Applied Text Mining in Python

Course Link: https://www.coursera.org/learn/python-text-mining

Applied Text Mining in Python
This is the second course in the specialization about using text mining to mine applications for application knowledge. In this class you will learn how to use mining tools for app development and exploration. Tools discussed include: NN, XNN, RGBN, XYB, TGAL, NPB, Bluetooth, LIDAR, and GPS. You will need to know how to use tools and set up a basic mining setup to mine applications.

This course is aimed at anyone interested in app development and/or game programming. It is intended to be fun and educational, so if you have no previous mining experience, this course is for you!

* Required: Basic knowledge of python, basic knowledge of machine learning, recommender systems, and basic understanding of data science.

>>> By enrolling in this course you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms_of_service <<>> By enrolling in this course you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms_of_service <<https://www.coursera.org/learn/python-text-mining

Text Mining and Analytics

Course Link: https://www.coursera.org/learn/text-mining

Text Mining and Analytics Capstone
After successfully completing the first three courses in this specialization, you will be able to mine text using the advanced algorithms and techniques learned in the first three courses. This course is designed to help prepare you to join the search for lucrative and valuable clients by mining text. This first course will take you through fundamental concepts such as text clustering, corpora, and text mining. You will learn key algorithms for clustering words in a text, different algorithms for mining text and different types of information about words. You will also learn about different types of textual information, such as hypertext links, and about the semantics of different words. This course is designed to give you a chance to apply the knowledge you’ve acquired throughout the Specialization to solve a real-world data analysis problem. We’ll employ different statistical techniques to cover the different data sets being analyzed, using techniques from signal generation, classification, and sentiment analysis. We’ll also focus on the use of basic statistics to describe the state of the data, the number of observations, and the rate of change. These concepts and techniques will be applied to the analysis of a simple text file, from which you will derive insights into the mining process.Upon completing this course, you will be able to:1. Partition text to a desired degree within a text or text context2. Mine text using text clustering techniques3. Mine text using corpora4. Use text mining techniques to mine text5. Practice and use basic statistics to describe the state of the data6. Use simple text mining tools to mine text7. Integrate text mining techniques into your analysis8. Use a variety of techniques to mine text for your specialties9. Identify and apply signal generation techniques to mine text for your specialties10. Mining text using corpora11. Mining text using sentiment analysis12. Mining text using bignormal analysis13. Mining text using a variety of algorithms14. Mining text for your specialties15. Summary, Practitioner and Final Project16. Good Luck and Have Fun1. Intro and Setting up your environment2. Mining text2. Mining text for your specialties3. Mining text for your specialties4. Summary, Practitioner and Final Project4. Good Luck and Have Fun

* What is a data analysis session?
A session is any time you want to look at a data set, analyze it, and prepare a report about your analysis is an unlimited time investment. A session can be 2 or more, depending on your choice of study, and it depends on your schedule. We have prepared a checklist which you can use to track your progress and make sure you’re on the right track. In addition, we have prepared some questions to ask in an interview or through the course. These will help you understand the class and the expectations of the students. In addition, you will be given quizzes at the

Course Link: https://www.coursera.org/learn/text-mining

Applied Data Science with Python Specialization

Course Link: https://www.coursera.org/specializations/data-science-python

Applied Data Science with Python Specialization
This course builds upon the introductory programming skills and data science experience from the first class in the Specialization. We will be covering topics such as data abstraction, functional programming, data visualization, and programming in Python. You will learn how to use the built-in pandas data science library, install and configure packages, and use the Scikit-Learn libraries to explore, train, and explore datasets. You will also learn how to use the Scikit-Learn packages to perform custom types of analysis and visualization, and how to see how such analyses and visualizations are implemented in Python.

This course requires prior knowledge of programming in Python. We will be covering the basics with an emphasis on safety first. Please be aware that this course uses Python 3.

This course requires prior knowledge of Python programming, preferably in the ‘practice’ or ‘advanced’ stages. We will be covering the basics with an emphasis on safety first. Please be aware that this course uses Python 3.

This course requires prior knowledge of machine learning, particularly the deep learning fields. We will be covering the basics with an emphasis on safety first. Please be aware that this course uses Python 3.

This course requires prior knowledge of statistics and regression.  We will be covering the basics with an emphasis on safety first. Please be aware that this course uses Python 3.

This course requires prior knowledge of machine learning, particularly the deep learning fields.  We will be covering the basics with an emphasis on safety first. Please be aware that this course uses Python 3.

This course requires prior knowledge of statistics and regression.  We will be covering the basics with an emphasis on safety first. Please be aware that this course uses Python 3.

This course requires prior knowledge of statistics and regression.  We will be covering the basics with an emphasis on safety first. Please be aware that this course uses Python 3.

This course requires prior knowledge of machine learning, particularly the deep learning fields.  We will be covering the basics with an emphasis on safety first. Please be aware that this course uses Python 3.

This course requires prior knowledge of statistics and regression.  We will be covering the basics with an emphasis on safety first. Please be aware that this course uses Python 3.

This course requires prior knowledge of statistics and regression.  We will be covering the basics with an emphasis on safety first. Please be aware that this course uses Python 3.

This course requires prior knowledge of statistics and regression.  We will be covering the basics with an emphasis on safety first. Please be aware that this course uses Python 3.

This course requires prior knowledge of statistics and regression.  We will be covering the basics with an emphasis on safety first. Please be aware that this course uses Python 3.

This course requires prior knowledge of statistics and regression.  We will be covering the basics with an emphasis on safety first. Please be aware that this course uses Python 3.

This course requires prior knowledge of statistics and regression.  We will be covering the basics with an emphasis on safety first. Please be aware that this course uses Python 3.

This course requires prior knowledge of machine learning, particularly the deep learning fields.  We will be covering the basics with an emphasis on safety first. Please be aware that this course uses Python 3.

This course requires prior knowledge of statistics and regression.  We will be covering the basics with an emphasis on safety first. Please be aware that this course uses Python 3.

This course requires prior knowledge of statistics and regression.  We will be covering the basics with an emphasis on safety first. Please be aware that this course uses Python 3.

This course requires prior knowledge of statistics and regression.  We will be covering the basics with an emphasis on safety first. Please be aware that this course uses Python 3.

This course requires prior knowledge of machine learning, particularly the deep learning fields.  We will be covering the basics with an emphasis on safety first. Please be aware that this course uses Python 3.

This course requires prior knowledge of statistics and regression.  We will be covering the basics with an emphasis on safety first. Please be aware that this course uses Python 3.

This course requires prior knowledge of statistics and regression.  We will be covering the basics with an emphasis on safety first. Please be aware that this course uses Python 3.

This course requires prior knowledge of statistics and regression.  We will be covering the basics with an emphasis on safety first. Please be aware that this course uses Python 3.

This course requires prior knowledge of statistics and regression.  We will be covering the basics with an emphasis on safety first. Please be aware that this course uses Python 3

Course Link: https://www.coursera.org/specializations/data-science-python

Data Mining Specialization

Course Link: https://www.coursera.org/specializations/data-mining

Data Mining Specialization
The Data Mining Specialization is a course created by the faculty at UC Berkeley School of Information and has been co-sponsored by the UC Berkeley Computer Science Department. The course will teach you how to use basic data mining techniques to solve some real world problems. Work on a data set and solve some mining problems for a project of your choice. Your completed project will be a data file that can be used to mine for mining applications.

Data mining requires knowledge of basic computer science techniques, algorithms, and programming. The course requires no prior programming knowledge, but it does require a working knowledge of data analysis and visualization. The course is made up of 4 modules, each one revolving around a subject in computer science:

Module 1: Computer Science and Basic Data Mining
Module 2: Intro to Programming in Python
Module 3: Advanced Python Programming
Module 4: Mining for Data in Python

Each module consists of a set of progressively deeper and more challenging problems. The course requires the completion of a daily ‘probabilistic programming’ exercise, in which you attempt to solve a problem from one module to another.

The course is designed for non-native English speakers who are interested in exploring computing and programming in Python. The course is made up of 4 modules, each one revolving around a subject in computer science:

Module 1: Introductory Programming in Python
Module 2: Python’s Symbols
Module 3: The File and FilePath
Module 4: Mining for Data in Python

Each module consists of a set of progressively deeper and more challenging problems. The course requires the completion of a daily ‘probabilistic programming’ exercise, in which you attempt to solve a problem from one module to another.

The course is designed for non-native English speakers who are interested in exploring computing and programming in Python. The course is made up of 4 modules, each one revolving around a subject in computer science:

Module 1: Introduction to Python
Module 2: Basic Data Representation
Module 3: Python’s Symbols
Module 4: Mining for Data in Python

Each module consists of a set of progressively deeper and more challenging problems. The course requires the completion of a daily ‘probabilistic programming’ exercise, in which you attempt to solve a problem from one module to another.

The course is made up of 4 modules, each one revolving around a subject in computer science:

Module 1: Introduction to Programming in Python
Module 2: Python’s Literals and Arrays
Module 3: Functions and Arrays
Module 4: Mining for Data in Python

Weekly Quiz
Data Visualization
Ever wonder how data is generated and used? Take a look at this course and data visualization concepts. In this course, we will learn key principles and techniques for data visualization, focusing on key indicators such as charts, graphs, and plots. We will also cover key concepts such as data privacy and data aggregator, how data is rendered and how different technologies such as data warehousing and analytics can improve visualization. By the end of this course, you will be able to…

– Explain key data visualization principles
– Design effective visualizations for data
– Choose appropriate visualization techniques
– Conceive convincing data-gathering and visualization design presentations
– Write compelling, convincing and compelling visualizations
– Answer reader comments and questionsIntroduction to Data Visualization
Tools for Visualization
Protecting Data
Choosing an Overlay
Data Collection and Processing with Apache Spark
This one-week, accelerated course introduces the data processing concepts, algorithms, and techniques that you need to know to utilize the full power of Big Data analysis. You will learn in detail how to compile data from source, store it, load it, and process it.

At the end of this course, you will…
– Understand how to utilize the Apache Spark library for data processing
– Understand the basic tools and technologies used to retrieve data from sources
– Know how to utilize the Apache Spark Big Data framework for interactive processing
– Know how to utilize various Apache Spark features for processing data
– Know how to utilize the HTTP client libraries including clients and servers
– Know how to utilize the HTTP server libraries including clients and serversNew Map, New MapReduce, Storage Spaces, Streaming, and Benchmarking
Analysis and Benchmarking
New Map, New MapReduce, Storage Spaces, Streaming, and Benchmarking
Data Analysis and Presentation Skills
In this course, you will learn foundational techniques for presenting data and will practice and build skills in presenting data in a variety of formats, including graphic, textual, and visual. You will also explore the concepts

Course Link: https://www.coursera.org/specializations/data-mining

Best Coursera Courses for Data Science

Here is a list of best coursera courses for data science.

1. Introduction to Data Science Specialization

This data science specialization provided by IBM, which include 4 sub courses. In this Specialization learners will develop foundational Data Science skills to prepare them for a career or further learning that involves more advanced topics in Data Science. The specialization entails understanding what is Data Science and the various kinds of activities that a Data Scientist performs. It will familiarize learners with various open source tools, like Jupyter notebooks, used by Data Scientists. It will teach them about methodology involved in tackling data science problems. The specialization also provides knowledge of relational database concepts and the use of SQL to query databases. Learners will complete hands-on labs and projects to apply their newly acquired skills and knowledge.

1) What is Data Science?
2) Open Source tools for Data Science
3) Data Science Methodology
4) Databases and SQL for Data Science

2. Applied Data Science Specialization

This data science specialization also provided by IBM, which include 4 sub courses. This is an action-packed specialization is for data science enthusiasts who want to acquire practical skills for real world data problems. It appeals to anyone interested in pursuing a career in Data Science, and already has foundational skills (or has completed the Introduction to Applied Data Science specialization). You will learn Python – no prior programming knowledge necessary. You will then learn data visualization and data analysis. Through our guided lectures, labs, and projects you’ll get hands-on experience tackling interesting data problems. Make sure to take this specialization to solidify your Python and data science skills before diving deeper into big data, AI, and deep learning.

1) Python for Data Science
2) Data Visualization with Python
3) Data Analysis with Python
4) Applied Data Science Capstone

3. Applied Data Science with Python Specialization

The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have basic a python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data. Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order. All 5 are required to earn a certificate.

1) Introduction to Data Science in Python
2) Applied Plotting, Charting & Data Representation in Python
3) Applied Machine Learning in Python
4) Applied Text Mining in Python
5) Applied Social Network Analysis in Python

4. Data Science Specialization

This data science specialization provides by Johns Hopkins University, which covers the concepts and tools you’ll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. In the final Capstone Project, you’ll apply the skills learned by building a data product using real-world data. At completion, students will have a portfolio demonstrating their mastery of the material. The specialization includes 10 sub courses:

1) The Data Scientist’s Toolbox
2) R Programming
3) Getting and Cleaning Data
4) Exploratory Data Analysis
5) Reproducible Research
6) Statistical Inference
7) Regression Models
8) Practical Machine Learning
9) Developing Data Products
10) Data Science Capstone

5. Data Science at Scale Specialization

This data science specialization provides by University of Washington, which covers intermediate topics in data science. You will gain hands-on experience with scalable SQL and NoSQL data management solutions, data mining algorithms, and practical statistical and machine learning concepts. You will also learn to visualize data and communicate results, and you’ll explore legal and ethical issues that arise in working with big data. In the final Capstone Project, developed in partnership with the digital internship platform Coursolve, you’ll apply your new skills to a real-world data science project. The specialization includes 4 sub courses:

1) Data Manipulation at Scale: Systems and Algorithms
2) Practical Predictive Analytics: Models and Methods
3) Communicating Data Science Results
4) Data Science at Scale – Capstone Project

6. Advanced Data Science with IBM Specialization

This data science specialization also provided by IBM, which include 4 sub courses. As a coursera certified specialization completer you will have a proven deep understanding on massive parallel data processing, data exploration and visualization, and advanced machine learning & deep learning. You’ll understand the mathematical foundations behind all machine learning & deep learning algorithms. You can apply knowledge in practical use cases, justify architectural decisions, understand the characteristics of different algorithms, frameworks & technologies & how they impact model performance & scalability.

1) Fundamentals of Scalable Data Science
2) Advanced Machine Learning and Signal Processing
3) Applied AI with DeepLearning
4) Advanced Data Science Capstone

7. Genomic Data Science Specialization

This data science specialization provides by Johns Hopkins University, which covers the concepts and tools to understand, analyze, and interpret data from next generation sequencing experiments. It teaches the most common tools used in genomic data science including how to use the command line, Python, R, Bioconductor, and Galaxy. The sequence is a stand alone introduction to genomic data science or a perfect compliment to a primary degree or postdoc in biology, molecular biology, or genetics. To audit Genomic Data Science courses for free, visit https://www.coursera.org/jhu, click the course, click Enroll, and select Audit. The specialization includes 8 courses:

1) Introduction to Genomic Technologies
2) Genomic Data Science with Galaxy
3) Python for Genomic Data Science
4) Algorithms for DNA Sequencing
5) Command Line Tools for Genomic Data Science
6) Bioconductor for Genomic Data Science
7) Statistics for Genomic Data Science
8) Genomic Data Science Capstone

8. Data Mining Specialization

This data science specialization provides by Illinois State University, which teaches data mining techniques for both structured data which conform to a clearly defined schema, and unstructured data which exist in the form of natural language text. Specific course topics include pattern discovery, clustering, text retrieval, text mining and analytics, and data visualization. The Capstone project task is to solve real-world data mining challenges using a restaurant review data set from Yelp. Courses 2 – 5 of this Specialization form the lecture component of courses in the online Master of Computer Science Degree in Data Science. You can apply to the degree program either before or after you begin the Specialization. The specialization includes 6 courses:

1) Data Visualization
2) Text Retrieval and Search Engines
3) Text Mining and Analytics
4) Pattern Discovery in Data Mining
5) Cluster Analysis in Data Mining
6) Data Mining Project

9. Data Analysis and Interpretation Specialization

Learn SAS or Python programming, expand your knowledge of analytical methods and applications, and conduct original research to inform complex decisions. The Data Analysis and Interpretation Specialization takes you from data novice to data expert in just four project-based courses. You will apply basic data science tools, including data management and visualization, modeling, and machine learning using your choice of either SAS or Python, including pandas and Scikit-learn. Throughout the Specialization, you will analyze a research question of your choice and summarize your insights. In the Capstone Project, you will use real data to address an important issue in society, and report your findings in a professional-quality report. You will have the opportunity to work with our industry partners, DRIVENDATA and The Connection. Help DRIVENDATA solve some of the world’s biggest social challenges by joining one of their competitions, or help The Connection better understand recidivism risk for people on parole in substance use treatment. Regular feedback from peers will provide you a chance to reshape your question. This Specialization is designed to help you whether you are considering a career in data, work in a context where supervisors are looking to you for data insights, or you just have some burning questions you want to explore. No prior experience is required. By the end you will have mastered statistical methods to conduct original research to inform complex decisions.

1) Data Management and Visualization
2) Data Analysis Tools
3) Regression Modeling in Practice
4) Machine Learning for Data Analysis
5) Data Analysis and Interpretation Capstone

10. Executive Data Science Specialization

Assemble the right team, ask the right questions, and avoid the mistakes that derail data science projects. In four intensive courses, you will learn what you need to know to begin assembling and leading a data science enterprise, even if you have never worked in data science before. You’ll get a crash course in data science so that you’ll be conversant in the field and understand your role as a leader. You’ll also learn how to recruit, assemble, evaluate, and develop a team with complementary skill sets and roles. You’ll learn the structure of the data science pipeline, the goals of each stage, and how to keep your team on target throughout. Finally, you’ll learn some down-to-earth practical skills that will help you overcome the common challenges that frequently derail data science projects.

1)A Crash Course in Data Science
2)Building a Data Science Team
3)Managing Data Analysis
4)Data Science in Real Life
5)Executive Data Science Capstone

11. Other Useful Data Science Common Courses:

1) Data Science Math Skills
2) Data Science Ethics
3) How to Win a Data Science Competition: Learn from Top Kagglers

Posted by BestCourseraCourses, Post Link: https://bestcourseracourses.com/best-coursera-courses-for-data-science/