data mining project report great learning

Learn About the Mining Industry of South Africa

South Africa is rich in minerals, and it supplies a large amount of the world’s production of these minerals thanks to an active mining industry. This country has some of the biggest reserves of gold, manganese, coal, platinum, diamonds, vanadium and chromite ore. Read on for more information.

South Africa’s economy has long been dependent on gold and diamond mining. Gold makes up more than 33 percent of the nation’s exports, and the diamond industry comes in at the fourth spot for worldwide diamond producers. Experts consider South Africa ripe for finding additional deposits of precious minerals and metals, as well as energy and industrial minerals, ferrous minerals, non-ferrous minerals and metals.

South Africa has experienced growth in secondary industries in recent years, which has led to the decline of mining’s relative contribution to the nation’s gross domestic product. Even so, the mining industry continues to be an important contributor to South Africa’s economy, creating jobs, making up about 18 percent of the gross domestic product and earning more than 50 percent of foreign exchange.

Gold Mines in South Africa

Gold was discovered in South Africa in the late 1800s, and this discovery led to the development of Johannesburg and several other cities. South Africa’s Witwatersrand Basin is the biggest gold resource in the world. This basin covers three provinces in South Africa, although gold is found only on the north and west margins.

Gold mining began to suffer after the turn of the 21st century. Mines were forced to dig deeper, and gold prices dropped. That being said, gold mining continues to make up an important part of the employment sector.

Coal Mining in South Africa

Coal mining also began in South Africa in the late 1800s, in many of the same areas where gold was discovered. Coal mines are located in northeastern South Africa. The nation produces coal with a medium ash content. To prepare some of the coal for export, it’s washed to make it a higher grade. Some of the coal is left at a lower grade for power station boilers owned by the South African public electric utility.

Initially, coal mining was a private industry. This changed around 1970, when foreign oil companies looking for coal became involved with the market, creating new coal mines. Currently, coal mining comes in as the third largest mining industry for South Africa, following gold and platinum.

The Diamond Mining Industry in South Africa

The diamond mining industry in South Africa has existed since the 1860s. The first diamond was found near Hopetown, weighing 21.25 carats. Alluvial deposits were where the first diamonds were discovered, and later a large number of diamonds were discovered at the Cullinan mine.

Diamond mining in South Africa is considered to have a solid future, and the nation is engaged in expansion of its three biggest mines, which are expected to remain active for 25 to 50 more years.

Future Opportunities for Mining

Due to the importance of mining to South Africa’s economy, the nation will continue to prioritize operations, as well as exploration and discovery of new deposits. Using drones with geothermal imagery might eliminate geological drilling and sampling, which could enhance efficiency and reduce costs. Robotic technology may replace human workers, which should improve worker safety and help reduce negative impacts on the environment.

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Data Mining

Learn Data Mining from basics in this free online training. This free Data Mining course is taught hands-on by experts. Learn about Data Description, Data Manipulation, Skewness & a lot more. Best for Beginners. Start now!

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What you learn in Data Mining ?

About this free certificate course.

This Data Mining course will introduce you to prominent Data Mining concepts. The course begins by introducing you to data description concepts. You will understand the basics of data, data manipulation, and skewness using histograms in the first half of the course. You will then learn to visualize outliers using boxplots, correlation using scatter plots, and understand what machine learning is. You will also understand regression analysis, multiple linear regression, and logistic regression, with demonstrated examples in the latter part of this course. There is an assessment to evaluate your knowledge at the end of the course. Complete the course for free and avail your certificate. You can also study the attached materials for reference.   

After this free, self-paced, intermediate-level guide to Data Mining, you can enroll in the Data Science course and embark on your career with the professional Post Graduate certificate. Learn various concepts in depth with millions of aspirants across the globe!

• Course Outline

You will learn mathematics concepts for data mining tasks such as statistics, its types, population, parameter, sample, mean, median, mode, normal distribution, interquartile range of IQR, and its upper and lower limits. This section comprehends a demonstration of the outlier concept at the end of the course for your better understanding.

You shall understand data and learn to infer insights from the datasets using the diabetes dataset in this section.

This section explains how to work with or manipulate the data with different methods in a given set to extract a particular range of values. You will also understand how a dataset not showing accurate data can be recognized and be replaced with the median since it does not get affected by outliers.

You shall understand the outlier concept in-depth in this section. You will learn to detect and impute outliers and understand their working later in this section. You will also learn to infer/express data using the histogram. 

You will learn to express missing data and express data in box plots for simple representation and also understand outlier analysis concepts in this section. 

You will learn to represent correlation with different methods and scatter plots or heat maps using automobile dataset to perform exploratory data analysis in this section. 

This module begins by defining machine learning. It then discusses how a machine understands the tasks with examples and explains supervised and unsupervised learning concepts in machine learning. 

This section shall define regression, brief different types of regression, and then explain what regression analysis is in machine learning. You will learn to work with regression analysis to understand the data better. 

This section shall explain simple linear regression. You will learn to import classes and packages and work with Google Colaboratory to understand linear regression better. 

 You will understand the concept of multiple data points, to begin with in this section and then learn to work with multiple linear regression with NumPy.

You will learn to work with the dataset by understanding a project on Salary Prediction. You will also learn to work with NumPy, Pandas, and Matplotlib library for the project. 

You will learn a supervised learning technique to classify the data based on the classifying points and logistic regression in this section. 

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Frequently Asked Questions

What are the prerequisites required to learn the Data Mining course?

Data Mining is an intermediate-level course. Before you begin with this course, you will have to do a little homework on data science if you do not have a thorough understanding of it.

How long does it take to complete this free Data Mining certificate course?

 This free certificate course is 2.5-hours long. You can learn Data Mining concepts and work at your convenience to understand the subject since the course is self-paced. 

Will I have lifetime access to this free course?

Yes, once you enroll in this Data Mining course, you will have lifetime access to this Great Learning Academy's free course. You can log in and learn at your leisure.

What are my next learning options after I complete Data Mining?

Once you complete this free course, you can follow up with the data mining process and data mining applications, or you can also opt for a Master's in Data Science that will aid in advancing your career growth in this leading field

Is it worth learning Data Mining?

Yes, it is beneficial to learn Data Mining. Different techniques are used to understand data to work on projects and building models. These techniques contribute to analyzing data through identifying patterns and relationships to solve business problems. 

What are Data Mining tools used for?

Data mining tools help data miners discover patterns, trends, and groupings within a huge dataset and transform the data into more structured information. Popular data mining tools include MonkeyLearn, RapidMiner, Oracle Data Mining, and IBM SPSS Modeler. 

Why is Data Mining so popular?

Data mining is a simple technique that opens data science, artificial intelligence, and machine learning professionals to business opportunities since it can be leveraged for predictive and descriptive abilities. The predictive and descriptive capabilities of data mining can predict the future trend and also heighten profits. 

Will I get a certificate after completing this free Data Mining course?

Yes, you will get a certificate of completion for the Data Mining course after completing all the modules and cracking the assessment/quiz. All the assessment tests your knowledge and badges your skills.

What knowledge and skills will I gain upon completing this course?

You will understand data manipulation, skewness, visualization, machine learning, and regression analysis concepts. You will learn to work with different representation techniques for visualization purposes and be able to work on projects efficiently after you have completed this data mining course.

How much does this Data Mining course cost?

It is an entirely free course from Great Learning Academy. Anyone interested in learning data mining techniques for data science, big data, artificial intelligence, and machine learning concepts can get started with this course. You can also refer to the attached materials for additional knowledge. 

Is there any limit on how many times I can take this free course?

Once you enroll in the Data Mining course, you have lifetime access to it. So, you can log in anytime and learn it for free online at your convenience. 

Can I sign up for multiple courses from Great Learning Academy at the same time?

Yes, you can enroll in as many courses as you want from Great Learning Academy. There is no stricture to the number of courses you can enroll in at once, but since the courses offered by Great Learning Academy are free, we suggest you learn one by one to get the best out of the subject. 

Why choose Great Learning Academy for this Data Mining course?

Great Learning is a global educational technology platform committed to developing skilled professionals. Great Learning Academy is a Great Learning project that provides free online courses to assist people in succeeding in their careers. Great Learning Academy's free online courses have helped over 4 million students from 140 countries. It's a one-stop destination for all of a student's needs.

This course is free and self-paced, but it also includes solved problems, demonstrated codes, sample projects, and presented examples to help you comprehend the numerous areas that fall under the subject and awards you a certificate to showcase your skills. The course is conducted by topic experts and is carefully tailored to cater to both beginners and professionals.

Who is eligible to take this course?

Anybody interested in learning the Data Mining concepts and techniques for Data Science and AIML can take up the course. So, enroll in our Data Mining course today and learn it for free online.

What are the steps to enroll in this Data Mining course?

Enrolling in Great Learning Academy's Data Mining course is a simple and straightforward approach. You will have to sign-up with your E-Mail ID, enter your user details, and then you can start learning at your own pace. 

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Data mining course.

Data Mining, alternatively known as Knowledge Discovery in Database(KDD), is considered the most beneficial technique that helps researchers, entrepreneurs, and individuals to extract valuable insights from the collected dataset. It includes processes like Data cleaning, Data cleaning, integration, selection, transformation, mining, pattern evaluation, and knowledge presentation. This technique, where we extract the information to identify the trends, patterns, and helpful information that allows businesses to make data-driven decisions through Data Mining, is proven to be advantageous for their growth.

Data Mining can also be considered as a type of investigation where we try to find the hidden patterns and information from various categories of data collected. These data are stored in particular areas like data warehouses. Its efficient analysis and data mining algorithms help in decision-making and other helpful information, resulting in cost-cutting and generating revenue. Data Mining has advanced techniques to find the patterns and trends from the storage of large amounts of data. It utilizes complex mathematical algorithms to evaluate large data for future trend predictions. Many Businesses use Data Mining techniques to extract specific data from vast data storage.

Data Mining is similar to Data Science. The Data Mining process includes different types of services like text mining, video mining, web mining, social media mining, and pictorial data mining. Softwares are used to achieve Data Mining more efficiently. There are many high-end software through which you can locate data that is hard to find manually. Through Data Mining, you can predict your Business goals, identify data, prepare data, model and evaluate data, and present the data. You can perform Data Mining on various types of data like Relational Database. It is a collection of multiple data sets organized by tables, columns, and records. You can extract the required data and access the information you are looking for from the databases through these databases. 

When you search the required data from the databases, tables convey and share the information that facilitates the data searching, reporting, and organization. Data Warehouse is also a type of data that helps in Data Mining. A data warehouse in a business is a technology that collects all the relevant data and provides valuable insights. Marketing and financial domains offer a lot of data from multiple places that can be stored in Data Warehouses. These extracted data are analyzed and are utilized for decision-making for businesses. Data Warehouses are designed for data analysis purposes. Many organizations are making use of Data Warehouses for data storage and analysis.

There are many advantages of Data Mining. Its techniques enable businesses to get knowledge-based information, and it also helps them make lucrative changes in the operation and production processes. Data Mining is more cost-efficient than other statistical applications. It also allows businesses to make crucial decisions regarding their growth. It helps them to uncover various patterns, trends, and behaviors. Data Mining can be done and implemented on the new system or the existing ones. Data Mining is quicker than other methods and helps you analyze enormous data sets in a shorter period. Data Mining is considered an excellent technique to analyze and manipulate data.

You can find many Data Mining applications in health care, education, fraud detection , CRM, manufacturing engineering, financial banking, lie detection, market basket analysis, and more. Data Mining is very useful for businesses with intense consumer demands such as retail, financial, communication, marketing agencies, sales, etc. It also helps the companies predict what products the customers need and their preferences. Data has the power to determine future events and to uncover them Data Mining plays an important role. It helps many organizations realize the method to develop and promote their data in favor of customers, which in return brings them revenue.

Data Mining also faces some challenges during its implementation. These challenges can be related to the techniques, data, methods, performance, etc. Data Mining is effective if you tackle all these challenges that arise during its execution appropriately. It must be correct and complete to get useful insights from the collected data. Sometimes these incomplete and noisy data create havoc while Data Mining as you will be dealing with a large amount of data. These problems may also occur because of human mistakes or the data measuring instruments. Sometimes these large data can be inaccurate and unreliable. It is difficult to collect data from customers who are unwilling to provide their information, making data incomplete.

To enter Data Mining in-depth, you must first get a brief introduction to Data Mining. You should go through various Data Mining examples to understand its mechanism. It will also help you face the challenges that come during its execution. These challenges may be due to incomplete and noisy data, data distribution, complex data, performance, data privacy and security, data visualization, and more. Data Mining involves refined data analysis tools, and it helps you find previously unknown patterns and relations in the vast data sets. The tools get help from statistical models, mathematical algorithms, and Machine Learning techniques like neural networks or decision trees to analyze these data. Hence, Data Mining involves analysis and prediction.

With the help of advanced statistics, mathematics, and Machine Learning techniques, Data Mining has become more effective and efficient. It involves Machine Learning, database management, and statistics, and professionals aim to understand these techniques and how to process them in their favor. Many have made Data Mining their career, and it is also in demand as we live in a data-driven world. From the recent Data Mining projects , developers have come across various Data Mining techniques like Classification, Clustering, Regression, Outer, Sequential Patterns, Prediction, and Association Rules. All these Data Mining techniques make Data Mining more efficient and effective, and it helps professionals improve Data Mining performances. 

To learn all the types and techniques of Data Mining, enroll in a free Data Mining course offered by Great Learning. Register in this course and get in-depth knowledge of Data Mining and its mechanism. Complete the course to get free Data Mining certificate and grab more job opportunities.

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Data Mining Project

This course is part of Data Mining Foundations and Practice Specialization

Taught in English

Some content may not be translated

Instructor: Qin (Christine) Lv

Financial aid available

1,688 already enrolled

Recommended experience

Intermediate level

data science professionals or domain experts, some experience working with data, completion of Data Mining Pipeline and Data Mining Methods

What you'll learn

Identify the key components of and propose a real-world data mining project.

Design and develop real-world solutions across the full data mining pipeline.

Summarize and present the key findings of the data mining project.

Analyze the overall project process and identify possible improvements. 

Skills you'll gain

• data mining project design and development
• data mining project process analysis and improvement
• data mining project summary and presentation
• data mining project formulation

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There are 4 modules in this course

Data Mining Project offers step-by-step guidance and hands-on experience of designing and implementing a real-world data mining project, including problem formulation, literature survey, proposed work, evaluation, discussion and future work.

This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder Course logo image courtesy of Mariana Proença, available here on Unsplash: https://unsplash.com/photos/_WgnXndHmQ4

This week provides you with a general introduction of the Data Mining Project course from the architect's perspective, focusing on the the initial brainstorming of project ideas which will prepare you for the rest of the course.

What's included

11 videos 3 readings 2 discussion prompts

11 videos • Total 126 minutes

• Meet Your Instructor! • 4 minutes • Preview module
• Preparing for this Specialization • 9 minutes
• Introduction to Data Mining Project • 13 minutes
• Data Mining Project: Four Views • 12 minutes
• Project Scoping and Brainstorming • 8 minutes
• Overview of Data Mining Projects • 17 minutes
• Mobile/Wearable/IoT Sensing Projects • 10 minutes
• Data Fusion & Anomaly Detection Projects • 15 minutes
• Earthquake & NBA OSN Analysis Projects • 13 minutes
• Presentation: Purpose and Planning • 9 minutes
• Presentation: Slides and Presenting • 11 minutes

3 readings • Total 30 minutes

• Earn Academic Credit for Your Work! • 10 minutes
• Course Support • 10 minutes
• About This Course • 10 minutes

2 discussion prompts • Total 190 minutes

• Introduce Yourself • 10 minutes
• Project Ideas • 180 minutes

Project Proposal

This week discusses in detail what should be included in your project proposal and ends with an opportunity to craft your own.

7 videos 1 peer review

7 videos • Total 92 minutes

• Project Proposal 1 • 12 minutes • Preview module
• Project Proposal 2 • 13 minutes
• Project Proposal Submissions • 11 minutes
• Project Proposal Review 1 • 12 minutes
• Project Proposal Review 2 • 11 minutes
• Example: Solar Farm Project • 11 minutes
• Example: OSN Hazards Project • 19 minutes

1 peer review • Total 180 minutes

• Your Project Proposal • 180 minutes

Project Checkpoint

This week focuses in on checking the status of your project. After reviewing your project, you will take some time to incorporate the progress you've made with updates to your initial proposal.

3 videos 1 peer review

3 videos • Total 49 minutes

• Project Checkpoint 1 • 15 minutes • Preview module
• Project Checkpoint 2 • 17 minutes
• Project Checkpoint Review • 16 minutes
• Checking Your Project • 180 minutes

Final Project Report

This week discusses in detail the final project report, highlighting the importance of summarizing the key findings and analyzing the overall project process.

4 videos 1 peer review

4 videos • Total 51 minutes

• Project Final Report 1 • 13 minutes • Preview module
• Project Final Report 2 • 14 minutes
• Project Final Report Review 1 • 14 minutes
• Project Final Report Review 2 • 9 minutes

1 peer review • Total 240 minutes

• Your Project's Final Report • 240 minutes

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Data Science Foundations: Statistical Inference

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Applications of Software Architecture for Big Data

Resampling, Selection and Splines

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What is a cross-listed course.

A cross-listed course is offered under two or more CU Boulder degree programs on Coursera. For example, Dynamic Programming, Greedy Algorithms is offered as both CSCA 5414 for the MS-CS and DTSA 5503 for the MS-DS.

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· Your transcript will be affected. Cross-listed courses are considered equivalent when evaluating graduation requirements. However, we encourage you to take your program's versions of cross-listed courses (when available) to ensure your CU transcript reflects the substantial amount of coursework you are completing directly in your home department. Any courses you complete from another program will appear on your CU transcript with that program’s course prefix (e.g., DTSA vs. CSCA).

· Programs may have different minimum grade requirements for admission and graduation. For example, the MS-DS requires a C or better on all courses for graduation (and a 3.0 pathway GPA for admission), whereas the MS-CS requires a B or better on all breadth courses and a C or better on all elective courses for graduation (and a B or better on each pathway course for admission). All programs require students to maintain a 3.0 cumulative GPA for admission and graduation.

Can I take cross-listed courses to fulfill my degree requirements?

Yes. Cross-listed courses are considered equivalent when evaluating graduation requirements. You can identify cross-listed courses by checking your program’s student handbook.

How do I upgrade and earn credit from CU Boulder?

You may upgrade and pay tuition during any open enrollment period to earn graduate-level CU Boulder credit for << this course/ courses in this specialization>>. Because << this course is / these courses are >> cross listed in both the MS in Computer Science and the MS in Data Science programs, you will need to determine which program you would like to earn the credit from before you upgrade. 

MS in Data Science (MS-DS) Credit: To upgrade to the for-credit data science (DTSA) version of << this course / these courses >>, use the MS-DS enrollment form. See How It Works Opens in a new tab .

MS in Computer Science (MS-CS) Credit: To upgrade to the for-credit computer science (CSCA) version of << this course / these courses >>, use the MS-CS enrollment form. See How It Works Opens in a new tab .

If you are unsure of which program is the best fit for you, review the MS-CS Opens in a new tab and MS-DS Opens in a new tab program websites, and then contact [email protected] Opens in a new tab or [email protected] Opens in a new tab if you still have questions.

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B3155 - PROJECT WORK IN MACHINE LEARNING AND DATA MINING

Course unit page.

Teacher Claudio Sartori

Teaching Mode Traditional lectures

Language English

Campus of Bologna

Degree Programme Second cycle degree programme (LM) in Artificial Intelligence (cod. 9063)

This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.

Academic Year 2023/2024

Learning outcomes.

At the end of the course, the student is able to apply the knowledge acquired in Machine Learning in order to carry out autonomously a project focusing on a topic agreed upon with the teacher.

Course contents

Three possible tracks:

• A scientific paper is considered as the base for the development of a new data mining algortihm. The software engineering best practices have to be used to include the project into an open-source framework.
• An innovative software/tool is chosen for experiments and tests on given datasets.
• The professor assigns a dataset and a mining task to be developed on it, or the students proposes a dataset that can be accepted by the professor.

If you follow track 3 do the following:

• visit the Kaggle [https://www.kaggle.com/] website;
• choose three competitions of your interest, either active or closed; the requirements are to have not less than some thousands of data units and not lass than two data files of different structure/content type to be considered (train and test data count only once)
• send and email to the teacher with the subject "[Project work] Kaggle competitions choice" and containing the link to the three competition of your interest
• wait for the answer and, when the competition is assigned, do as specified on the "Assessment methods section below"

Readings/Bibliography

Scientific papers and documentation provided by the teacher

Teaching methods

The teacher assigns a project , to be developed individually or by a very small group; tutoring will be available on demand.

Assessment methods

This activity is mainly oriented to develop abilities based on the knowledge acquired in the related course.

After the assignment of the task by the teacher:

• create a github folder named “surname-projecttitle” and share it with the teacher;
• create a readme.md describing the project, with objectives, results, pertinent references, and anything else that may be useful for understanding
• the folder must contain a readme.md with the installation and startup notes, and the prerequisites for operation
• the solution should produced as a python notebook with an adequate amount of comments and program outputs, in order to constitute an adequate report of the project development (export the notebook also as a pdf in landscape format); if the software is plain code, instead of a notebook, it is necessary to add a report, written in latex, in landscape format, deliver latex and pdf.
• the software must be commented in English, the names of the variables must be meaningful and inspired by English, filenames must start with the project title, possibly a shortcut
• the .pptx or .tex with any complementary files
• agree with the teacher a date for the presentation and possible demonstration of the software
• at any time it is possible to consult the teacher by e-mail or by requesting an appointment
• delays are not a problem, please, at the expiry of the three months, notify via e-mail the need to continue

Teaching tools

• Python Notebooks

Office hours

See the website of Claudio Sartori

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