matching.vtt 2.7 KB
8. Most Common Data Visualizations/7. More Line Charts.vtt 2.3 KB
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1. INTRODUCTION TO THE COURSE The Key Concepts and Software Tools/2. Data & Script For the Course.html 123 bytes

[FTUForum.com] [UDEMY] Complete Data Wrangling & Data Visualisation With Python [FTU]

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