Best Data Mining Courses

Find the best online Data Mining Courses for you. The courses are sorted based on popularity and user ratings. We do not allow paid placements in any of our rankings. We also have a separate page listing only the Free Data Mining Courses.

Data Science A-Z™: Real-Life Data Science Exercises Included

Learn Data Science step by step through real Analytics examples. Data Mining, Modeling, Tableau Visualization and more!

Created by Kirill Eremenko - Data Scientist

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Students: 191699, Price: $89.99

Students: 191699, Price:  Paid

Extremely Hands-On... Incredibly Practical... Unbelievably Real!

This is not one of those fluffy classes where everything works out just the way it should and your training is smooth sailing. This course throws you into the deep end.

In this course you WILL experience firsthand all of the PAIN a Data Scientist goes through on a daily basis. Corrupt data, anomalies, irregularities - you name it!

This course will give you a full overview of the Data Science journey. Upon completing this course you will know:

  • How to clean and prepare your data for analysis
  • How to perform basic visualisation of your data
  • How to model your data
  • How to curve-fit your data
  • And finally, how to present your findings and wow the audience

This course will give you so much practical exercises that real world will seem like a piece of cake when you graduate this class. This course has homework exercises that are so thought provoking and challenging that you will want to cry... But you won't give up! You will crush it. In this course you will develop a good understanding of the following tools:

  • SQL
  • SSIS
  • Tableau
  • Gretl

This course has pre-planned pathways. Using these pathways you can navigate the course and combine sections into YOUR OWN journey that will get you the skills that YOU need.

Or you can do the whole course and set yourself up for an incredible career in Data Science.

The choice is yours. Join the class and start learning today!

See you inside,

Sincerely,

Kirill Eremenko

Python A-Z™: Python For Data Science With Real Exercises!

Programming In Python For Data Analytics And Data Science. Learn Statistical Analysis, Data Mining And Visualization

Created by Kirill Eremenko - Data Scientist

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Students: 130580, Price: $99.99

Students: 130580, Price:  Paid

Learn Python Programming by doing!

There are lots of Python courses and lectures out there. However, Python has a very steep learning curve and students often get overwhelmed. This course is different!

This course is truly step-by-step. In every new tutorial we build on what had already learned and move one extra step forward.

After every video you learn a new valuable concept that you can apply right away. And the best part is that you learn through live examples.

This training is packed with real-life analytical challenges which you will learn to solve. Some of these we will solve together, some you will have as homework exercises.

In summary, this course has been designed for all skill levels and even if you have no programming or statistical background you will be successful in this course!

I can't wait to see you in class,

Sincerely,

Kirill Eremenko

Introduction to Data Science using Python (Module 1/3)

Learn Data science / Machine Learning using Python (Scikit Learn)

Created by Rakesh Gopalakrishnan - Over 260,000 Students

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Students: 120642, Price: Free

Students: 120642, Price:  Free

Are you completely new to Data science?

Have you been hearing these buzz words like Machine learning, Data Science, Data Scientist, Text analytics, Statistics and don't know what this is?

Do you want to start or switch career to Data Science and analytics?

If yes, then I have a new course for you. In this course, I cover the absolute basics of Data Science and Machine learning. This course will not cover in-depth algorithms. I have split this course into 3 Modules. This module, takes a 500,000ft. view of what Data science is and how is it used. We will go through commonly used terms and write some code in Python. I spend some time walking you through different career areas in the Business Intelligence Stack, where does Data Science fit in, What is Data Science and what are the tools you will need to get started. I will be using Python and Scikit-Learn Package in this course. I am not assuming any prior knowledge in this area. I have given some reading materials, which will help you solidify the concepts that are discussed in this lectures.

This course will the first data science course in a series of courses. Consider this course as a 101 level course, where I don't go too much deep into any particular statistical area, but rather just cover enough to raise your curiosity in the field of Data Science and Analytics.

The other modules will cover more complex concepts. 

Tableau 20 Advanced Training: Master Tableau in Data Science

Master Tableau 20 in Data Science by solving Real-Life Analytics Problems. Learn Visualisation and Data Mining by doing!

Created by Kirill Eremenko - Data Scientist

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Students: 76386, Price: $109.99

Students: 76386, Price:  Paid

Tableau 2020 Advanced Training: Master Tableau in Data Science

Master Tableau 2020 in Data Science by solving Real-Life Analytics Problems. Learn Visualisation and Data Mining by doing!

Ready to take your Tableau skills to the next level?

Want to truly impress your boss and the team at work?

This course is for you!

Hours of professional Tableau Video training, unique datasets designed with years of industry experience in mind, engaging exercises that are both fun and also give you a taste for Analytics of the REAL WORLD.

In this course you will learn:

  • How to use Groups and Sets to increase your work efficiency 10x

  • How to use Dynamic Sets

  • How to Control Sets With Parameters

  • Everything about Table Calculations and how to use their power in your analysis

  • How to perform Analytics and Data Mining in Tableau

  • How to create Animations in Tableau

  • And much, much more!

Each module is independent so you can start learning from wherever you see fit. The more you learn the better you will get. However, you can stop at any time you will still have a strong set of skills to take with you.

Each module is based on a unique case study, where you will need to apply Tableau in a practical way and learn theory by doing.

Introduction to R

Learn the core fundamentals of the R language for interactive use as well as programming

Created by Jagannath Rajagopal - Entrepreneur and Data Scientist

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Students: 34525, Price: Free

Students: 34525, Price:  Free

UPDATE: As of Nov 22, 2018, this course is now free! Many thanks to all my existing students who made it possible for the wider audience to benefit from the course material :-)

With "Introduction to R", you will gain a solid grounding of the fundamentals of the R language! 

This course has about 90 videos and 140+ exercise questions, over 10 chapters. To begin with, you will learn to Download and Install R (and R studio) on your computer. Then I show you some basic things in your first R session. 

From there, you will review topics in increasing order of difficulty, starting with Data/Object Types and Operations, Importing into R, and Loops and Conditions

Next, you will be introduced to the use of R in Analytics, where you will learn a little about each object type in R and use that in Data Mining/Analytical Operations. 

After that, you will learn the use of R in Statistics, where you will see about using R to evaluate Descriptive Statistics, Probability Distributions, Hypothesis Testing, Linear Modeling, Generalized Linear Models, Non-Linear Regression, and Trees. 

Following that, the next topic will be Graphics, where you will learn to create 2-dimensional Univariate and Multi-variate plots. You will also learn about formatting various parts of a plot, covering a range of topics like Plot Layout, Region, Points, Lines, Axes, Text, Color and so on. 

At that point, the course finishes off with two topics: Exporting out of R, and Creating Functions

Each chapter is designed to teach you several concepts, and these have been grouped into sub-sections. A sub-section usually has the following: 

  • A Concept Video

  • An Exercise Sheet

  • An Exercise Video (with answers)

 
 
 

Why take a course to learn R? 

When I look to advancing my R knowledge today, I still face the same sort of situation as when I originally started to use R. Back when I was learning R, my approach was learn by doing. There was a lot of free material out there (and I refer to that early in the course) that gave me a framework, but the wording was highly technical in nature. Even with the R help and the free material, it took me up to a couple of months of experimentation to gain a certain level of proficiency. What I would have liked at that time was a way to learn the fundamentals quicker. I have designed this course with exactly that in mind. 

Why my course? 

For those of you that are new to R, this course will cover enough breadth/depth in R to give you a solid grounding. I use simple language to explain the concepts. Also, I give you 140+ exercise questions many of which are based on real world data for practice to get you up and running quickly, all in a single package. This course is designed to get you functional with R in little over a week

For those beginners with some experience that have learnt R through experimentation, this course is designed to complement what you know, and round out your understanding of the same. 

Essentials of Data Science

Discover what Data Science is all about

Created by Maximilian Schallwig - Data Scientist

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Students: 25288, Price: Free

Students: 25288, Price:  Free

Data Science is growing ever faster as Big Data become an increasingly important part of our lives.
Because data is universal, the applications of Data Science are pretty much endless, all you need is access to the data of the system that you want to study.

Since it's such a new field, there are a lot of questions about what is Data Science, what do Data Scientists do, and what do you need to succeed as a Data Scientist? 

This course is designed to give you an overview of the three essential areas of Data Science, the areas that every good data scientist should know, and being proficient in these areas can be the key to your success. After this course you will have a clear understanding of what Data Science is all about, and can make a clear decision of if it's the right field for you. You will also know what areas are important in Data Science, and hence can make informed decisions on what areas to focus on learning.

When you really get into Data Science there are other areas that start coming in too, but all of these can be traced back to one, or multiple, of these three basic foundations.

Cluster Analysis and Unsupervised Machine Learning in Python

Data science techniques for pattern recognition, data mining, k-means clustering, and hierarchical clustering, and KDE.

Created by Lazy Programmer Team - Artificial Intelligence and Machine Learning Engineer

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Students: 21880, Price: $29.99

Students: 21880, Price:  Paid

Cluster analysis is a staple of unsupervised machine learning and data science.

It is very useful for data mining and big data because it automatically finds patterns in the data, without the need for labels, unlike supervised machine learning.

In a real-world environment, you can imagine that a robot or an artificial intelligence won’t always have access to the optimal answer, or maybe there isn’t an optimal correct answer. You’d want that robot to be able to explore the world on its own, and learn things just by looking for patterns.

Do you ever wonder how we get the data that we use in our supervised machine learning algorithms?

We always seem to have a nice CSV or a table, complete with Xs and corresponding Ys.

If you haven’t been involved in acquiring data yourself, you might not have thought about this, but someone has to make this data!

Those “Y”s have to come from somewhere, and a lot of the time that involves manual labor.

Sometimes, you don’t have access to this kind of information or it is infeasible or costly to acquire.

But you still want to have some idea of the structure of the data. If you're doing data analytics automating pattern recognition in your data would be invaluable.

This is where unsupervised machine learning comes into play.

In this course we are first going to talk about clustering. This is where instead of training on labels, we try to create our own labels! We’ll do this by grouping together data that looks alike.

There are 2 methods of clustering we’ll talk about: k-means clustering and hierarchical clustering.

Next, because in machine learning we like to talk about probability distributions, we’ll go into Gaussian mixture models and kernel density estimation, where we talk about how to "learn" the probability distribution of a set of data.

One interesting fact is that under certain conditions, Gaussian mixture models and k-means clustering are exactly the same! We’ll prove how this is the case.

All the algorithms we’ll talk about in this course are staples in machine learning and data science, so if you want to know how to automatically find patterns in your data with data mining and pattern extraction, without needing someone to put in manual work to label that data, then this course is for you.

All the materials for this course are FREE. You can download and install Python, Numpy, and Scipy with simple commands on Windows, Linux, or Mac.

This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.

"If you can't implement it, you don't understand it"

  • Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".

  • My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch

  • Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?

  • After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times...

Suggested Prerequisites:

  • matrix addition, multiplication

  • probability

  • Python coding: if/else, loops, lists, dicts, sets

  • Numpy coding: matrix and vector operations, loading a CSV file

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

  • Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)

What is Data Science ?

Fundamental Concepts for Beginners

Created by Gopinath Ramakrishnan - Data Science & Machine Learning Enthusiast, Agile Coach

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Students: 19599, Price: Free

Students: 19599, Price:  Free

If you have absolutely no idea what Data Science is and are looking for a very quick non-technical introduction to Data Science , this course will help you get started on fundamental concepts underlying Data Science.

If you are an experienced Data Science professional, attending this course will give you some idea of how to explain your profession to an absolute lay person.

There are lots of very good  technical and programming focused courses available on Data  Science in Udemy and elsewhere.

This short  course will lay a firm foundation for better understanding and appreciation of what is being taught in advanced Data Science courses. 

Bootcamp for KNIME Analytics Platform

For users new to KNIME and data science, or experienced users of other data science tools.

Created by KNIME Inc - Data Science and Evangelism

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Students: 19142, Price: Free

Students: 19142, Price:  Free

If you've never used KNIME Analytics Platform before, this is the course for you. You can use KNIME Analytics Platform to create visual workflows with an intuitive, drag and drop style graphical interface, without the need for coding.

We'll start with installation and setup of the software, and present detailed materials on its features. We'll move on to some practical application of data blending from different sources, and use real datasets to show you all the different way you can transform, clean, and aggregate information. Finally, we'll introduce some machine learning algorithms for classification, and show you how to build your own models.

More than 50 videos are provided, along with some exercises for you to work on independently. By the end of the course, we want you to feel comfortable with the interface of KNIME Analytics Platform, be able to perform common processing tasks with your own data, and start putting predictive analytics into practice.

R Level 1 – Data Analytics with R

Use R for Data Analytics and Data Mining

Created by R-Tutorials Training - Data Science Education

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Students: 12944, Price: $109.99

Students: 12944, Price:  Paid

Are you new to R?

Do you want to learn more about statistical programming?

Are you in a quantitative field?

You just started learning R but you struggle with all the free but unorganized material available elsewhere?

Do you want to hack the learning curve and stay ahead of your competition?

If your answer is YES to some of those points - read on!

This Tutorial is the first step - your Level 1 - to R mastery.

All the important aspects of statistical programming ranging from handling different data types to loops and functions, even graphs are covered.

While planing this course I used the Pareto 80/20 principle. I filtered for the most useful items in the R language which will give you a quick and efficient learning experience.

Learning R will help you conduct your projects. On the long run it is an invaluable skill which will enhance your career.

Your journey will start with the theoretical background of object and data types. You will then learn how to handle the most common types of objects in R. Much emphasis is put on loops in R since this is a crucial part of statistical programming. It is also shown how the apply family of functions can be used for looping.

In the graphics section you will learn how to create and tailor your graphs. As an example we will create boxplots, histograms and piecharts. Since the graphs interface is quite the same for all types of graphs, this will give you a solid foundation.

With the R Commander you will also learn about an alternative to RStudio. Especially for classic hypthesis tests the R Coomander GUI can save you some time.

According to the teaching principles of R Tutorials every section is enforced with exercises for a better learning experience. Furthermore you can also check out the r-tutorials R exercise database over at our webpage. In the database you will find more exercises on the topics of this course.

You can download the code pdf of every section to try the presented code on your own.

This tutorial is your first step to benefit from this open source software.

What R you waiting for?

Martin

Bash Shell Programming for Data Sciences: Animated

Innovative Project-based Animated Linux Command Line Masterclass: Bash Shell Programming Data Mining Science- 7.5 Hours

Created by Scientific Programmer™ Team - ScientificProgrammer.me | Instructor Team

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Students: 11528, Price: $19.99

Students: 11528, Price:  Paid

THIS IS THE BEST, MOST INNOVATIVE AND THE HIGHEST RATED AWARD WINNING LINUX COMMAND LINE COURSE (ANIMATED TUTORIALS & LINUX COMMAND LINE HD SCREEN CASTS) ON THE UDEMY PLATFORM. AWESOME FIVE STARS ⭐⭐⭐⭐⭐ VIDEOS OF 7.5 HOURS, ALL UPDATED FOR THE 2021.

This awesome course is specifically designed to show you how to use the Linux commands and Bash shell programming to handle textual data which can be a csv format data or systems log file. In this course you will  learn Bash by doing projects. 

However, you need to understand the fact that Bash may not the best way to handle all kinds of data! But there often comes a time when you are provided with a pure Bash environment, such as what you get in the common Linux based Super-computers and you  just want an early result or view of the data before you drive into the real programming, using Python, R and SQL, SPSS, and so on. Expertise  in these data-intensive languages also comes at the price of spending a  lot of time on them.

In contrast, bash scripting is simple, easy to learn and perfect for mining textual data! Particularly if you deal with  genomics, microarrays, social networks, life sciences, and so on. It  can help you to quickly sort, search, match, replace, clean and optimise  various aspect of your data, and you wouldn’t need to go through any  tough learning curves. We strongly believe, learning and using Bash  shell scripting should be the first step if you want to say, Hello Big Data!

Also Featured on! popular Data Analytics Portals! Towards Data Science, Code Burst, Devto and so on.

This course starts with some practical bash-based flat file data mining projects involving:

  • University ranking data

  • Facebook data

  • AU Crime Data

  • Text Mining with Shakespeare-era Play and Poems

(Data sets and PDF text documentations are provided at the end of each section) + Free interactive playgrounds included!

If you haven’t used Bash before, feel free to skip the projects and get to  the tutorials part (supporting materials: eBook). Read the tutorials and then come back to the  projects again. The tutorial section will introduce with bash scripting,  regular expressions, AWK, sed, grep and so on. Students purchasing this course will receive free access to the interactive version (with Scientific code playgrounds) of this course from the Scientific Programming School (SCIENTIFIC PROGRAMMING IO). Based on your earlier feedback, we are introducing a Zoom live class lecture series on this course through which we will explain different aspects of Linux command line for Data analytics. Live classes will be delivered through the Scientific Programming School, which is an interactive and advanced e-learning platform for learning scientific coding.

MONEY BACK GUARANTEE IF NOT 100% SATISFIED!

When you enroll you will get lifetime access to all of the course contents and any updates and when you complete the course 100% you will also get a Certificate of completion that you can add to your resumé/CV to show off to the world your new-found Linux & Scientific Computing Mastery! So What are you Waiting For? Click that shiny enroll button and we'll See you inside. We created here a total of one university semester worth of knowledge (valued USD $2500-6000) into one single video course, and hence, it's a high-level overview.  Don't forget to join our Q&A live community where you can get free help anytime from other students and the instructor. This awesome course is a component of the Learn Scientific Computing master course.

UDEMY EARLY ACCESS PROGRAM REVIEWS (5 out of 5 Stars):

"This is one of the best course I have reviewed in Udemy. All the chapters are very useful. The instructor explained exactly what you need  to use Bash as your data analysis tool in your pocket. I look forward more  coursed from this Instructor. The instructor is very experienced, explanations are  on point. Than you for creating a great course." -  Tarique Syed

"The instructor was very engaging. Changed a boring, hard-to-understand tool into something usable and easy-to-use, all the while making it fun to learn." - Prat Ram"Well done. Well - structured and explained course. Will definitely recommend the course to my course. From my point of view, everything was OK in the course." - Sem Milaserdov  "Overall, the course delivered what promised with a good resource for those who want to learn and do more. The course is filled with resource and the educator attached his own book on the subject for the learners." - Afshin Kalantari

"It's a very well organized course, from the background, basic Linux cli which everyone should be to build  data processing scenarios. wonderful class." - Charley Guan

Use of Business Intelligence | basics of Data & Data Mining

You will learn Business Intelligence, Data Requirements, data analysis, Dashboards, Data warehouse & Mining

Created by Manish Gupta - Hospitality Finance Expert and Business Strategist

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Students: 10743, Price: $89.99

Students: 10743, Price:  Paid

This course will teach you three main things.

1. Meaning Business Intelligence and process of developing business intelligence system -

We will let you through the process of making business decisions, the process of analysis, How to develop Dashboards, How to identify KPIs, and align with business needs.

2. Meaning of Data and basics of database management and design

In this section we will learn what data is included (its much more than text and tables), How we store the data and analysise the data through data warehouse process

3. Understand what is data mining and how data mining can be used in business.

We will take you through basis process of data mining. THIS COURSE IS NOT FOR YOU IF YOU ARE LOOKING TO LEARN ALGORITHMS OR REAL PROGRAMMING. In this course we will focus on how you can use data mining techniques in understanding and resolving business problems.

Business Intelligence is necessary at every step of business whether planning, growing, scaling, and day-to-day decision making. We will learn how we can create intelligent systems.

We will also learn how to design and create a dashboard by choosing the right KPIs balancing all key prospects of the business.

The cornerstone of business intelligence is data and its storage. we will learn what are different types of data and how it is stored in a database. We will earn the essentials of data ware.

Data mining is important due to the large data volumes generated by society. we will learn how we can use this vast data in business applications

Data Science with Analogies, Algorithms and Solved Problems

Machine learning, Data Mining, Data Science, Deep Learning, Data analysis, Data analytics, Python, Visualization

Created by Ajay Dhruv - Assistant Professor at VIT Mumbai, India

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Students: 9523, Price: Free

Students: 9523, Price:  Free

Interested to know about the field of Machine Learning?

Then this course is for you! This course has been designed such that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way.

We will walk you into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this field. While preparing this course special care is taken that the concepts are presented in fun and exciting way but at the same time, we dive deep into machine learning.

Here is a list of few of the topics we will be learning:

• Difference between Data Mining and Deep Learning

• Data and 5 Vs of Big Data

• Types of Attributes

• Outliers

• Supervised learning, Unsupervised learning, Reinforcement learning

• Python Libraries

• CNN, RNN, LSTM

• K - means Clustering Algorithm

• Bayesian Algorithm, ID3 Algorithm

• Simple Linear Regression

• Anaconda

• Visualization

TABLEAU 2018 ADVANCED: Master Tableau in Data Science

Master Tableau in Data Science by solving Real-Life Analytics Problems. Learn Visualisation and Data Mining by doing!

Created by Kirill Eremenko - Data Scientist

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Students: 5984, Price: $124.99

Students: 5984, Price:  Paid

Ready to take your Tableau skills to the next level? 

Want to truly impress your boss and the team at work?

This course is for you!

Hours of professional Tableau Video training, unique datasets designed with years of industry experience in mind, engaging exercises that are both fun and also give you a taste for Analytics of the REAL WORLD.

In this course you will learn:

  • How to use Groups and Sets to increase your work efficiency 10x
  • Everything about Table Calculations and how to use their power in your analysis
  • How to perform Analytics and Data Mining in Tableau
  • How to create Animations in Tableau
  • And much, much more!

Each module is independent so you can start learning from wherever you see fit. The more you learn the better you will get. However, you can stop at any time you will still have a strong set of skills to take with you.

Fundamental Question on Data Mining

Multiple Choice Questions (MCQ) on Data Mining

Created by Harish Kumar Maheshwari - Academic Consultant / Electronics Engineer

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Students: 5804, Price: $19.99

Students: 5804, Price:  Paid

Preparing for Interview in Data Mining? Don’t be stressed, take our Data Mining based quiz questions and prepare yourself for the interview.

With this Data Mining Quiz Questions, we are going to you build your confidence by providing tips and trick to solve Data Mining based questions. Here you will get Data Mining based Multiple Choice Questions and Answers for your next  job or exam. In Data Mining Multiple Choice Questions based practice tests, there will be a series of practice tests wherein you can test your Basic Data Mining concepts.

Who should Practice these Data Mining based Questions ?

  • Anyone wishing to sharpen their knowledge in Data Mining

  • Anyone preparing for JOB interview in Data Mining

What is the Importance of Data Mining ?

Data Mining is a revolutionary technology that’s changing how businesses and industries function across the globe in a good way. This Data Mining quiz, is a practice test that is focused to help people wanting to start their career in the Data Mining industry. This Data Mining Bootcamp helps you assess how prepared are you for the Job Interview.

Here, you get Data Mining Based MCQs that test your knowledge on the technology. These Questions are prepared by subject matter experts and are in line with the questions you can come across in Job Interview. Take this test today!

Generally, you need to refer a variety of books and Websites in order to cover the ocean of topics in Data Mining. To make it easy for you guys, I have collected a few Data Mining Based questions from different topics, When you solve these Question then definitely Your confidence will Increase.

Learn Python For Data Science W/ Search & Recommender Algos!

Practice hands-on text mining with no prior coding skills! Learn basic keyword extraction, search, and recommendation.

Created by Larry Wai - VP Data Science at Chegg

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Students: 5359, Price: $89.99

Students: 5359, Price:  Paid

This course covers the basic data science skills of python and text mining of keywords.  The student will also learn simple search and recommendation algorithms.  Data processing, calculations, and analysis related to keyword extraction will be taught using a hands-on project / coding test based approach.  Python will be taught in a systematic, example based method using the text dataset included especially for this course.  In addition to python, the exercises will include application of skills using the emacs editor.  The course should greatly benefit anybody interested in learning how to code, and especially for aspiring data scientists.

Text Mining and Natural Language Processing in R

Hands-on text mining and natural language processing (NLP) training for data science applications in R

Created by Minerva Singh - Bestselling Instructor & Data Scientist(Cambridge Uni)

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Students: 4769, Price: $99.99

Students: 4769, Price:  Paid

Do You Want to Gain an Edge by Gleaning Novel Insights from Social Media?

Do You Want to Harness the Power of Unstructured Text and Social Media to Predict Trends?

Over the past decade there has been an explosion in social media sites and now sites like Facebook and Twitter are used for everything from sharing information to distributing news. Social media both captures and sets trends. Mining unstructured text data and social media is the latest frontier of machine learning and data science. 

LEARN FROM AN EXPERT DATA SCIENTIST  WITH +5 YEARS OF EXPERIENCE:

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. Unlike other courses out there, which focus on theory and outdated methods, this course will teach you practical techniques to harness the power of both text data and social media to build powerful predictive models. We will cover web-scraping, text mining and natural language processing along with mining social media sites like Twitter and Facebook for text data. Additionally you will learn to apply both exploratory data analysis and machine learning techniques to gain actionable insights from text and social media data .

TAKE YOUR DATA SCIENCE CAREER TO THE NEXT LEVEL

BECOME AN EXPERT IN TEXT  MINING & NATURAL LANGUAGE PROCESSING :

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 R based data science in real life. After taking this course, you’ll easily use packages like caret, dplyr to work with real data in R. You will also learn to use the common social media mining and natural language processing packages to extract insights from text data.  
I will even introduce you to some very important practical case studies - such as identifying important words in a text and predicting movie sentiments based on textual  reviews. You will also extract tweets pertaining to trending topics and analyze their underlying sentiments and identify topics with Latent Dirichlet allocation. With this Powerful  course, you’ll know it all:  extracting text data from websites, extracting data from social media sites and carrying out analysis of these using visualization, stats, machine learning, and deep learning!  

Start analyzing data for your own projects, whatever your skill level and Impress your potential employers with actual examples of your data science projects.

 HERE IS WHAT YOU WILL GET:

  • Data Structures and Reading in R, including CSV, Excel, JSON, HTML data.

  • Web-Scraping using R
  • Extracting text data from Twitter and Facebook using APIs
  • Extract and clean data from the FourSquare app
  • Exploratory data analysis of textual data
  • Common Natural Language Processing techniques such as sentiment analysis and topic modelling
  • Implement machine learning techniques such as clustering, regression and classification on textual data
  • Network analysis

Plus you will apply your newly gained skills and complete a practical text analysis assignment

We will spend some time dealing with some of the theoretical concepts. 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.

All the data and code used in the course has been made available free of charge and you can use it as you like. You will also have access to additional lectures that are added in the future for FREE.

JOIN THE COURSE NOW!

Regression, Data Mining, Text Mining, Forecasting using R

Learn Regression Techniques, Data Mining, Forecasting, Text Mining using R

Created by ExcelR Solutions - Pioneer in professional management trainings & consulting

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Students: 4351, Price: $99.99

Students: 4351, Price:  Paid

Data Science using R is designed to cover majority of the capabilities of R from Analytics & Data Science perspective, which includes the following:

  • Learn about the basic statistics, including measures of central tendency, dispersion, skewness, kurtosis, graphical representation, probability, probability distribution, etc.
  • Learn about scatter diagram, correlation coefficient, confidence interval, Z distribution & t distribution, which are all required for Linear Regression understanding
  • Learn about the usage of R for building Regression models
  • Learn about the K-Means clustering algorithm & how to use R to accomplish the same
  • Learn about the science behind text mining, word cloud, sentiment analysis & accomplish the same using R
  • Learn about Forecasting models including AR, MA, ES, ARMA, ARIMA, etc., and how to accomplish the same using R
  • Learn about Logistic Regression & how to accomplish the same using R

Data Mining with R: Go from Beginner to Advanced!

Learn to use R software for data analysis, visualization, and to perform dozens of popular data mining techniques.

Created by Geoffrey Hubona, Ph.D. - Associate Professor of Information Systems

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Students: 4351, Price: $99.99

Students: 4351, Price:  Paid

This is a "hands-on" business analytics, or data analytics course teaching how to use the popular, no-cost R software to perform dozens of data mining tasks using real data and data mining cases. It teaches critical data analysis, data mining, and predictive analytics skills, including data exploration, data visualization, and data mining skills using one of the most popular business analytics software suites used in industry and government today. The course is structured as a series of dozens of demonstrations of how to perform classification and predictive data mining tasks, including building classification trees, building and training decision trees, using random forests, linear modeling, regression, generalized linear modeling, logistic regression, and many different cluster analysis techniques. The course also trains and instructs on "best practices" for using R software, teaching and demonstrating how to install R software and RStudio, the characteristics of the basic data types and structures in R, as well as how to input data into an R session from the keyboard, from user prompts, or by importing files stored on a computer's hard drive. All software, slides, data, and R scripts that are performed in the dozens of case-based demonstration video lessons are included in the course materials so students can "take them home" and apply them to their own unique data analysis and mining cases. There are also "hands-on" exercises to perform in each course section to reinforce the learning process. The target audience for the course includes undergraduate and graduate students seeking to acquire employable data analytics skills, as well as practicing predictive analytics professionals seeking to expand their repertoire of data analysis and data mining knowledge and capabilities.

Text Mining, Scraping and Sentiment Analysis with R

Learn how to use Twitter social media data for your R text mining work.

Created by R-Tutorials Training - Data Science Education

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Students: 3842, Price: $94.99

Students: 3842, Price:  Paid

Are you an advanced R user, looking to expand your R toolbox?

Are you interested in social media sentiment analysis?

Do you want to learn how you can get and use Twitter data for your R analysis?

Do you want to learn how you can systematically find related words (keywords) to a search term using Twitter and R?

Are you interested in creating visualizations like wordclouds out of text data?

Do you want to learn which R packages you can use for web scraping and text analysis purposes?

If YES came to your mind to some of those points – this course might be tailored towards your needs!

This course will teach you anything you need to know about how to handle social media data in R. We will use Twitter data as our example dataset.

During this course we will take a walk through the whole text analysis process of Twitter data.

At first you will learn which packages are available for social media analysis.

You will learn how to scrape social media (Twitter) data and get it into your R session.

After that we will filter, clean and structure our text corpus.

The next step is the visualization of the text data via wordclouds and dendrograms.

And in the last section we will do a whole sentiment analysis by using a common word lexicon.

All of those steps are accompanied by exercise sessions so that you can check if you can put the information to work.

According to the teaching principles of R Tutorials every section is enforced with exercises for a better learning experience. You can download the code pdf of every section to try the presented code on your own.

Disclaimer required by Twitter: 'TWITTER, TWEET, RETWEET and the Twitter logo are trademarks of Twitter, Inc or its affiliates.'

Data Science:Data Mining & Natural Language Processing in R

Harness the Power of Machine Learning in R for Data/Text Mining, & Natural Language Processing with Practical Examples

Created by Minerva Singh - Bestselling Instructor & Data Scientist(Cambridge Uni)

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Students: 3205, Price: $94.99

Students: 3205, Price:  Paid

                      

                               MASTER DATA SCIENCE, TEXT MINING AND NATURAL LANGUAGE PROCESSING IN R:

Learn to carry out pre-processing, visualization and machine learning tasks such as: clustering, classification and regression in R. You will be able to mine insights from text data and Twitter to give yourself & your company a competitive edge.    

                               LEARN FROM AN EXPERT DATA SCIENTIST  WITH +5 YEARS OF EXPERIENCE:

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 R 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 students an incomplete knowledge of the subject. Unlike other courses out there, we are not going to stop at machine learning. We will also cover data mining, web-scraping, text mining and natural language processing along with mining social media sites like Twitter and Facebook for text data.

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

You’ll start by absorbing the most valuable R Data Science basics and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in R.
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 R based data science in real life. After taking this course, you’ll easily use packages like caret, dplyr to work with real data in R. You will also learn to use the common NLP packages to extract insights from text data.  
I will even introduce you to some very important practical case studies - such as detecting loan repayment and tumor detection using machine learning. You will also extract tweets pertaining to trending topics and analyze their underlying sentiments and identify topics with Latent Dirichlet allocation. With this Powerful All-In-One R Data Science course, you’ll know it all: visualization, stats, machine learning, data mining, and neural networks!  

The underlying motivation for the course is to ensure you can apply R based data science on real data 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 projects.

 HERE IS WHAT YOU WILL GET:

(a) This course will take you from a basic level to performing some of the most common advanced data science techniques using the powerful R based tools.   

(b) Equip you to use R to perform the different exploratory and visualization tasks for data modelling.   

(c) Introduce you to some of the most important machine learning concepts in a practical manner such that you can apply these concepts for practical data analysis and interpretation.   (d) You will get a strong understanding of some of the most important data mining, text mining and natural language processing techniques.   

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

More Specifically, here's what's covered in the course:

  • Getting started with R, R Studio and Rattle for implementing different data science techniques

  • Data Structures and Reading in Pandas, including CSV, Excel, JSON, HTML data.

  • How to Pre-Process and “Wrangle” your R 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 R

  • Neural Networks for Classification and Regression

  • Web-Scraping using R

  • Extracting text data from Twitter and Facebook using APIs

  • Text mining

  • Common Natural Language Processing techniques such as sentiment analysis and topic modelling

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.

All the data and code used in the course has been made available free of charge and you can use it as you like. You will also have access to additional lectures that are added in the future for FREE.

JOIN THE COURSE NOW!


Text Mining and Sentiment Analysis with Tableau and R

Data Science with R and Tableau: Extract valuable info out of Twitter to rock in marketing, finance, or any research.

Created by R-Tutorials Training - Data Science Education

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Students: 3184, Price: $94.99

Students: 3184, Price:  Paid

Extract valuable info out of Twitter for marketing, finance, academic or professional research and much more.

This course harnesses the upside of R and Tableau to do sentiment analysis on Twitter data. With sentiment analysis you find out if the crowd has a rather positive or negative opinion towards a given search term. This search term can be a product (like in the course) but it can also be a person, region, company or basically anything as long as it is mentioned regularly on Twitter.

While R can directly fetch the text data from Twitter, clean and analyze it, Tableau is great at visualizing the data. And that is the power of the method outlined in this course. You get the best of both worlds, a dream team.

Content overview and course structure:

The R Side

Getting a Twitter developers account

Connection of Twitter and R

Getting the right packages for our approach

Harvesting Tweets and loading them into R

Refining the harvesting approach by language, time, user or geolocation

Handling Twitter meta data like: favorites, retweets, timelines, users, etc

Text cleaning

Sentiment scoring via a simple lexicon approach (in English)

Data export (csv) for further Tableau work

Tableau Side:

Data preparation for visualizations

Quick data exploration

Dashboards

Visualizing - 

  • Popularity of different products
  • Popularity between different locations on a map
  • Changes in popularity over time

You only need basic R skills to follow along. There is a free version of Tableau called Tableau public desktop, or even better: as a full time college student you can get a free but full version of Tableau desktop professional.

The course comes with the R code to copy into your R session.

Disclaimer required by Twitter: 'TWITTER, TWEET, RETWEET and the Twitter logo are trademarks of Twitter, Inc or its affiliates.'

Case Studies in Data Mining with R

Learn to use the "Data Mining with R" (DMwR) package and R software to build and evaluate predictive data mining models.

Created by Geoffrey Hubona, Ph.D. - Associate Professor of Information Systems

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Students: 2145, Price: $89.99

Students: 2145, Price:  Paid

Case Studies in Data Mining was originally taught as three separate online data mining courses. We examine three case studies which together present a broad-based tour of the basic and extended tasks of data mining in three different domains: (1) predicting algae blooms; (2) detecting fraudulent sales transactions; and (3) predicting stock market returns. The cumulative "hands-on" 3-course fifteen sessions showcase the use of Luis Torgo's amazingly useful "Data Mining with R" (DMwR) package and R software. Everything that you see on-screen is included with the course: all of the R scripts; all of the data files and R objects used and/or referenced; as well as all of the R packages' documentation. You can be new to R software and/or to data mining and be successful in completing the course. The first case study, Predicting Algae Blooms, provides instruction regarding the many useful, unique data mining functions contained in the R software 'DMwR' package. For the algae blooms prediction case, we specifically look at the tasks of data pre-processing, exploratory data analysis, and predictive model construction. For individuals completely new to R, the first two sessions of the algae blooms case (almost 4 hours of video and materials) provide an accelerated introduction to the use of R and RStudio and to basic techniques for inputting and outputting data and text. Detecting Fraudulent Transactions is the second extended data mining case study that showcases the DMwR (Data Mining with R) package. The case is specific but may be generalized to a common business problem: How does one sift through mountains of data (401,124 records, in this case) and identify suspicious data entries, or "outliers"? The case problem is very unstructured, and walks through a wide variety of approaches and techniques in the attempt to discriminate the "normal", or "ok" transactions, from the abnormal, suspicious, or "fraudulent" transactions. This case presents a large number of alternative modeling approaches, some of which are appropriate for supervised, some for unsupervised, and some for semi-supervised data scenarios. The third extended case, Predicting Stock Market Returns is a data mining case study addressing the domain of automatic stock trading systems. These four sessions address the tasks of building an automated stock trading system based on prediction models that utilize daily stock quote data. The goal is to predict future returns for the S&P 500 market index. The resulting predictions are used together with a trading strategy to make decisions about generating market buy and sell orders. The case examines prediction problems that stem from the time ordering among data observations, that is, from the use of time series data. It also exemplifies the difficulties involved in translating model predictions into decisions and actions in the context of 'real-world' business applications.

Information Retrieval and Mining Massive Data Sets

Learn various techniques to build a Google scale Information Retrieval System.

Created by Omkar Deshpande - Data Scientist at WalmartLabs

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Students: 1687, Price: $19.99

Students: 1687, Price:  Paid

The goal is to introduce various techniques required to build an IR System. In this course we will explore various methods to solve big data problem. We will evaluate alternative solutions and trade offs. In the later part of the course we will discuss various data mining algorithms to make sense of massive data sets.

Data Science – Data Mining Unsupervised Learning R & Python

Become a Practical Data Scientist

Created by 360DigiTMG Elearning - 360DigiTMG is a leading training institute

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Students: 1307, Price: $24.99

Students: 1307, Price:  Paid

Learners will understand about Data Science- Data Mining Unsupervised Learning in developing & analyzing Data Science projects or Artificial Intelligence projects. Data mining unsupervised techniques are used as EDA techniques to derive insights from the business data.This course includes practical approach and discussed about Clustering segmentation, Dimension reduction, Association rules, Recommended system, Network Analytics, Text mining etc,.

Clustering segmentation : In this first module of unsupervised learning, get introduced to clustering algorithms. Learn about different approaches for data segregation to create homogeneous groups of data. Hierarchical clustering, K means clustering are most commonly used clustering algorithms. Understand the different mathematical approaches to perform data segregation. Also learn about variations in K-means clustering like K-medoids, K-mode techniques, learn to handle large data sets using CLARA technique.

Dimension Reduction (PCA) / Factor Analysis Description: Learn to handle high dimensional data. The performance will be hit when the data has a high number of dimensions and machine learning techniques training becomes very complex, as part of this module you will learn to apply data reduction techniques without any variable deletion. Learn the advantages of dimensional reduction techniques. Also, learn about yet another technique called Factor Analysis.

Association rules : Learn to measure the relationship between entities. Bundle offers are defined based on this measure of dependency between products. Understand the metrics Support, Confidence and Lift used to define the rules with the help of Apriori algorithm. Learn pros and cons of each of the metrics used in Association rules

Recommended system : Personalized recommendations made in e-commerce are based on all the previous transactions made. Learn the science of making these recommendations using measuring similarity between customers. The various methods applied for collaborative filtering, their pros and cons, SVD method used for recommendations of movies by Netflix will be discussed as part of this module.

Network Analytics : Study of a network with quantifiable values is known as network analytics. The vertex and edge are the node and connection of a network, learn about the statistics used to calculate the value of each node in the network. You will also learn about the google page ranking algorithm as part of this module.

Learn Data Mining and Machine Learning With Python

Learn how to create Machine Learning algorithms in Python and use them in Data Mining

Created by Data Science Guide - Data Scientist & SQL Developer

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Students: 982, Price: $19.99

Students: 982, Price:  Paid

If you need to learn how to understand and create Machine Learning models used to solve business problems, this course is for you. You will learn in this course everything you need about Data Mining process, Machine Learning and how to implement Machine Learning algorithms in Data Mining. This course was designed to provide information in a simple and straight forward way so ease learning methods. You will from scratch and keep building your knowledge step by step until you become familiar with the most used Machine Learning algorithms.   

Text Mining – Learn it from Scratch Using Case Study in R!

Learn text mining and data mining in order to make your first move towards learning Natural Language Processing.

Created by Bhaumik Shah - Data Scientist!

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Students: 546, Price: $89.99

Students: 546, Price:  Paid

  • Are you ready to discover interesting patterns, extract useful knowledge, and support decision making by mining and analyzing your Social Media Data i.e. Unstructured Data/ Text data?

  • Are you ready to build your 1st Word Cloud that gives you Visual insights on your Text Data within Few Minutes!?

Now if go by facts and figures than by 2020, there will be around 2.9 billion social network users around the globe. And a vast amount of new information and data is generated every day i.e. Over 2.5 quintillion bytes of data are created every single day, and it's only going to grow from there. By 2020, it's estimated that 1.7MB of data will be created every second for every person on earth. And 90% of world's data is in an unstructured format (text data), which makes mining this unstructured data i.e. text mining one of the most sought after skills in Data Science and Machine learning.

LEARN IT FROM EXPERIENCED DATA SCIENCE GUY!

Hello Everyone!......... My name is Bhaumik S. Shah and I am a data science guy with master's degree in it and  I have a good amount of experience of working on different data science projects related to Machine learning (Regression, Classification, etc.), Time Series, Text Mining, Natural Language Processing, etc. as a freelancer. I have designed this course to make you familiar with text mining with a practical case study approach unlike other courses out there which focuses a lot on theory rather than practical approach. So in this course, we will start text mining from scratch, so it's okay if you are not familiar with this topic. We will cover some important/ required theory related to text mining in the beginning and we will straight move towards one case study using R for implementing it! . And you will also have access to future classes for FREE that will be updated in this course soon, in order to keep it relevant to the changing scenarios.

Project based Text Mining in Python

Use of Natural Language Processing, Machine Learning and Sentiment Analysis towards Data Science

Created by Taimoor khan - Asst. Professor

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Students: 417, Price: $19.99

Students: 417, Price:  Paid

In this course, we study the basics of text mining.

  1. The basic operations related to structuring the unstructured data into vector and reading different types of data from the public archives are taught.

  2. Building on it we use Natural Language Processing for pre-processing our dataset.

  3. Machine Learning techniques are used for document classification, clustering and the evaluation of their models.

  4. Information Extraction part is covered with the help of Topic modeling

  5. Sentiment Analysis with a classifier and dictionary based approach

  6. Almost all modules are supported with assignments to practice.

  7. Two projects are given that make use of most of the topics separately covered in these modules.

  8. Finally, a list of possible project suggestions are given for students to choose from and build their own project.

Data Scraping & Data Mining from Beginner to Pro with Python

Mastering Web Scraping, Web Crawling, Data Mining in Python, Beautiful Soup, Scrapy, Selenium, CSS Selectors, Requests

Created by AI Sciences - AI Experts & Data Scientists |4+ Rated | 160+ Countries

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Students: 380, Price: $94.99

Students: 380, Price:  Paid

Comprehensive Course Description:

Data scraping is the technique of extracting data from the internet. Data scraping is used for getting the data available on different websites and APIs. This also involves automating the web flows for extracting the data from different web pages.

The course ‘Data Scraping and Data Mining from Beginner to Professional’ is crafted to cover the topics that result in the development of the most in-demand skills in the workplace. These topics will help you understand the concepts and methodologies with regard to Python. The course is:

  • Easy to understand.

  • Imaginative and descriptive.

  • Comprehensive.

  • Practical with live coding.

  • Full of quizzes with solutions.

  • Rich with state-of-the-art and updated knowledge of this field.

This course is designed for beginners. We’ll spend sufficient time to lay a solid groundwork for newbies. Then, we will go far deep gradually with a lot of practical implementations where every step will be explained in detail.

As this course is essentially a compilation of all the basics, you will move ahead at a steady rate. You will experience more than what you have learned. At the end of every concept, we will be assigning you Home Work/assignments/activities/quizzes along with solutions. They will assess / (further build) your learning based on the previous data scraping and data mining concepts and methods. Most of these activities are designed to get you up and running with implementations.

The 4 hands-on projects included in this course are the most important part of this course. These projects allow you to experiment for yourself with trial and error. You will learn from your mistakes. Importantly, you will understand the potential gaps that may exist between theory and practice.

Data Scraping is undoubtedly a rewarding career that allows you to solve some of the most interesting real-world problems. You will be rewarded with a fabulous salary package, too. With a core understanding of Data Scraping, you can fine-tune your workplace skills and ensure emerging career growth.

So, without further delay, get started with this course and pursue the knowledge that can sharpen your skills.

Teaching is our passion:

We strive to create updated and workplace-relevant online tutorials that could help you in understanding the concepts adequately. Our aim is to create a strong basic understanding for our students before moving onward to the advanced version. We have added enough exercises into the course. You will be able to grasp the concepts easily, and you will be inspired to think for yourself in regard to the right solution and implement it. High-quality video content, descriptive course material, assessment questions, course notes, and handouts are some of the perks of this course. Please approach our friendly team in case of any queries, and we assure you we will respond as quickly as possible.

Course Content:

The comprehensive and engaging course consists of the following topics:

1. Introduction:

a. Intro

i. Why Data Scraping?

ii. Applications of Data Scraping

iii. Introduction of Instructor

iv. Introduction to Course, Scraping, Tools

v. Projects Overview

2. Requests Module:

a. Getting Started with Requests Module

i. Introduction to Python Requests and Installations

ii. Going Through the Documentation

b. Extracting Data

i. Sending a request to the server

ii. Getting a response from the server

iii. Parsing the data

iv. Controlling pagination

v. Understanding Ajax populated data

vi. Parsing Ajax response data

3. Beautiful Soup (BS4):

a. Getting Started with Beautiful Soup

i. Introduction to Beautiful Soup

ii. Going Through the Documentation

b. Hands-on with BS4 Parser

i. Extracting data using BS4 parser

ii. Developing an understanding of BS4 parser functions

iii. Attributes of tags

iv. Multi-valued attributes of tags

v. Merge data from two different requests.

c. Project

i. Building movie recommender system by getting live data from IMDB

4. CSS Selectors:

a. Getting Started with CSS Selectors

i. Introduction to CSS Selectors

b. Hands-on with the CSS Selectors

i. Descendants, Id, Class-based selection

ii. Nested Tags, ID Tags, Class Tags based selection

iii. Coma Separator, Universal Selectors based selection

iv. Sibling Notations, Direct Child based selection

v. Child Selectors based selection

vi. Negations, Attributes based selection

vii. Attributes, Attributes values-based selection

viii. Attributes Wild Cards values-based selection

5. Scrapy:

a. Getting Started with Scrapy

i. Introduction to Scrapy

ii. Going through the documentation

b. Hands-on with Scrapy

i. Developing the understanding of Spider flow.

ii. Creating our Scrapy project and understanding the framework

iii. Writing Spiders from scratch.

iv. Understanding the Response object along with all its param including url, status, headers, body, request, meta, flags, certificate, ip_address, copy, replace, urljoin, follow, follow_all.

v. Working on Scrapy shell.

vi. Understanding request flow in Scrapy.

vii. Applying CSS selectors to Scrapy response for getting data.

viii. Extracting nested data from the website.

ix. Combine Data from multiple callbacks.

c. Projects

i. Scraping IMDB

ii. Getting products information from HUGO BOSS

6. Selenium:

a. Getting started with Selenium

i. Introduction to Selenium

b. Hands-on with Scrapy

i. Configuring the web driver.

ii. Parse response and extract the required data

iii. Automating website flow

iv. Navigating the website with form filling

c. Project

i. Language translation system using deepL website

After completing this information-packed course successfully, you will be able to:

  • Implement any project from scratch that requires Data Scraping knowledge.

  • Relate the concepts and practical aspects of Data Scraping with real-world problems.

  • Know the theory and practical aspects of Data Scraping concepts.

  • Gather data from websites in the smartest way.

Who this course is for:

  • People who are quite beginners and know absolutely nothing about Data Scraping.

  • People who want to make smart solutions.

  • People who want to learn Data Scraping with real data.

  • People who love to learn theory and then implement it using Python.

Advanced Statistics and Data Mining for Data Science

Your one stop solution to conquering the woes in Statistics, Data Mining, Data Analysis and Data Science

Created by Packt Publishing - Tech Knowledge in Motion

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Students: 279, Price: $89.99

Students: 279, Price:  Paid

Data Science is an ever-evolving field. Data Science includes techniques and theories extracted from statistics, computer science, and machine learning. This video course will be your companion and ensure that you master various data mining and statistical techniques.

The course starts by comparing and contrasting statistics and data mining and then provides an overview of the various types of projects data scientists usually encounter. You will then learn predictive/classification modeling, which is the most common type of data analysis project. As you move forward on this journey, you will be introduced to the three methods (statistical, decision tree, and machine learning) with which you can perform predictive modeling. Finally, you will explore segmentation modeling to learn the art of cluster analysis. Towards the end of the course, you will work with association modeling, which will allow you to perform market basket analysis.

This course uses SPSS v25, while not the latest version available, it provides relevant and informative content for legacy users of SPSS.

About the Author :

Jesus Salcedo has a Ph.D. in Psychometrics from Fordham University. He is an independent statistical and data mining consultant and has been using SPSS products for over 20 years. He is a former SPSS Curriculum Team Lead and Senior Education Specialist who has written numerous SPSS training courses and trained thousands of users.