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TORRENT DETAILS

[FTUForum.com] [UDEMY] Complete Data Science Training With Python For Data Analysis [FTU]

TORRENT SUMMARY

	Status:	All the torrents in this section have been verified by our verification system
Category:	Other
Infohash	bdcf2933d59f4d0ed95a0ec7f904f79a4643f4b0
Size:	2.3 GB in 94 files
Release Date(s):	Torrent Release: 18-07-2019 by user
Swarm:	0 Seeds & 1 Peers
Vote:	No votes yet.

DESCRIPTION

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/

Complete Guide to Practical Data Science with Python: Learn Statistics, Visualization, Machine Learning & More

BESTSELLER

Created by : Minerva Singh
Last updated : 1/2019
Language : English
Caption (CC) : Included
Torrent Contains : 253 Files, 14 Folders
Course Source : https://www.udemy.com/complete-data-science-training-with-python-for-data-analysis/

What you'll learn

• Install Anaconda & Work Within The iPytjhon/Jupyter Environment, A Powerful Framework For Data Science Analysis
• Become Proficient In Using The Most Common Python Data Science Packages Including Numpy, Pandas, Scikit & Matplotlib
• Be Able To Read In Data From Different Sources (Including Webpage Data) & Clean The Data
• Carry Out Data Exploratory & Pre-processing Tasks Such As Tabulation, Pivoting & Data Summarizing In Python
• Become Proficient In Working With Real Life Data Collected From Different Sources
• Carry Out Data Visualization & Understand Which Techniques To Apply When
• Carry Out The Most Common Statistical Data Analysis Techniques In Python Including T-Tests & Linear Regression
• Understand The Difference Between Machine Learning & Statistical Data Analysis
• Implement Different Unsupervised Learning Techniques On Real Life Data
• Implement Supervised Learning (Both In The Form Of Classification & Regression) Techniques On Real Data
• Evaluate The Accuracy & Generality Of Machine Learning Models
• Build Basic Neural Networks & Deep Learning Algorithms
• Use The Powerful H2o Framework For Implementing Deep Neural Networks

Course content
all 124 lectures 12:56:56

Requirements

• Be Able To Use PC At A Beginner Level, Including Being Able To Install Programs
• A Desire To Learn Data Science
• Prior Knowledge Of Python Will Be Useful But NOT Necessary

Description

THIS IS A COMPLETE DATA SCIENCE TRAINING WITH PYTHON FOR DATA ANALYSIS :

It's A Full 12-Hour Python Data Science BootCamp To Help You Learn Statistical Modelling, Data Visualization, Machine Learning & Basic Deep Learning In Python!

HERE IS WHY YOU SHOULD TAKE THIS COURSE :

First of all, this course a complete guide to practical data science using Python...

That means, this course covers ALL the aspects of practical data science and if you take this course alone, you can do away with taking other courses or buying books on Python based data science.

In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal. By storing, filtering, managing, and manipulating data in Python, you can give your company a competitive edge & boost your career to the next level!

THIS IS MY PROMISE TO YOU:

COMPLETE THIS ONE COURSE & BECOME A PRO IN PRACTICAL PYTHON BASED DATA SCIENCE!

But, first things first, 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 data science related techniques and producing publications for international peer reviewed journals.

Over the course of my research I realized almost all the Python data science courses and books out there do not account for the multidimensional nature of the topic and use data science interchangeably with machine learning...

This gives student an incomplete knowledge of the subject. This course will give you a robust grounding in all aspects of data science, from statistical modeling to visualization to machine learning.

Unlike other Python instructors, I dig deep into the statistical modeling features of Python and gives you a one-of-a-kind grounding in Python Data Science!

You will go all the way from carrying out simple visualizations and data explorations to statistical analysis to machine learning to finally implementing simple deep learning based models using Python

DISCOVER 12 COMPLETE SECTIONS ADDRESSING EVERY ASPECT OF PYTHON DATA SCIENCE (INCLUDING) :

• A full introduction to Python Data Science and powerful Python driven framework for data science, Anaconda
• Getting started with Jupyter notebooks for implementing data science techniques in Python
• A comprehensive presentation about basic analytical tools- Numpy Arrays, Operations, Arithmetic, Equation-solving, Matrices, Vectors, Broadcasting, etc.
• Data Structures and Reading in Pandas, including CSV, Excel, JSON, HTML data
• How to Pre-Process and “Wrangle” your Python data by removing NAs/No data, handling conditional data, grouping by attributes, etc.
• Creating data visualizations like histograms, boxplots, scatterplots, barplots, pie/line charts, and more!
• Statistical analysis, statistical inference, and the relationships between variables
• Machine Learning, Supervised Learning, Unsupervised Learning in Python
• You’ll even discover how to create artificial neural networks and deep learning structures...& MUCH MORE!

With this course, you’ll have the keys to the entire Python Data Science kingdom!

NO PRIOR PYTHON OR STATISTICS/MACHINE LEARNING KNOWLEDGE IS REQUIRED :

You’ll start by absorbing the most valuable Python Data Science basics and techniques...

I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in Python.

My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement Python based data science in real life.

After taking this course, you’ll easily use packages like Numpy, Pandas, and Matplotlib to work with real data in Python.

You’ll even understand deep concepts like statistical modeling in Python’s Statsmodels package and the difference between statistics and machine learning (including hands-on techniques).

I will even introduce you to deep learning and neural networks using the powerful H2o framework!

With this Powerful All-In-One Python Data Science course, you’ll know it all: visualization, stats, machine learning, data mining, and deep learning!

The underlying motivation for the course is to ensure you can apply Python based data science on real data and put into practice today. Start analyzing data for your own projects, whatever your skill level and IMPRESS your potential employers with actual examples of your data science abilities.

HERE IS WHAT THIS COURSE WILL DO FOR YOU :

This course is your one shot way of acquiring the knowledge of statistical data analysis skills that I acquired from the rigorous training received at two of the best universities in the world, perusal of numerous books and publishing statistically rich papers in renowned international journal like PLOS One.

This course will:

(a) Take students without a prior Python and/or statistics background background from a basic level to performing some of the most common advanced data science techniques using the powerful Python based Jupyter notebooks.

(b) Equip students to use Python for performing different statistical data analysis and visualization tasks for data modelling.

(c) Introduce some of the most important statistical and machine learning concepts to students in a practical manner such that students can apply these concepts for practical data analysis and interpretation.

(d) Students will get a strong background in some of the most important data science techniques.

(e) Students will be able to decide which data science techniques are best suited to answer their research questions and applicable to their data and interpret the results.

It is a practical, hands-on course, i.e. we will spend some time dealing with some of the theoretical concepts related to data science. However, majority of the course will focus on implementing different techniques on real data and interpret the results. After each video you will learn a new concept or technique which you may apply to your own projects.

JOIN THE COURSE NOW!

Who this course is for :

• Anyone Who Wishes To Learn Practical Data Science Using Python
• Anyone Interested In Learning How To Implement Machine Learning Algorithms Using Python
• People Looking To Get Started In Deep Learning Using Python
• People Looking To Work With Real Life Data In Python
• Anyone With A Prior Knowledge Of Python Looking To Branch Out Into Data Analysis
• Anyone Looking To Become Proficient In Exploratory Data Analysis, Statistical Modelling & Visualizations Using iPython.

VISITOR COMMENTS (0

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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 Data Science in Python Bootcamp/1. What is Data Science.mp4	17.4 MB
	1. Introduction to the Data Science in Python Bootcamp/1. What is Data Science.vtt	4 KB
	1. Introduction to the Data Science in Python Bootcamp/2. Introduction to the Course Instructor.m4v	55.6 MB
	1. Introduction to the Data Science in Python Bootcamp/2. Introduction to the Course Instructor.vtt	13.5 KB
	1. Introduction to the Data Science in Python Bootcamp/3. Data For the Course.html	98 B
	1. Introduction to the Data Science in Python Bootcamp/3.1 scriptsLecture.zip.zip	308 MB
	1. Introduction to the Data Science in Python Bootcamp/4. Introduction to the Python Data Science Tool.mp4	25 MB
	1. Introduction to the Data Science in Python Bootcamp/4. Introduction to the Python Data Science Tool.vtt	10.1 KB
	1. Introduction to the Data Science in Python Bootcamp/5. For Mac Users.mp4	10.2 MB
	1. Introduction to the Data Science in Python Bootcamp/5. For Mac Users.vtt	3.9 KB
	1. Introduction to the Data Science in Python Bootcamp/6. Introduction to the Python Data Science Environment.mp4	40.3 MB
	1. Introduction to the Data Science in Python Bootcamp/6. Introduction to the Python Data Science Environment.vtt	17.2 KB
	1. Introduction to the Data Science in Python Bootcamp/7. Some Miscellaneous IPython Usage Facts.mp4	12 MB
	1. Introduction to the Data Science in Python Bootcamp/7. Some Miscellaneous IPython Usage Facts.vtt	4.5 KB
	1. Introduction to the Data Science in Python Bootcamp/8. Online iPython Interpreter.mp4	7.7 MB
	1. Introduction to the Data Science in Python Bootcamp/8. Online iPython Interpreter.vtt	3.4 KB
	1. Introduction to the Data Science in Python Bootcamp/9. Conclusion to Section 1.mp4	6.5 MB
	1. Introduction to the Data Science in Python Bootcamp/9. Conclusion to Section 1.vtt	3.1 KB
	10. Unsupervised Learning in Python/1. Unsupervised Classification- Some Basic Ideas.mp4	6.2 MB
	10. Unsupervised Learning in Python/1. Unsupervised Classification- Some Basic Ideas.vtt	1.8 KB
	10. Unsupervised Learning in Python/10. Principal Component Analysis (PCA)-Practical Implementation.mp4	9.1 MB
	10. Unsupervised Learning in Python/10. Principal Component Analysis (PCA)-Practical Implementation.vtt	4.2 KB
	10. Unsupervised Learning in Python/11. Conclusions to Section 10.mp4	5.5 MB
	10. Unsupervised Learning in Python/11. Conclusions to Section 10.vtt	2.5 KB
	10. Unsupervised Learning in Python/2. KMeans-theory.mp4	5.1 MB
	10. Unsupervised Learning in Python/2. KMeans-theory.vtt	2.5 KB
	10. Unsupervised Learning in Python/3. KMeans-implementation on the iris data.mp4	19.5 MB
	10. Unsupervised Learning in Python/3. KMeans-implementation on the iris data.vtt	7.6 KB
	10. Unsupervised Learning in Python/4. Quantifying KMeans Clustering Performance.mp4	9.6 MB
	10. Unsupervised Learning in Python/4. Quantifying KMeans Clustering Performance.vtt	4.4 KB
	10. Unsupervised Learning in Python/5. KMeans Clustering with Real Data.mp4	12.1 MB
	10. Unsupervised Learning in Python/5. KMeans Clustering with Real Data.vtt	4.5 KB
	10. Unsupervised Learning in Python/6. How Do We Select the Number of Clusters.mp4	19 MB
	10. Unsupervised Learning in Python/6. How Do We Select the Number of Clusters.vtt	4.2 KB
	10. Unsupervised Learning in Python/7. Hierarchical Clustering-theory.mp4	10.2 MB
	10. Unsupervised Learning in Python/7. Hierarchical Clustering-theory.vtt	5 KB
	10. Unsupervised Learning in Python/8. Hierarchical Clustering-practical.mp4	29.4 MB
	10. Unsupervised Learning in Python/8. Hierarchical Clustering-practical.vtt	9.5 KB
	10. Unsupervised Learning in Python/9. Principal Component Analysis (PCA)-Theory.mp4	5.9 MB
	10. Unsupervised Learning in Python/9. Principal Component Analysis (PCA)-Theory.vtt	3 KB
	11. Supervised Learning/1. What is This Section About.mp4	24.9 MB
	11. Supervised Learning/1. What is This Section About.vtt	11.5 KB
	11. Supervised Learning/10. knn-Classification.mp4	18.2 MB
	11. Supervised Learning/10. knn-Classification.vtt	8 KB
	11. Supervised Learning/11. knn-Regression.mp4	8.4 MB
	11. Supervised Learning/11. knn-Regression.vtt	4 KB
	11. Supervised Learning/12. Gradient Boosting-classification.mp4	15 MB
	11. Supervised Learning/12. Gradient Boosting-classification.vtt	6 KB
	11. Supervised Learning/13. Gradient Boosting-regression.mp4	10.9 MB
	11. Supervised Learning/13. Gradient Boosting-regression.vtt	3.7 KB
	11. Supervised Learning/14. Voting Classifier.mp4	9.5 MB
	11. Supervised Learning/14. Voting Classifier.vtt	3.8 KB
	11. Supervised Learning/15. Conclusions to Section 11.mp4	7.2 MB
	11. Supervised Learning/15. Conclusions to Section 11.vtt	2.9 KB
	11. Supervised Learning/16. Section 11 Quiz.html	163 B
	11. Supervised Learning/2. Data Preparation for Supervised Learning.mp4	28.3 MB
	11. Supervised Learning/2. Data Preparation for Supervised Learning.vtt	10.1 KB
	11. Supervised Learning/3. Pointers on Evaluating the Accuracy of Classification and Regression Modelling.mp4	24 MB
	11. Supervised Learning/3. Pointers on Evaluating the Accuracy of Classification and Regression Modelling.vtt	10.5 KB
	11. Supervised Learning/4. Using Logistic Regression as a Classification Model.mp4	20.6 MB
	11. Supervised Learning/4. Using Logistic Regression as a Classification Model.vtt	8.7 KB
	11. Supervised Learning/5. RF-Classification.mp4	28.5 MB
	11. Supervised Learning/5. RF-Classification.vtt	12.2 KB
	11. Supervised Learning/6. RF-Regression.mp4	23.6 MB
	11. Supervised Learning/6. RF-Regression.vtt	9.7 KB
	11. Supervised Learning/7. SVM- Linear Classification.mp4	7.4 MB
	11. Supervised Learning/7. SVM- Linear Classification.vtt	3.2 KB
	11. Supervised Learning/8. SVM- Non Linear Classification.mp4	5.1 MB
	11. Supervised Learning/8. SVM- Non Linear Classification.vtt	2.3 KB
	11. Supervised Learning/9. Support Vector Regression.mp4	10.2 MB
	11. Supervised Learning/9. Support Vector Regression.vtt	4.3 KB
	12. Artificial Neural Networks (ANN) and Deep Learning (DL)/1. Theory Behind ANN and DNN.mp4	22.6 MB
	12. Artificial Neural Networks (ANN) and Deep Learning (DL)/1. Theory Behind ANN and DNN.vtt	9.9 KB
	12. Artificial Neural Networks (ANN) and Deep Learning (DL)/10. Specify the Activation Function.mp4	6.2 MB
	12. Artificial Neural Networks (ANN) and Deep Learning (DL)/10. Specify the Activation Function.vtt	2.2 KB
	12. Artificial Neural Networks (ANN) and Deep Learning (DL)/11. H2O Deep Learning For Predictions.mp4	12 MB
	12. Artificial Neural Networks (ANN) and Deep Learning (DL)/11. H2O Deep Learning For Predictions.vtt	5.2 KB
	12. Artificial Neural Networks (ANN) and Deep Learning (DL)/12. Conclusions to Section 12.mp4	5.2 MB
	12. Artificial Neural Networks (ANN) and Deep Learning (DL)/12. Conclusions to Section 12.vtt	2.1 KB
	12. Artificial Neural Networks (ANN) and Deep Learning (DL)/13. Section 12 Quiz.html	163 B
	12. Artificial Neural Networks (ANN) and Deep Learning (DL)/2. Perceptrons for Binary Classification.mp4	10 MB
	12. Artificial Neural Networks (ANN) and Deep Learning (DL)/2. Perceptrons for Binary Classification.vtt	4.7 KB
	12. Artificial Neural Networks (ANN) and Deep Learning (DL)/3. Getting Started with ANN-binary classification.mp4	8.5 MB
	12. Artificial Neural Networks (ANN) and Deep Learning (DL)/3. Getting Started with ANN-binary classification.vtt	3.5 KB
	12. Artificial Neural Networks (ANN) and Deep Learning (DL)/4. Multi-label classification with MLP.mp4	13.5 MB
	12. Artificial Neural Networks (ANN) and Deep Learning (DL)/4. Multi-label classification with MLP.vtt	4.8 KB
	12. Artificial Neural Networks (ANN) and Deep Learning (DL)/5. Regression with MLP.mp4	9 MB
	12. Artificial Neural Networks (ANN) and Deep Learning (DL)/5. Regression with MLP.vtt	3.5 KB
	12. Artificial Neural Networks (ANN) and Deep Learning (DL)/6. MLP with PCA on a Large Dataset.mp4	19.2 MB
	12. Artificial Neural Networks (ANN) and Deep Learning (DL)/6. MLP with PCA on a Large Dataset.vtt	7.6 KB
	12. Artificial Neural Networks (ANN) and Deep Learning (DL)/7. Start With Deep Neural Network (DNN).html	229 B
	12. Artificial Neural Networks (ANN) and Deep Learning (DL)/8. Start with H20.mp4	12.1 MB
	12. Artificial Neural Networks (ANN) and Deep Learning (DL)/8. Start with H20.vtt	4.3 KB

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