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[FTUForum.com] [UDEMY] Complete Data Wrangling & Data Visualisation With Python [FTU]
TORRENT SUMMARY
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Learn to Preprocess, Wrangle and Visualise Data For Practical Data Science Applications in Python
BESTSELLER
Created by : Minerva Singh Last updated : 4/2019 Language : English Caption (CC) : Included Torrent Contains : 110 Files, 10 Folders Course Source : https://www.udemy.com/complete-data-wrangling-data-visualisation-with-python/
What you'll learn
• Install and Get Started With the Python Data Science Environment- Jupyter/iPython • Read In Data Into The Jupiter/iPython Environment From Different Sources • Carry Out Basic Data Pre-processing & Wrangling In the Jupyter Environment • Learn to IDENTIFY Which Visualisations Should be Used in ANY given Situation • Go From A Basic Level To Performing Some Of The MOST COMMON Data Preprocessing, Data Wrangling & Data Visualization Tasks In Jupyter • How To Use Some Of The MOST IMPORTANT R Data Wrangling & Visualisation Packages Such As Matplotlib • Build POWERFUL Visualisations and Graphs from REAL DATA • Apply Data Visualization Concepts For PRACTICAL Data Analysis & Interpretation • Gain PROFICIENCY In Data Preprocessing, Data Wrangling & Data Visualisation In Jupyter By Putting Your Soon-To-Be-Acquired Knowledge Into IMMEDIATE Application
Requirements
• The Ability To Install the Anaconda Environment On Your Computer/Laptop • Know how to install and load packages in Anaconda • Interest in Learning to Process and Visualise Real Data
Description
Hello, My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation).
I have several years of experience in analyzing real life data from different sources using statistical modeling and producing publications for international peer reviewed journals. If you find statistics books & manuals too vague, expensive & not practical, then you’re going to love this course!
I created this course to take you by hand and teach you all the concepts, and tackle the most fundamental building block on practical data science- data wrangling and visualisation.
GET ACCESS TO A COURSE THAT IS JAM PACKED WITH TONS OF APPLICABLE INFORMATION!
This course is your sure-fire way of acquiring the knowledge and statistical data analysis wrangling and visualisation skills that I acquired from the rigorous training I received at 2 of the best universities in the world, perusal of numerous books and publishing statistically rich papers in renowned international journal like PLOS One.
To be more specific, here’s what the course will do for you:
(a) It will take you (even if you have no prior statistical modelling/analysis background) from a basic level to performing some of the most common data wrangling tasks in Python.
(b) It will equip you to use some of the most important Python data wrangling and visualisation packages such as seaborn.
(c) It will Introduce some of the most important data visualisation concepts to you in a practical manner such that you can apply these concepts for practical data analysis and interpretation.
(d) You will also be able to decide which wrangling and visualisation techniques are best suited to answer your research questions and applicable to your data and interpret the results.
The course will mostly focus on helping you implement different techniques on real-life data such as Olympic and Nobel Prize winners
After each video you will learn a new concept or technique which you may apply to your own projects immediately! Reinforce your knowledge through practical quizzes and assignments.
TAKE ACTION NOW :) You’ll also have my continuous support when you take this course just to make sure you’re successful with it. If my GUARANTEE is not enough for you, you can ask for a refund within 30 days of your purchase in case you’re not completely satisfied with the course.
TAKE ACTION TODAY! I will personally support you and ensure your experience with this course is a success.
Who this course is for :
• Students Interested In Getting Started With Data Science Applications In The Jupyter Environment • Students Interested in Learning About the Common Pre-processing Data Tasks • Students Interested in Gaining Exposure to Common Python Packages Such As pandas • Those Interested in Learning About Different Kinds of Data Visualisations • Those Interested in Learning to Create Publication Quality Visualisations.
For More Udemy Free Courses >>> https://ftuforum.com/ For more Lynda and other Courses >>> https://www.freecoursesonline.me/ Our Forum for discussion >>> https://discuss.ftuforum.com/
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FILE LIST
Filename
Size
0. Websites you may like/How you can help Team-FTU.txt
237 B
1. INTRODUCTION TO THE COURSE The Key Concepts and Software Tools/1. Welcome to the Course.mp4
12.4 MB
1. INTRODUCTION TO THE COURSE The Key Concepts and Software Tools/1. Welcome to the Course.vtt
2.9 KB
1. INTRODUCTION TO THE COURSE The Key Concepts and Software Tools/2. Data & Script For the Course.html
123 B
1. INTRODUCTION TO THE COURSE The Key Concepts and Software Tools/2.1 Data and Code.zip.zip
123.3 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/3. Python Data Science Environment.vtt
10.2 KB
1. INTRODUCTION TO THE COURSE The Key Concepts and Software Tools/4. For Mac Users.mp4
50.1 MB
1. INTRODUCTION TO THE COURSE The Key Concepts and Software Tools/4. For Mac Users.vtt
3.8 KB
1. INTRODUCTION TO THE COURSE The Key Concepts and Software Tools/5. Introduction to IPythonJupyter.mp4
102.7 MB
1. INTRODUCTION TO THE COURSE The Key Concepts and Software Tools/5. Introduction to IPythonJupyter.vtt
17.1 KB
1. INTRODUCTION TO THE COURSE The Key Concepts and Software Tools/6. ipython in Browser.mp4
40.5 MB
1. INTRODUCTION TO THE COURSE The Key Concepts and Software Tools/6. ipython in Browser.vtt
3.5 KB
2. Read in Data From Different Sources With Pandas/1. What are Pandas.mp4
85 MB
2. Read in Data From Different Sources With Pandas/1. What are Pandas.vtt
9.8 KB
2. Read in Data From Different Sources With Pandas/2. Read CSV Data.mp4
53.9 MB
2. Read in Data From Different Sources With Pandas/2. Read CSV Data.vtt
5.7 KB
2. Read in Data From Different Sources With Pandas/3. Read Excel Data.mp4
42.4 MB
2. Read in Data From Different Sources With Pandas/3. Read Excel Data.vtt
3.6 KB
2. Read in Data From Different Sources With Pandas/4. Read in HTML Data.mp4
129.6 MB
2. Read in Data From Different Sources With Pandas/4. Read in HTML Data.vtt
11.1 KB
3. Data Cleaning/1. Remove NA Values.mp4
56 MB
3. Data Cleaning/1. Remove NA Values.vtt
6.5 KB
3. Data Cleaning/2. Missing Values in a Real Dataset.mp4
36.9 MB
3. Data Cleaning/2. Missing Values in a Real Dataset.vtt
6.3 KB
3. Data Cleaning/3. Data Imputation.mp4
56.4 MB
3. Data Cleaning/3. Data Imputation.vtt
9 KB
3. Data Cleaning/4. Imputing Qualitative Values.mp4
21 MB
3. Data Cleaning/4. Imputing Qualitative Values.vtt
3.3 KB
3. Data Cleaning/5. Theory Behind k-NN Algorithm.mp4
96.2 MB
3. Data Cleaning/5. Theory Behind k-NN Algorithm.vtt
6.5 KB
3. Data Cleaning/6. Use k-NN for Data Imputation.mp4
44.2 MB
3. Data Cleaning/6. Use k-NN for Data Imputation.vtt
6.1 KB
4. Basic Data Wrangling/1. Basic Principles.mp4
26.5 MB
4. Basic Data Wrangling/1. Basic Principles.vtt
4.6 KB
4. Basic Data Wrangling/2. Preliminary Data Explorations.mp4
64.5 MB
4. Basic Data Wrangling/2. Preliminary Data Explorations.vtt
7.8 KB
4. Basic Data Wrangling/3. Basic Data Handling With Conditional Statements.mp4
49.4 MB
4. Basic Data Wrangling/3. Basic Data Handling With Conditional Statements.vtt
4.1 KB
4. Basic Data Wrangling/4. Drop ColumnRow.mp4
47.6 MB
4. Basic Data Wrangling/4. Drop ColumnRow.vtt
4.3 KB
4. Basic Data Wrangling/5. Change Column Name.mp4
25.2 MB
4. Basic Data Wrangling/5. Change Column Name.vtt
3.6 KB
4. Basic Data Wrangling/6. Change the Column Type.mp4
22.7 MB
4. Basic Data Wrangling/6. Change the Column Type.vtt
3.9 KB
4. Basic Data Wrangling/7. Explore Date Related Data.mp4
25.1 MB
4. Basic Data Wrangling/7. Explore Date Related Data.vtt
3.6 KB
4. Basic Data Wrangling/8. Simple Date Related Computations.mp4
25.3 MB
4. Basic Data Wrangling/8. Simple Date Related Computations.vtt
3.8 KB
5. More Data Wrangling/1. Data Grouping.mp4
97.9 MB
5. More Data Wrangling/1. Data Grouping.vtt
8.3 KB
5. More Data Wrangling/2. Data Subsetting and Indexing.mp4
102 MB
5. More Data Wrangling/2. Data Subsetting and Indexing.vtt
7.8 KB
5. More Data Wrangling/3. More Data Subsetting.mp4
69.4 MB
5. More Data Wrangling/3. More Data Subsetting.vtt
8 KB
5. More Data Wrangling/4. Extract Information From Strings.mp4
38.3 MB
5. More Data Wrangling/4. Extract Information From Strings.vtt
4.2 KB
5. More Data Wrangling/5. (Fuzzy) String Matching.mp4
18.6 MB
5. More Data Wrangling/5. (Fuzzy) String Matching.vtt
2.7 KB
5. More Data Wrangling/6. Ranking & Sorting.mp4
82.3 MB
5. More Data Wrangling/6. Ranking & Sorting.vtt
7.4 KB
5. More Data Wrangling/7. Concatenate.mp4
70.1 MB
5. More Data Wrangling/7. Concatenate.vtt
7.9 KB
5. More Data Wrangling/8. Merging and Joining.mp4
96.8 MB
5. More Data Wrangling/8. Merging and Joining.vtt
10.7 KB
6. Feature Selection and Transformation/1. Correlation Analysis.mp4
56.4 MB
6. Feature Selection and Transformation/1. Correlation Analysis.vtt
8.6 KB
6. Feature Selection and Transformation/2. Using Correlation to Decide Which Features to Retain.mp4
34.1 MB
6. Feature Selection and Transformation/2. Using Correlation to Decide Which Features to Retain.vtt
5 KB
6. Feature Selection and Transformation/3. Univariate Feature Selection.mp4
39.2 MB
6. Feature Selection and Transformation/3. Univariate Feature Selection.vtt
4.6 KB
6. Feature Selection and Transformation/4. Recursive Feature Elimination (RFE).mp4
36.5 MB
6. Feature Selection and Transformation/4. Recursive Feature Elimination (RFE).vtt
4.1 KB
6. Feature Selection and Transformation/5. Theory Behind PCA.mp4
23.9 MB
6. Feature Selection and Transformation/5. Theory Behind PCA.vtt
2.9 KB
6. Feature Selection and Transformation/6. Implement PCA.mp4
26.7 MB
6. Feature Selection and Transformation/6. Implement PCA.vtt
4.1 KB
6. Feature Selection and Transformation/7. Data Standardisation.mp4
32.5 MB
6. Feature Selection and Transformation/7. Data Standardisation.vtt
4.1 KB
6. Feature Selection and Transformation/8. Create a New Feature.mp4
40 MB
6. Feature Selection and Transformation/8. Create a New Feature.vtt
5.8 KB
7. Theory Behind Data Visualisation/1. What is Data Visualisation.mp4
68.3 MB
7. Theory Behind Data Visualisation/1. What is Data Visualisation.vtt
9.9 KB
7. Theory Behind Data Visualisation/2. Some Theoretical Principles Behind Data Visualisation.mp4
66.1 MB
7. Theory Behind Data Visualisation/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
8. Most Common Data Visualizations/1. Histograms-Visualize the Distribution of Continuous Numerical Variables.vtt
11.7 KB
8. Most Common Data Visualizations/2. Boxplots-Visualize the Distribution of Continuous Numerical Variables.mp4
40.5 MB
8. Most Common Data Visualizations/2. Boxplots-Visualize the Distribution of Continuous Numerical Variables.vtt
5.5 KB
8. Most Common Data Visualizations/3. Scatter plot-Relationship Between Two Numerical Variables.mp4
106.8 MB
8. Most Common Data Visualizations/3. Scatter plot-Relationship Between Two Numerical Variables.vtt
12.1 KB
8. Most Common Data Visualizations/4. Barplot.mp4
170.7 MB
8. Most Common Data Visualizations/4. Barplot.vtt
22.5 KB
8. Most Common Data Visualizations/5. Pie Chart.mp4
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