A significant number of newcomers in data science tend to spend a huge amount of time to develop theoretical knowledge and earn certifications only. While theoretical knowledge is certainly required to become a good data science professional, recruiters don’t put much emphasis on certifications only. Instead, they tend to evaluate the potential of a candidate by going through his/her work.

As a data science professional, you may have worked on lots of crucial problems, but if you fail to present them to the recruiters, getting a good job in the field may become even more difficult. And this is exactly where data science projects come to your rescue. They help you demonstrate your data science skills to prospective employers. Therefore, it’s important to pick your data science projects carefully. The process of picking up data science projects can be overwhelming, especially when you’re planning to mention them in your CV. In this post, we’ve outlined five top data science projects to help you in your endeavor.

1- Movie Recommendation Engine

This is one of the most common data science projects for everyone in the field. Every successful data science professional has developed at least one recommendation engine in the entire career. Personalized recommendation engines are considered highly effective when it comes to demonstrating data science skills.

Problem: To analyze the Movie Lens dataset in order to comprehend patterns and trends that will help the system to recommend new movies to users.

2- Walmart Sales forecasting

Retail is one of those industries where data science is being used extensively and thus, it’s important to have worked on at least one project related to it. There’re a plethora of tasks including inventory management, product placement, product bundling, customized offers etc are being handled efficiently utilizing different types of data science techniques.

Problem: Predict the department-wise sales of the store.

3- Text Mining

A text mining project in your portfolio may dramatically improve your chances of being hired as a data science professional. It involves data mining and advanced analytics that can prove your skills as a professional. Text mining is heavily used in social media monitoring as it helps to obtain an overview of a broader public opinion on specific topics.

Problem: Classify a set of documents according to specific labels.

4- Urban Sound Classification

This is one of those data science projects that will help you demonstrate skills in machine learning. This project is designed to getting you introduced to audio processing in the context of the usual classification scenario.

Problem: Classify the kind of sound from an audio.

5- Chicago Crime Analysis

Law enforcement agencies take help of data science techniques to understand the actual reasons behind crimes and thus, to be able to prevent their repetitions. While the problem may seem easy, data management is the key here.

Problem: Predict the type of crime.

Parting Thoughts

If you’re a complete beginner in the data science field, it’s important to select data science projects with limited variables and data. The above ones may seem a little challenging, but they should be fun to do.

.  .  .

To learn more about data science, click here and read our another article.[/vc_column_text]

The post What is a good data science project? appeared first on Magnimind Academy.

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.

.  .  .

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

The post What tools do you use to perform Data Analysis? appeared first on Magnimind Academy.

Key advantages of big data

Here’re the biggest advantages of using big data.

• Improved business processes: Probably the biggest advantage of big data is it helps businesses to gain a huge competitive advantage. Apart from being able to understand, as well as, target customers better, analyzing big data can result in the improvement and optimization of certain facets of business operations. For instance, by mining big data retailers can not only explore patterns in consumption and production but can also promote better inventory management, improve the supply chain, optimize distribution channels, among others.
• Fraud detection: This advantage of using big data comes from the implementation of machine learning technologies. It helps banks and other financial institutions to detect frauds like fraudulent purchases with credit cards often before even the cardholder gets to know about it.
• Improved customer service: One of the most common goals among big data analytics programs is improving customer service. Today’s businesses capture a huge amount of information from different sources like customer relationship management (CRM) systems, social media together with other points of customer contact. By analyzing this massive amount of information they get to know about the tastes and preferences of a user. And with the help of the big data technologies, they become able to create experiences which are more responsive, personal, and accurate than ever before.

Key disadvantages of big data

Despite the advantages of big data, it comes with some serious challenges that make its implementation difficult or risky. Here’re the biggest disadvantages.

• Privacy and security concerns: Probably the biggest disadvantage of big data is that it can make businesses a softer target for cyberattackers. Even giant businesses have experienced instances of massive data breaches. However, with the implementation of GDPR, businesses are increasingly trying to invest in processes, protocols, and infrastructure to be able to maintain big data
• Need for technical expertise: Working with big data needs a great deal of technical proficiency and that’s one of the key reasons for which big data experts and data scientists belong to the highly paid and highly coveted group in the IT landscape. Training existing staff or hiring experts to handle big data can easily increase the cost of a business considerably.

Final Takeaway

Despite the advantages and disadvantages of big data we discussed here, it just cannot be denied that data powers almost everything these days and businesses have only started to scratch the surface of the possibilities. While in the future, the complexity might be higher, but we can surely hope to see more advanced big data operations to sail through the challenges.

.  .  .

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

The post What are the advantages and disadvantages of big data? appeared first on Magnimind Academy.

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.

.  .  .

To learn about blockchain, click here and read our another article.

The post Blockchain: How can we use it and what skills are required to learn? appeared first on Magnimind Academy.

1- Targeting the audience

These days, one of the biggest problems experienced by businesses is that they fail to capture the attention of common people. The problem lies in the fact that advertisements often don’t connect with the audience. If you too are experiencing this issue, implementation of machine learning can help you sail through. You can use computer speech and vision to obtain valuable insights about your audience and use that information to create more targeted ads that result in more engagements which mean more profit.

2- Personalized customer service

Quality of customer service can make or break a business. With the help of machine learning tools and technologies, it’s now possible to combine years of data pertaining to customer services and merge it with NLP technology. The natural language processing algorithms make interactions with customers more personalized by leveraging that data. Each and every customer receives the most useful answers to their queries, which greatly increases the quality quotient of customer service. Additionally, the technology reduces the need for heavy investment that results in reduced customer servicing costs.

3- Personalize product recommendations

If you’re into e-commerce environment, then you probably know that the customers like to have personalized product recommendations delivered to them. For them, it improves their overall shopping experience and for you, it brings a new opportunity to sell more products. By leveraging the power of predictive analysis and machine learning, you can look beyond what the consumers searching for and try to connect those dots on what they most likely want. Matching customers to specific products or services will increase the chances of more conversions and thus, more profit.

4- Dynamic pricing

Change of pricing based on the level of demand or a need can bring a good opportunity to increase your revenue stream. For instance, Uber uses machine learning to create dynamic prices. It uses the technology to optimize the ride-sharing aspect and to minimize wait time. It can temporarily change pricing in an area to obtain a higher revenue stream and can lower rates where the demand is much lower. Machine learning can utilize available data to predict areas where demand may occur, which you can leverage to attract more customers, increasing your bottom line.

Final Thoughts

These days, businesses are capturing data from a huge number of sources and with the help of machine learning tools and technologies, they’re becoming able to develop a better brand exposure to obtain successful outcomes. Machine learning has already started impacting almost every part of the business domain. So, it’d be wise to integrate this technology with your existing technologies to improve profit.

.  .  .

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

The post How do I use Machine Learning to gain profit? appeared first on Magnimind Academy.

1- What’s data science?

Put simply, data science can be considered as the way of extracting valuable insights from raw data. Stakeholders and business decision-makers use these insights to make critical business decisions. As the world is getting more and more connected, a huge amount of data is being generated by businesses, as well as, common people almost every moment. While a business can always collect that data but it’ll be of no use until that’s analyzed. And this is exactly where data science professionals come into the picture.

Data science involves a huge number of expertise areas and disciplines to develop a thorough, holistic, and refined approach toward raw data. Data science professionals have to be skilled in a plethora of disciplines – from math, statistics, and data engineering to advanced computing and visualizations to perform the roles effectively. While data science is a broad spectrum and there’re lots of jobs associated with it, data science professionals typically follow a workflow. Usually, the steps of such a workflow include capturing data, managing data, performing exploratory analysis, and reporting.

2- How data science can revolutionize the industry?

Applications of data science can be observed in a huge number of industries and the field has already helped us achieve some major goals which weren’t simply possible or needed a great deal of time and effort to accomplish them just a few years ago. Implementation of data science not only presents a great opportunity to make a substantial economic impact but it holds the power to revolutionize the industries as well. Here’re some ways it can do it.

• Businesses can use data science to derive actionable insights from a massive amount of data and can put that to their use. For instance, it can greatly help them to predict the future. With the help of machine learning algorithms, data science professionals can identify patterns in raw data which wouldn’t be possible otherwise and forecast future outcomes with a greater level of accuracy.
• Businesses can implement data science to review risky ideas before putting them into action actually and thus, they become able to avoid potentially costly mistakes and risks.

3- Parting thoughts

There’re lots of other ways through which data science impact almost all the industries. Some of the major ways it can revolutionize the world include bringing precision to public services, stability for businesses, a new degree of operational efficiency, and many more. And we can only expect to see the presence of data science in a more improved manner across more industries in the coming years.

.  .  .

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

The post What exactly does Data Science mean? Is it really going to revolutionize the industry? appeared first on Magnimind Academy.

1. Probably the biggest difference between data science and data mining lies in their terms. Data science is a broad field that includes the processes of capturing of data, analyzing, and deriving insights from it. On the other hand, data mining is mainly about finding useful information in a dataset and utilizing that information to uncover hidden patterns.

2. Another major difference between data science and data mining is that the former is a multidisciplinary field that consists of statistics, social sciences, data visualizations, natural language processing, data mining etc while the latter is a subset of the former.

3. The role of a data science professional can be considered as a combination of an AI researcher, a deep learning engineer, a machine learning engineer, or a data analyst, to some extent. The person might be able to perform the role of a data engineer as well. On the contrary, a data mining professional doesn’t necessarily have to be able to perform all these roles.

4. Another notable difference between data science and data mining lies in the type of data used by these professionals. Usually, data science deals with every type of data whether structured, semi-structured, or unstructured. On the other hand, data mining mostly deals with structured data.

5. If you consider the nature of work in the fields, you’d be able to find another difference. In data science, you’re not only finding patterns and analyzing them which are key components of the data mining Instead, with the help of data science tools and technologies, you’re expected to be able to forecast future events by leveraging the present and historical data.

6. The term data science has been around for a long time – since the 1960s whereas data mining became known in the 1990s, amongst the database communities.

7. While the field of data science concentrates on the science of data, the field of data mining is mainly concerned with the process.

Final Takeaway

When it comes to handling the steadily increasing amount of data, both data science and data mining play crucial roles in helping businesses in identifying opportunities and making effective decisions. So, while the objective of both these fields remain similar – to derive insights that can help a business to grow – the key differences lie in the tools and technologies used, nature of work, and in the steps to perform respective responsibilities to attain that objective.

.  .  .

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

The post 7 Awesome Difference Between Data Science vs Data Mining appeared first on Magnimind Academy.

1- Hadoop

One of the most popular big data analytics tools, Hadoop is an open-source framework and provides massive storage for all types of data. With its exceptional processing power and ability to deal with numerous tasks, Hadoop keeps professionals from worrying about hardware failure.

2- OpenRefine

This big data analytics tool lets professionals clean up data for analysis. It comes with cells under columns which is similar to relational database tables. With this tool, you’d be able to perform things like cleaning messy data, the transformation of data, parsing data from websites etc.

3- RapidMiner

RapidMiner is one of the big data analytics tools that offer machine learning procedures together with data mining techniques like data visualization, processing, predictive analytics etc. Apart from business and commercial applications, this big data analytics tool is used for application development.

4- Apache Spark

It’s an open-source and powerful big data analytics tool that comes with a huge number of high-level operators which make it easy to develop parallel apps. It not only offers lightning-fast processing but also comes with lots of abilities including helping in running an application in Hadoop cluster, offering built-in APIs in Python, Scalar, or Java, being able to integrate with Hadoop etc.

5- MongoDB

This big data analytics tool is a contemporary alternative to databases. Its best application can be found when it comes to working with databases that change or vary frequently or the ones which are unstructured or semi-structured. Some of its best uses include product catalogs, content management systems etc.

6- Lumify

You can consider this big data analytics tool as a big data analysis, fusion, and visualization platform. It helps professionals to explore relationships and explore connections in their data through a suite of analytic options. It’s built on scalable big data technologies and comes with interface elements for images, videos, and textual content.

7- Tableau

This is one of the leaders in big data analytics tools and a viable option for non-data scientists engaged in different organizations. A big benefit of using this big data analytics tool is that professionals can reuse existing skills when it comes to big data. Tableau uses a standardized SQL to query, as well as, interface with big data systems and thus, makes it possible for companies to use an existing database to identify the insights they’re looking for, from a massive dataset. It’s also equipped with the VizQL data visualization technology that allows for data visualization without organizing the data first.

.  .  .

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

The post How we use big data analytics tools? appeared first on Magnimind Academy.

1- Data scientist

In general, data scientists design and develop new processes for data production and modeling. Apart from interpreting and performing product experiments and data studies, these people are tasked with developing predictive models, prototypes, algorithms, and custom analysis. They also need to be able to work with a wide range of techniques to deal with data including machine learning and data mining. To be able to perform all these, holding advanced degrees like a master’s or Ph.D. is crucial to becoming a successful data scientist.

2- Data analyst

Data analysts sift through massive amounts of data and generate reports and visualizations that explain the insights on what the data is hiding to help companies make strategic business decisions. In general, data analysts come with an undergraduate degree in science, technology, engineering, or math major, and sometimes hold an advanced degree. These professionals also have experience in science, math, programming, databases, predictive analytics, and modeling.

3- Skills required to become a data scientist or a data analyst

3.1- Data scientist

If you want to become a data scientist, you should focus on developing the following qualifications:

• Master’s or Ph.D. in mathematics, statistics, or computer science
• Ability to work with statistical computer languages like Python, R, SQL etc
• Ability to develop and work with data architectures
• Knowledge of machine learning techniques
• Knowledge of advanced statistical concepts and techniques
• Ability to work with web services
• Ability to present/visualize data for stakeholders
• Strong verbal and written communication skills, and more

3.2- Data analyst

To become a successful data analyst, you should focus on the following:

• Degree in statistics, mathematics, or business
• Ability to work with languages like Python, R, SQL etc
• A solid combination of intellectual curiosity, analytical skills, and reporting acumen
• A strong understanding of various emerging technologies
• Ability to work in agile development methodology
• Exceptional common on Office and Excel, and more

4- Data scientist vs. data analyst

The skills required to be either of a data scientist or a data analyst overlap in some areas. For instance, both the positions require some fundamental know-how of mathematics, knowledge of software engineering, understanding of algorithms, and good communication skills. The major difference is that a data scientist comes with all the skills of a data analyst together with solid business acumen and the ability to clearly communicate their findings in comprehensible formats to business stakeholders and IT leaders in such a manner that in can influence the way a business approaches a challenge.

.  .  .

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

The post What are the basic requirements for a Data Analyst or a Data Scientist? appeared first on Magnimind Academy.

1- What is data science?

The world is becoming a digital space day-by-day and businesses across the globe are dealing with massive amounts of data every day. Businesses have started to realize that this massive amount of data is of no use until they become able to use it to their advantage. Data science is the field that offers a perfect blend of various high-end tools and technologies that help businesses discover patterns hidden in that data. So, you can consider data science as a field with the objective of making business predictions and decisions by analyzing massive amounts of data.

The entire data science process can be distributed in the following stages:

• Data capture
• Maintenance of data
• Processing of data
• Data analyzing
• Communication of the findings

2- Components of data science

When it comes to demystifying data science, it’s important to understand the key components of data science. Let’s have a look.

• Various types of raw datasets
• Computer programming language
• Statistics and probability
• Machine learning
• Big data

3- What is a data scientist?

Probably you’ve already heard that the job role of data scientist has been declared as the hottest one of the 21^st century. These days, data scientists have become crucial assets for almost any organization. These professionals are data-driven individuals with robust technical skills and are capable of working with large amounts of information to derive actionable insights that help to build business strategies in their companies.

4- Different roles in the data science field

Over the last decade, a variety of job positions associated with data science have emerged. Some of the most common ones include data scientist, data analyst, and data engineer. Let’s have a quick look at the skills required to get into these positions to help you chalk out your future career path.

• Data scientist: Strong mathematical and statistical skills, programming skills, data visualization, storytelling, among others.
• Data analyst: Mathematical and statistical skills, programming skills, data wrangling, data visualization, among others.
• Data engineer: Databases, programming languages, frameworks, among others.

Final Takeaway

If you’re interested to step in the data science field, it’s important to understand that to become a successful professional in this field, you’d need to acquire a diverse skillset. This is an ever-evolving field and it’s not possible to learn everything. But, if you can master the basics, you’ll surely be in a position to learn anything as and when required. And that’s probably the best way to succeed in fast-developing field like data science.

.  .  .

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

The post Demystifying Data Science appeared first on Magnimind Academy.