1- An overview of data mining applications

If you’ve made up your mind to learn data mining, here are some applications of it, knowing which would help you choose your career path:

• Market Basket Analysis: This modeling technique is based upon a theory that buying a specific set of articles makes you more inclined to buy another group of articles. A retailer can use this technique to recognize a buyer’s purchase behavior, which can then be used to tweak the offerings or change the store’s layout to encourage buying. The use of differential analysis comparison of results between customers in different demographic groups or even between stores at different locations can help retailers make the necessary changes to boost sales.
• Fraud Detection: Unlike time-consuming and complex traditional methods of fraud detection, data mining can help find meaningful patterns in data. This information can then be used to collect sample records, classify them as fraudulent or non-fraudulent, and build a model using this data where the algorithm is designed to spot whether the record is fraudulent or not.
• Future Healthcare: Using data mining approaches such as machine learning, multi-dimensional databases, statistics, data visualization, and soft computing, the number of patients in each category can be predicted. This can help develop processes that ensure each patient gets appropriate care at the right time and the right place. Apart from helping to improve healthcare and reduce costs, data mining can also assist healthcare insurers in identifying fraud and abuse.
• Education: EDM or Educational Data Mining helps predict the future learning behavior of students, and even lets you study the effects of educational support as well as how advanced scientific knowledge can help in learning. Thus, data mining can help educational and training institutes focus on what they should teach and the ways to effectively teach apart from predicting the results of the students.

Apart from the above, many other industries like banking, transportation, manufacturing, etc. can also gain from data mining and data science.

2- How to learn data mining?

If you have your eyes set on the field of data science and want to master data mining, you can either get enrolled in a full-time course or find some bootcamps to join where you’ll learn all that you need to, albeit much faster than a traditional course. In case you plan to use bootcamps, remember that you’ll need a good statistical and machine learning foundation to understand what’s being taught and apply this knowledge to get useful information by cutting the noise of Big Data.

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To learn more about data mining, click here and read our another article.

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1- Data Analyst vs. Data Scientist

The job of a data analyst is to collect, process, and apply statistical algorithms to structured data to derive benefits and help in informed decision making.

Though the goal of a data scientist is similar, a data scientist also possesses robust skills for handling large amounts of unstructured data, potentially processing them in almost real-time. If you’re a data scientist, you’ll find out important information and have the ability to clean and process it, which is then followed by running advanced algorithms on the data, which could have originated from an extensive range of sources. Data scientists also have strong business acumen, intellectual curiosity, storytelling and visualization skills, and a positive attitude toward teamwork.

2- Data Analyst to Data Scientist – The Road to Transition

Though most data analysts will have a good foundation, it would still take them some time to become a data scientist. This could be from a couple of weeks to some years depending on whether you opt for a data science bootcamp in Silicon Valley or take the arduous route of full-time degrees and programs. A data analyst would need to invest time, effort, and money to develop skills to apply cutting-edge approaches comprehensively on awkward structures and/or large/unstructured data sets.

3- Data Analyst to Data Scientist – Skills Needed for the Transition

Answering this question is difficult as sophisticated data science projects may have an intricate pipeline with several elements, and mastering all at the same time is impossible. Still, you should hone your skills (as you may have already worked with these technologies as a data analyst) or at least, touch upon a reasonable part of these:

• Data Science languages: Python and R
• Distributed computing: Spark and Hadoop
• Relational databases: PostgreSQL and MySQL
• Non-relational databases: MongoDB
• Machine learning models: Boosted Trees SVM (Support Vector Machines), Regression, NNs (Neural Networks)
• Graph databases: GraphX and Neo4J
• Cloud: AWS/GCP/Azure
• Data Visualization and Webapps: RShiny and D3
• API Interaction: Rest and OAuth
• Specialist domains: OCR (optical character recognition), NLP (Natural language processing), and Computer Vision

To fast-track your transition (from a data analyst to data scientist), you can choose a data science bootcamp in Silicon Valley that has industry leaders as its instructors. With projects, hands-on assignments, and mentorship from your instructors, such a bootcamp will get you trained in the most in-demand skills, tools, and expertise essential to think and work as a modern data scientist. Thus, you can be job-hunt ready faster than waiting for years to complete traditional or full-time classroom courses to get a job in the field of data science.

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1- KNIME

KNIME Analytics Platform is one of the most popular solutions for data-driven innovation. It helps you discover the hidden potential in the data, predict new futures, or derive fresh insights. With a wide range of integrated tools, the most comprehensive choice of advanced algorithms, hundreds of ready-to-run examples, and over a thousand modules, this is one of the best toolboxes for any data analysis professional.

2- Tableau Public

It’s one of the highly effective data analysis tools with good functionalities and features. Tableau Public is considered exceptionally powerful in the business domain because it communicates insights via data visualization. It comes with a million row limit which offers a great working ground for tasks related to data analysis. With the help of Tableau’s visuals, you can chalk out a hypothesis quickly, sanity check the instinct, and start exploring the data.

3- RapidMiner

This data analysis tool works similar to KNIME i.e. through visual programming and can help you manipulate, analyze, and model data. RapidMiner helps data science teams to become more productive via an open-source platform for data preparation, machine learning, as well as, model deployment. It comes with a unified data science platform which expedites the development of complete analytical workflows in a single environment, thus improving efficiency dramatically.

4- OpenRefine

Formerly GoogleRefine, OpenRefine can help you clean, transform, and extend even messy data. This data analysis tool comes with a number of clustering algorithms that help you explore massive datasets with ease. You’d also be able to extend the data utilizing external data and web services. It supports lots of file formats to facilitate import and export.

5- R-Programming

The popular programming language comes with a software environment that can be used for statistical computing and graphics. This interpreted language supports object-oriented programming features. R is a highly popular language among data science professionals for performing data analysis and developing statistical software. Apart from data mining, it also offers linear and nonlinear modeling, statistical and graphical techniques, classification, time-series analysis, and many more.

Conclusion

While all the above-mentioned data analysis tools are designed to make your job a tad easier, they’re only as effective as the information you put in and the analysis you conduct. As business remains at the core of data analysis, you should identify your own professional inclination first before you start learning these tools. Data analysis tools aren’t only available in a huge number, they’re highly diversified as well. And that’s why it’s crucial to determine the aspect of data analysis you want to head to.

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To learn more about data analysis, click here and read our another article.

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1- Popular blockchain use cases

1.1- Improved supply-chain communications

The majority of products that we purchase aren’t manufactured by any single entity. Instead, there remains a significant number of logistics and raw materials suppliers for a specific product before it comes to the market. Probably the biggest problem with such a system is if one single component fails, the entire brand has to experience negative outcomes. With the help of blockchain, it’s possible to clearly pinpoint the stage where the product has reached within the chain.

1.2- Distributed storage

These days, most people different types of storage services which aren’t immune to institutions that can force them to disclose the information. However, on the blockchain, data remain decentralized and stored in different high encrypted devices on a network. This leads to reduced risks of potential data breaches.

1.3- Unmatched IoT

IoT is all about the network-controlled management of specific types of electronic devices. With the help of smart contracts, it’s possible to automate the remote systems management. A combination of software, the network, and sensors facilitates an exchange of data between mechanisms and objects which leads to improved cost monitoring and increased system efficiency.

2- Skills required to become a blockchain developer

These days, more and more businesses are planning to adopt blockchain to reap the advantages of the technology. As a result, they’re looking for the right talents to help them drive results. If you too are interested in learning blockchain, here’re the skills you should focus on.

2.1- Data analysis

One of the greatest benefits of blockchain is data. To become a successful blockchain developer, you’d need to someone who can understand data and make use of it to be presented to a wide range of people across the business.

2.2- Programming

Like all other software-based implementations, blockchain also requires programming knowledge to work with. One of the most popular languages in blockchain development is C++ which you might have already learned. It’s advisable to have a good understanding of a few programming languages including C++, Python, Ruby, Java, and Solidity.

2.3- Business acumen

In the future, blockchain is going to impact every industry and every organizational role. Therefore, it’s highly crucial to understand a business thoroughly as the technology works in a way where every entity has a unique access and view.

Conclusion

If you’re interested to step into the field of blockchain, this is the best time. The fundamental tools of building blockchain applications remain the same virtually. And you can easily find a good number of reputable institutes that offer specialized courses on the technology that would make your journey of becoming a blockchain developer much easier.

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1- What is machine learning?

Put simply, machine learning is a subset of AI (artificial intelligence) and enables machines to step into a mode of self-learning without being programmed explicitly. Machine learning-enabled programs are able to learn, grow, and change by themselves when exposed to new data. With the help of this technology, computers can find valuable information without being programmed about where to look for specific piece information. Instead, they achieve it by utilizing algorithms which iteratively learn from data. Machine learning is unique within the field of artificial intelligence because it has triggered the largest real-life impacts for business. Due to this, machine learning is often considered separate from AI, which focuses more on developing systems to perform intelligent things. While the core concept of machine learning isn’t a new one, the ability to apply complicated mathematical calculations to big data automatically – quickly and iteratively – is a recent development.

2- Key characteristics of machine learning

In order to understand the actual power of machine learning, you have to consider the characteristics of this technology. There are lots of examples that echo the characteristics of machine learning in today’s data-rich world. Here are seven key characteristics of machine learning for which companies should prefer it over other technologies.

2.1- The ability to perform automated data visualization

A massive amount of data is being generated by businesses and common people on a regular basis. By visualizing notable relationships in data, businesses can not only make better decisions but build confidence as well. Machine learning offers a number of tools that provide rich snippets of data which can be applied to both unstructured and structured data. With the help of user-friendly automated data visualization platforms in machine learning, businesses can obtain a wealth of new insights in an effort to increase productivity in their processes.

2.2- Automation at its best

One of the biggest characteristics of machine learning is its ability to automate repetitive tasks and thus, increasing productivity. A huge number of organizations are already using machine learning-powered paperwork and email automation. In the financial sector, for example, a huge number of repetitive, data-heavy and predictable tasks are needed to be performed. Because of this, this sector uses different types of machine learning solutions to a great extent. The make accounting tasks faster, more insightful, and more accurate. Some aspects that have been already addressed by machine learning include addressing financial queries with the help of chatbots, making predictions, managing expenses, simplifying invoicing, and automating bank reconciliations.

2.3- Customer engagement like never before

For any business, one of the most crucial ways to drive engagement, promote brand loyalty and establish long-lasting customer relationships is by triggering meaningful conversations with its target customer base. Machine learning plays a critical role in enabling businesses and brands to spark more valuable conversations in terms of customer engagement. The technology analyzes particular phrases, words, sentences, idioms, and content formats which resonate with certain audience members. You can think of Pinterest which is successfully using machine learning to personalize suggestions to its users. It uses the technology to source content in which users will be interested, based on objects which they have pinned already.

2.4- The ability to take efficiency to the next level when merged with IoT

Thanks to the huge hype surrounding the IoT, machine learning has experienced a great rise in popularity. IoT is being designated as a strategically significant area by many companies. And many others have launched pilot projects to gauge the potential of IoT in the context of business operations. But attaining financial benefits through IoT isn’t easy. In order to achieve success, companies, which are offering IoT consulting services and platforms, need to clearly determine the areas that will change with the implementation of IoT strategies. Many of these businesses have failed to address it. In this scenario, machine learning is probably the best technology that can be used to attain higher levels of efficiency. By merging machine learning with IoT, businesses can boost the efficiency of their entire production processes.

2.5- The ability to change the mortgage market

It’s a fact that fostering a positive credit score usually takes discipline, time, and lots of financial planning for a lot of consumers. When it comes to the lenders, the consumer credit score is one of the biggest measures of creditworthiness that involve a number of factors including payment history, total debt, length of credit history etc. But wouldn’t it be great if there is a simplified and better measure? With the help of machine learning, lenders can now obtain a more comprehensive consumer picture. They can now predict whether the customer is a low spender or a high spender and understand his/her tipping point of spending. Apart from mortgage lending, financial institutions are using the same techniques for other types of consumer loans.

2.6- Accurate data analysis

Traditionally, data analysis has always been encompassing trial and error method, an approach which becomes impossible when we are working with large and heterogeneous datasets. Machine learning comes as the best solution to all these issues by offering effective alternatives to analyzing massive volumes of data. By developing efficient and fast algorithms, as well as, data-driven models for processing of data in real-time, machine learning is able to generate accurate analysis and results.

2.7- Business intelligence at its best

Machine learning characteristics, when merged with big data analytical work, can generate extreme levels of business intelligence with the help of which several different industries are making strategic initiatives. From retail to financial services to healthcare, and many more – machine learning has already become one of the most effective technologies to boost business operations.

Whether you are convinced or not, the above characteristics of machine learning have contributed heavily toward making it one of the most crucial technology trends – it underlies a huge number of things we use these days without even thinking about them.

3- Why the adoption of machine learning is getting thwarted?

It isn’t possible to predict whether machine learning-enabled systems will replace human workers or not. But it can be said that the biggest factor which is slowing down the advancements of cutting-edge technologies like machine learning is the lack of human skills. A new survey conducted by Cloudera reveals that for 51% of business leaders across Europe, it’s the skills shortage that was holding them back from implementation.

Machine learning, in a similar way like data science, is progressing in a clearly different way. As this technology trend involves capturing, collating, and interpreting data, an effective machine learning professional needs to a master of a huge number of disciplines – from mathematics and statistics to programming – all are required. As you may already imagine, machine learning is pretty complicated stuff and thus, it has become actually difficult for business leaders to find the right candidates who can help them to meet their digital transformation goals.

Those who are interested to become a machine learning professional should choose their learning avenue wisely. Though there are different types of avenues available including self-learning, traditional approach, bootcamps etc, most of them come with their own disadvantages. Given the broad spectrum of machine learning domain and its rapid advancements, aspirants need to understand that no course is actually comprehensive enough. If you too are interested in stepping into this field with real-life knowledge and possess the core skills to some extent, joining a bootcamp like the ones offered by Magnimind Academy would be a good idea.

Final Takeaway

These days, machine learning is gaining serious momentum throughout the world and it has become one of the key responsibilities of senior executives to steer their business in the right direction by leveraging its true characteristics. We are at the verge of entering a world where machines and humans will work in harmony to collaborate, campaign, and market their products/services in an innovative way which is more personal, effective, and informed than ever before. In order to attain this, it is the time for business owners to think about how they can leverage machine learning characteristics, how they want the technology to operate and behave to take the business forward. It’s also important to roll out an effective and transparent strategy encompassing machine learning. It’ll help the teams to understand how they can perform their tasks more effectively by embracing the power of machine learning.

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To learn more about machine learning, click here and read our another article.

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