Data Mining Project

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

Data Mining Project
In this project-based course, you will take the first step towards exploring data mining and extracting valuable information from it. You will work on a data mining project of your own invention. We will use a simple text editor (MTL) and explore the topic through videos, slides and handouts. You will learn the basics of mining for data sets by state, county, city, state, zip, country, and even some basic algorithms. You will also learn the basics of extracting information from data sets by county, city, state, and county. You will also learn about typical data sets that are mined and how to use various tools to find interesting information in them. The course usually runs about 4-5 hours with 4-5 hours spent on each of the 3 topics discussed.Module 1: County Data Sets
Module 2: State Data Sets
Module 3: City Data Sets
Module 4: Country Data Sets
Data Analysis and Presentation Capstone
The Data Analysis and Presentation Capstone is an opportunity to apply the skills and knowledge you gained during the Data Analysis and Analysis Project.

You will have the opportunity to work side by side with another data analyst or data scientist as they work on the same project. They will explore data in a similar fashion as you did, but work in a controlled environment. They will use common data analysis techniques and algorithms that you have learned in this course. They will also have the opportunity to ask questions of you as you go about your project, which will demonstrate your mastery of the material.

After completing this course, you will be able to:
1. Explain why data and analysis are important
2. Use a data analysis tool to find interesting or valuable information
3. Use familiarity with programming to analyze and present data
4. Use knowledge of machine learning and statistics to find new or different types of information
5. Explain what machine learning and statistics are and how they can be used
6. Use common statistical techniques to find information
7. Use presentation skills to present data efficiently and effectively
8. Use presentation skills to present data graphically and effectively
9. Use presentation skills to present information in a dynamic and engaging way
10. Use presentation skills to present data in a way that integrates concepts from different topics in data scienceIntroduction and Overview of Data Analysis
Machine Learning and Statistics
Classification of Data and Formulas
Data Management and Visualization
The capstone is the culmination of the Data Analysis and Presentation Project and the specialization. In this part of the specialization, you will continue to explore and apply the concepts learned and concepts you have been developing throughout the specialization. You will use the tools and techniques you’ve learned in the course to create visualizations and dashboards. You will apply these techniques to data storage

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

Process Mining: Data science in Action

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

Process Mining: Data science in Action
Learning how to mine for data is one of the most important steps in any data science course. It is also one of the most involved. Data mining involves data analysis, exploration, and management. You will learn all the steps needed to design a mining strategy, implement it, tune it, and manage an organization of data miners.

This course will introduce you to data science and how data are used to mine for interesting and valuable information. You will learn the general concepts and practices of data science along with basic data analysis techniques. You will also learn how to use data to mine for interesting and valuable information. This course will also cover the various data science techniques that are taught in the Mining Data Science Specialization.

This is the third and last course in the Data Science Specialization. The Specialization is the specialization of courses that focus on a specific aspect of data science, and this specialization is the continuation of that aspect’s research.Module 1: Data Analysis and Mining
Module 2: Mining Strategies
Module 3: Tuning and Managing Data
Module 4: Data Science in Action
Performing and Presenting at the College and University Level
In this course you will learn how to develop your skills in audience analysis, negotiation, and presentation. You will learn how to develop your interpersonal and communication skills and to design a presentation. You will also learn the tools to perform and present at the college and university level.

At the end of this course you will be able to:
1. Survey your audience
2. Design a successful presentation
3. Use persuasive speech and gestures to influence others
4. Use appropriate vocabulary to convince others to do something they don’t want to
5. Present information effectively
6. Design a persuasive speech and gesture
7. Use appropriate vocabulary to convince others to do something they don’t want to
8. Present information effectively
9. Use appropriate vocabulary to convince others to do something they don’t want to
10. Use appropriate vocabulary to convince others to do something they don’t want to
11. Use appropriate vocabulary to persuade others to do something they don’t want to
12. Use appropriate vocabulary to convince others to do something they don’t want to
13. Use appropriate vocabulary to persuade others to do something they don’t want to
14. Use appropriate vocabulary to convince others to do something they don’t want to
15. Use appropriate

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

Pattern Discovery in Data Mining

Course Link: https://www.coursera.org/learn/data-patterns

Pattern Discovery in Data Mining
This course introduces pattern discovery and exploration of data using a broad range of natural language processing techniques. The course covers the topics of orthographic coordinates, orthographic graph theory, and story theory in the context of analyzing graph data. The course introduces the Python programming language and a variety of programs that can be used to explore and visualize graph data, as well as the NN framework. The course is intended to cover the very basics of pattern discovery and exploration, using the Python libraries and the NN framework to implement many of the key sub-routines used. The course is intended to cover the very basics of graph data, using the Python libraries and the NN framework to implement many of the key sub-routines used.

This is the fourth and last course in the Data Mining specialization that explores the many facets of data mining. The goal of this specialization is to deep dive into the data mining process, allowing students to apply the learn-by-doing approach to data mining. The course will also allow students to explore the full range of ways in which data and graph mining are related, including the computation of structural similarity measures, the use of trees and random forests, and the exploration of a variety of other algorithms.

Good luck as you get started!Welcome and Setting Up the NN Framework
Functions & Programming
Graphs & Trees
Rasterization & Random Forests & Other Algorithms
Pattern Discovery
This course introduces the basic concepts of pattern discovery, and the programming models and patterns that are used to follow your patterns. The emphasis of the course is on the analysis and manipulation of data, which is the focus of the specialization. The course is split up into four modules, with a set of practice exercises in between. Throughout the course, you will learn about the basic concepts of pattern discovery, and will practice the manipulation, manipulation, and analysis of graph and string data. The course will also cover the various algorithms and techniques that are used to follow patterns, including searching for clusters and patterns, character classification, clustering, visual inspection, and pattern exploration and validation.Week 1: Beginner’s Guide to Graphs and Strings
Week 2: Hunched Over Analysis
Week 3: More Hints at Clusters & Patterns
Week 4: Regular Expression & Matching and Testing
Palliative Care: A Global Perspective
Palliative care provides critical support to those suffering from severe and life-limiting pain. This course explores the many facets of palliative care, the various areas of specialized clinical practice, and the important ethical and philosophical debates that arise from the current practice of palliative care.Course Introduction
Pain Management
Acute and Critical Care
Palliative Care in Palliative Care
<|start Course Link: https://www.coursera.org/learn/data-patterns

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

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