data mining


Data mining is a process of data science technology. The data science technology works completely on the data so do the data mining. In this technology, many processes are involved, but data mining is said to be the most important one. In the data mining process, the collected data is analyzed for discovering the hidden patterns in it. The data is collected before analysis. The data which is collected is in impure form. This means that all the data which is accumulated is not of use. We have to use only useful information from it. That’s why data mining is done.

The data mining process discovers the hidden patterns from the data which is further used for solving the problems. The data is divided into sub-parts and then data mining is performed on them. After the process, the data is integrated again. All these tasks are executed and accomplished at the place called data warehouses. The data mining process is majorly used by companies. This is so because this process helps in reducing the overall cost with higher revenue.


Here the steps followed in the data mining process are described precisely. They are listed below:

  • The data is first collected from the people with the help of different methods. 
  • After the collection of the data, it is transformed. 
  • After the transformation, the data is loaded into the data warehouses. 
  • When the data reach data warehouses, then it is managed and stored in the multidimensional databases. 
  • When the data is stored in the databases, the authorization is provided to the data scientists or data analysts for accessing the data. This is done with the help of application software. 
  • The data is then finally represented into easier and readable forms. For example, graphs, charts, etc.


At present, the companies are implementing the data mining process for making their business more efficient. Of course, the data mining process also provides high revenues at a low cost. This is another reason for using data mining in companies.

The first step of the companies is to gather the required data. Generally, the data of the company is classified into three types which are:

  • Metadata
  • Transactional
  • Non-operational 

The transactional data covers that data on which operations are performed on each day. For example, costs, inventory, sales, etc. Non-operational data includes the data of forecasting etc. While the metadata consists of the logical designs of the databases.


The most important thing for a company is the customers. For the success of a company, the company must have a strong base of customers. It is very important to keep your customers attracted to your company. Many companies fail to do so because they don’t know about what products their customers are looking for? Many companies are very slow. They are slow in understanding the needs of their customers. When a company slowly understands the needs of the customers, then they start to deploy the products of poor quality which are not appreciated and liked by the customers.

The data mining process helps in understanding the need of every customer by discovering hidden patterns from the data and by deep analysis of the data. One strong example of this is when looking at adult dating and hookup applications. Free sex apps like Meetnfuck App utilized data from users on other top casual sex sites in order to tailor their platform to better fit the needs of their target demographic. The examples of data mining to better understand customers are endless. Another example of this would be food delivery applications. We get many offers on food items every day. What these food delivery companies do is analyze the shopping behavior of every customer. They analyze that which food item is frequently purchased by which customers. Then they make the clusters of those customers who purchase the same food items. Then they provide special offers to the customers accordingly. In this way, the data mining process help in building a strong customer base.


The companies invest a huge amount of money in the marketing and advertisement campaigns of their products. Even after this, many of them fail in promoting their products. This is so because the marketing and advertisements are not reached to the right audience. As said above, the data mining process help in understanding the needs of the customers. After knowing the needs of the customers, companies can easily deliver the products. The data mining process helps in keeping an eye on the online activities of the customers. For example, for what products they are searching for, which products or types of products are liked by the customers, etc.

The data mining process helps in doing the advertising and marketing to the targeted audience. This activity results in a lower cost with an efficient result. The best example of this would be Netflix. If you have the subscription of Netflix then you would better know that this application suggests next movies or series watch after finishing one. Netflix does so by analyzing the past data of its customers. It analyzes the past data as well as the last movies or series you have watched. Based on that analysis, it gives new suggestions. This activity also helps in building good relations with customers. That’s why data mining is being used by many companies around the world.


The companies have one more reason to use data mining in their business which is risk management. The data mining process prevents the company from many risks. The data mining process for risk management is more useful for financial institutions. The companies have to face many fake people who do not return the borrowed money. This activity leads the company to debt. With the help of data mining, the companies analyze the past data of their customers. The analysis of the internal data is preferred by the companies first. After the analysis, they decide whether it is safe to approve the loan to the applicant or not. Along with this, the data mining process also increases the quality of the tools which are used in risk management.

What is Data Science?

Data science is a common term used in various fields these days because it is gaining more importance due to several reasons. It is a type of study meant for obtaining meaningful insights from data with a combination of programming skills, domain expertise, business skills, and statistics. Data scientists will use machine learning algorithms with artificial intelligence applications that can perform several tasks that need human intelligence. In addition, they generate insights that add more value to a business. The demands of data scientists are increasing day by day in the markets and they will get jobs in a company with a high salary.

What is the significance of data science?

Several companies today utilize digital spaces that deal with structured and unstructured data. As a result, they want to remain competitive in the markets for a long time to earn more revenues. With data science, it is possible to develop the big data required for development and implementation purposes. Data science is a blend of several structures including machine learning principles thereby showing ways to explore the hidden patterns from the raw data. It provides methods to determine the predictions and make decisions with deep analysis.

What are the advantages of data science?

The primary advantage of data science is that it helps to improve the products and services of a company based on customer feedback. A business cannot survive in the markets unless it has a robust customer base. Data science enables businesses to learn more about the buying trends of customers in detail thereby showing to make changes accordingly. A risk management plan is necessary irrespective of the industry and volume. Businesses can get solutions for risk management problems with big data analytics for running them without any difficulties.

Learning more about the life cycle of data science

Most companies will make mistakes in data collection and analysis without understanding the needs properly. Therefore, it is necessary to learn more about the life cycle of data science in detail for meeting essential requirements. It involves six phases enabling companies to focus more on their goals with high accuracy. They include discovery, data preparation, model planning, model building, operationalization, and communication which help to get optimal results. All phases play an important role in case studies letting companies find solutions for a problem with desired outputs. Moreover, they give ways to take a business to the next levels that can generate more revenues.

Major challenges faced by data scientists

Data science is growing in different parts of the world because it contributes more to improve decision-making skills and other things. A majority of data scientists face many challenges when they deal with data. Some of them include multiple data sources, data quality, data quantity, predictions, and not identifying the issues properly. Therefore, data scientists should know to manage them with ease for overcoming complications. They can create meta-algorithms which ultimately paves ways to generate data from others with similar results but different data sets.

How businesses can leverage data analytics?

Businesses can leverage data analytics with professionals who have a wide range of skills which ultimately help to attain top positions in the markets. Data science is an ideal one for all sizes of businesses to understand the current state of business thereby helping to build a solid foundation to predict future outcomes. This, in turn, gives ways to develop a product that perfectly matches the market needs. It even enables a business to target potential customers with customized advertisements. Targeting by using data can be very precise and specific as detailed by in their blog about hookup apps and how they target users based on location. Data analytics can help companies to streamline their operations significantly which maximizes the profits.

Things to consider while hiring a data scientist

Companies that are in need of data scientists should consider certain important things before hiring them. Some of them include the purpose, profiles, qualifications, previous experience if any, building a data-driven culture, and so on. It is necessary to evaluate the skills of data scientists in detail with special attention to get more ideas. Another thing is that it makes feasible ways to find the best one among them that can help to make the projects a successful one. A company should give more importance to a data scientist who contributes more to the development and growth.

Best Online Data Science Courses

Data Science courses are offered by many online platforms from different universities and companies. Each course has unique features that benefit people looking for a data science course online. Here are the best online Data science courses which do not need any prior programming experience. Its features are also included.

IBM Coursera Professional certificate course

Professional Data Science course from IBM is specifically designed for learners to adapt to the working environment. Real-life examples are explained and experienced by learners. Positive results are obtained from those who are opting for a career change. Learners looking for better and fun options can easily learn from the basics. It is from 3 to 7 weeks per sub course. The complete course includes 9 sub courses. Upon completion, the learner is well equipped with real-time situations to compete with the experienced data scientists. With certain consistency and motivation, this course certification can be helpful for career growth.

MIT edX certification course


Comparatively, this is a smaller course with 5 sub-courses. Data science taught here is mostly based on machine learning and statistics. Big data has been in demand in recent years and is included in this course with statistical data and probability. The basic concepts taught here are different, as it is more beneficial to large global companies where millions of data are involved. The decision-making skills are highly complex having ample data. It highly involves playing with numbers and analyzing to get conclusions. The basics become stronger after the completion of the course. MIT has the curriculum which is hypothetical yet close to solving real-life problems.

Harvard University edX certification course 

The courses offered by Harvard are recognized worldwide. The quality is equal to learning from the college itself. Data science certification from edX is affordable and has additional value for career growth as the content taught is updated. Data Science taught here is through machine learning and R language. These are two main programming languages through which data science is high on demand. Knowing programming basics helps in moving forward through the course easily. People without a programming background can pick up skills with more practice and consistency. The way of learning here is with probability or imagination. For more experience, the learner might need practice from different programming skills. This has 9 sub-courses with 8 weeks each to complete. The course enhances programming skills.

UC SanDiego edX certification 

UC SanDiego offers a micro Master’s program in Data Science. The package consists of all kinds of data science in different fields taught by many teachers from different areas. Python, probability, Big data, statistics, and machine learning are the things through which data science is practiced. As the certification is equal to a Master’s program, the concepts go deep into the subjects. It allows learners to explore data science. It is highly suggested for those who are into the subjects and want to dive into large data. It is a lengthy course, taking more than a year. The validity of the certification stays long giving an extra level of educational qualification.

University of Michigan Python certification

The data science course offered by the University of Michigan is solely on Python. It is the easiest way to learn data science which became a trend among engineers. Knowing the basics of Python is crucial, as the course only has problems solving with Python. It is one of the easiest programming languages where many programmers enjoy coding. It might seem extra effort, but the process is smooth and less stressful compared to other data science courses. It helps in being an expert to become a data scientist in a specific area. It takes 5 months to complete the certification course with each sub course 7 weeks each.

Stanford University Coursera certification

Machine learning certification offered by Stanford University on Coursera is a highly advanced data science course. Baidu AI group, former head of Google Brain and Andrew NG founded this course to enhance the learning experience for learners. The concepts taught are based on machine learning. The industry is expected to go big in the future. Machine learning is a wide concept and covering every detail almost impossible. This course extends to making simple robots. Passionate young learners are seeking this course to make the most of the technology by living in any part of the world.

Choosing the best course 

Choosing the best suitable course is entirely dependent on the learner. It is important to make time and spend money valuably. Most of the mentioned courses can be completed for free without certification. For those who want certification for high profile jobs need to make sure they are comfortable with the course. Certification needs completion of the course within the minimum required time. Each website has its own rules and regulations for certifications. Visit the main websites to know.