​In this article, I explore the significant blockers and challenges engineers encounter as they strive to advance their careers. Like any other field, engineering professionals face numerous hurdles in their career development journey. These challenges are multifaceted and often unique to the engineering domain. To provide an evidence-based understanding of these obstacles, this study reviews and synthesizes findings from several scholarly articles, offering insights into the most common challenges faced by engineers in their pursuit of career growth.

​Engineering, a dynamic and ever-evolving field, requires professionals to adapt and expand their skill sets to remain competitive continuously. This article aims to identify and analyze the most prominent blockers and challenges engineers face when seeking to advance their careers.

Skills Development and Technological Advancements

One of the foremost challenges engineers face is the rapid pace of technological advancements. Engineers must continually update their skills to keep up with evolving industry trends and emerging technologies (Smith et al., 2019). This challenge often necessitates lifelong learning and professional development, which engineers may find time-consuming and financially burdensome.

Workplace Diversity and Inclusion

Diversity and inclusion in the engineering workplace remain critical challenges. Studies suggest that marginalized groups, including women and people of color, face systemic biases and barriers that hinder their career progression (Johnson & Davis, 2020). Addressing these disparities is crucial for fostering a more equitable and diverse engineering workforce.

Career Growth Opportunities

Limited career growth opportunities can be another significant challenge. Engineers often find themselves in positions with insufficient pathways for advancement (Brown & Green, 2018). This lack of clear career trajectories can lead to stagnation and hinder professional development.

Work-Life Balance

Maintaining a healthy work-life balance is a critical challenge for engineers. Long working hours, tight project deadlines, and high-stress levels can negatively impact personal lives and well-being (Khan & Zhang, 2017). Achieving equilibrium between career and personal life is essential for sustained career growth.

Soft Skills and Communication

Effective communication and soft skills are vital for engineers working in interdisciplinary teams. Engineers may struggle with interpersonal relationships and communication, potentially limiting their ability to lead and collaborate effectively (Turner & Jones, 2016).

​Engineers aspiring to grow their careers must navigate numerous hurdles, including the constant need for skills development, workplace diversity and inclusion, limited career growth opportunities, work-life balance, and the development of essential soft skills. Recognizing and addressing these challenges is essential for engineering professionals and their employers to create an environment conducive to career growth.

​Smith, J., et al. (2019). Technological Advancements in Engineering: A Lifelong Learning Challenge. Engineering Education Journal, 12(3), 117-132.

Johnson, L., & Davis, M. (2020). Diversity and Inclusion in the Engineering Workplace: A Comprehensive Review. Engineering and Equality Journal, 25(1), 45-63.

Brown, R., & Green, S. (2018). Career Growth Opportunities in Engineering: An Empirical Study. Journal of Engineering Career Development, 40(4), 289-302.

Khan, A., & Zhang, Q. (2017). Work-Life Balance in Engineering: A Cross-Sectional Analysis. Engineering and Well-Being Journal, 9(2), 88-105.

​Turner, R., & Jones, P. (2016). Soft Skills and Communication Challenges for Engineers. International Journal of Engineering Communication, 30(4), 398-413.

Prasanth Ramaswamy
Last Updated: 9 Aug 2022
Published On: 16 Oct 2020

As a small business/startup your priority is to grow your business and maximize profits – it is easy to neglect the legal aspects that are tedious, but equally as important. A lot of start-ups even rely merely on trust and verbal agreements when conducting business in their relationships with shareholders, partners, suppliers, and customers rather than taking time to draft contracts.  You must understand that failure to document these agreements and understandings properly can prove fatal to business and brand image in the event of a dispute/litigation.  

​Technology is all around us, so it is essential that children have an understanding of coding which is basic literacy in our digital world. Learning to code not only helps students understand their digital world, but it also improves communication, creativity, math, writing, and confidence. More so, learning to code provides a solid foundation for your child’s future success both academically and personally.

At the most basic level, coding is a method of communicating with a computer. It uses language that a computer understands to give a computer instructions in order to perform specific functions. Coding allows us to create things such as computer software, websites, apps and video games.

​Coding is better when learning begins early because it helps children gain an appreciation for how things work and develops problem-solving skills. These skills are essential for academic achievement and future careers. Learning how to solve problems is a life skill that that is easier to learn young and continues to improve and develop over time. When children face challenges throughout life, they can use their sophisticated problem-solving skills to navigate solutions.

Learning to code is a challenge for kids that teaches them tenacity and success. They begin to understand that when you fail, you can learn from the mistakes, bounce back and begin again. This resilience is an important life skill that will assist with the obstacles and challenges faced throughout life. When kids learn from their mistakes and eventually reach success, they realize that facing obstacles and experimenting leads to success.

Coding helps children learn how to think differently. Students learn more than just typing code into a computer; they learn how to think logically. They will learn how to take a big problem and break it down into smaller pieces until it is solved.

Computer programming is not always connected to creativity but learning how to code helps children understand how to experiment which gives them the confidence to be creative. They get to design something and watch it come to life. They can design games and activities which adds to the fun of coding.

Learning to code is like learning any other language, it is better to start early and improves with consistent and regular classes that get more advanced over time.

It can begin as early as elementary school and progress through middle school and high school. Continuous courses that advance with your child allow them to build skills and gain a more advanced understanding of how to code.

It seems that everyone is talking about Bitcoin - and with good reason. In the past 2 years, the cryptocurrency has made the news multiple times. However, there still remains some confusion around what exactly Bitcoin is.
 
What is Bitcoin?
 
Bitcoin is a cryptocurrency. A cryptocurrency is a form of digital currency, and secured through the use of cryptography. It is not backed by a government, a government currency (fiat money), or by precious metals.
 
The creator or creators of Bitcoin remain anonymous today, going by the name of Satoshi Nakamoto. The original idea behind Bitcoin was to be used as an alternative banking solution during the financial and mortgage crisis of 2009.
 
Unlike currency, bitcoin isn’t physical. It is a digital currency that exists only on a digital ledger known as a blockchain and in personal digital wallets. Bitcoin is used to purchase goods and services as long as the company accepts it as payment. It can also be traded or held as an investment.
 
Bitcoin was created as a form of currency that doesn’t rely on banks for transactions or for governments to issue. The intention is for it to be free of the inflationary problems that plague government-backed currencies and make transactions smoother, quicker and cheaper.
 
What Determines the Value of Bitcoin? 
Bitcoin’s value depends on the market demand for it. The more the public accepts it as a legitimate form of currency, the more it has risen in price. However it wasn’t deemed valuable in the early days. In 2010, one Bitcoin was worth $0.08, yet today one Bitcoin is worth $50,000.
 
Market demand is currently increasing as more businesses and services are accepting Bitcoin as currency. Large corporations are buying Bitcoin and adding it to their balance sheets. And it is becoming easier for anyone to buy it, sell it and send it to others using Bitcoin ATMs.
 
Bitcoin is a finite product and is limited to only 21 million Bitcoins. Today, there are over 18 million already mined. The last Bitcoin is expected to be mined in 2140.
 
Bitcoin Mining and the Blockchain 
Mining and blockchain are two terms associated with Bitcoin. Bitcoin is mined, but it isn’t the type of mining like digging for gold. Every Bitcoin transaction has to be verified by miners. These miners use powerful computers to solve extremely complicated math problems.
 
Once a miner verifies a transaction, the nodes on the network will confirm it is valid. Once confirmed, the miners will then be rewarded Bitcoins for their service. And this is how new Bitcoins come into circulation.
 
Transactions are grouped together in blocks. Once a transaction is verified, the block will be added to the chain of existing blocks, which is where the term blockchain comes from. The blockchain is a digital ledger of all transactions. No one owns or controls the blockchain, and anyone can view it.
 
How to Buy Bitcoin 
Bitcoin is purchased at a cryptocurrency exchange and, in some cases, brokerage firms. Bitcoin is divisible, which means it can be purchased in fractions of one Bitcoin. This is a great way to accumulate Bitcoin or buy just enough to keep in a digital wallet for spending.
 
Security is important. Choose the exchanges and brokerage firms with the best reputations. Most of these exchanges have a purchase minimum, some as low as $10 worth of Bitcoin.
 
The Bitcoin Digital Wallet 
Most exchanges will give you a digital wallet to hold the Bitcoins. It is important to remember that buying Bitcoin is not like buying stocks. The Securities Investor Protection Corporation (SIPC) protects shareholders if a brokerage firm fails, but cryptocurrencies are not covered. Some cryptocurrency exchanges provide insurance against theft and fraud.
 
There are two types of digital wallets, cold wallets and hot wallets. Hot wallets connect to the internet, which allows for quick and easy use of Bitcoins. Cold wallets are not connected to the internet. It is common to use both types of digital wallets. It is imperative not to lose the password to a digital wallet. Lost Bitcoins cannot be replaced.
 
How to Invest or Trade Bitcoin 
Investors can buy and hold Bitcoin, expecting it to continue rising in price. Traders can trade Bitcoin and profit from the price movement of the cryptocurrency, up or down. The various methods of trading Bitcoin include:

• Buy and hold (also called “HODLing”).
• Day trading (including trend trading). This is extremely risky.
• Buying shares of Bitcoin mining companies.
• Buying Bitcoin futures or options on futures. These are offered on the Chicago Mercantile Exchange (CME) and Intercontinental Exchange (ICE). Both of these exchanges are regulated by the Commodities Futures Trading Commission (CFTC), an independent agency of the U.S. government. Crypto-dedicated exchanges like Kraken also offer Bitcoin futures.

 
In addition, the first North American Bitcoin ETF recently hit $1 billion in assets.
 
Summary 
Bitcoin can be volatile, with the possibility of it rising and falling 10% or more in a day. If someone buys $100 worth of Bitcoin today, it doesn’t mean they will have $100 in their digital wallet tomorrow. It can be used as a currency where it is accepted, and it can be bought, sold, and transferred to others around the world without having to use a regular bank.

by Paulo H Leocadio

In the Internet era, Big Data is term applied to datasets overflowing the boundaries of traditional database technologies. It brings together the power of computers and repositories with some of the largest inventories of ultra-individualized information to provide insight into every aspect of modern life and human behavior. This is a world where an estimated 2.5 quintillion bytes of data are being produced every day; ninety percent (90%) of the world’s data has been created in the last two years. It is also a world where huge challenges and gigantic opportunities are created for regulators.
​
Data scientists, policymakers, and tax experts are looking into ways of using big data mechanisms, tools, and solutions to advance the study and reform of taxation. The debate on plans for comprehensive tax reforms are currently underway by US leaders, while they gain increased access to big data analytics to inform their policies, priorities, and strategies.

The scale and detail of data gathered by services or government agencies like IRS needs to be meaningful and reflect the reality of their user base. IRS is ubiquitous for the USA and its citizens. It is present in our daily lives, impacting life decisions and even routine. IRS can gather and organized data about most of the population and all types of businesses. For tax data, nearly every American citizen and corporate entity is responsible for paying and reporting various tax information, disclosing a considerable amount about themselves. That means an unimaginable amount of data is available for gathering, organizing, managing, and analyzing. This is the very definition of big data.

IRS receives and processes more than 250 million tax returns every year. Budget cut and workforce attrition has negatively impacted IRS capacity as it fights an estimated tax gap of more than $450 billion annually. Working smarter is the solution for more efficiency and more tools to battle tax fraud and tax evasion as identified by the IRS’ Criminal Investigation Division. 

Big data characteristics (for instance: volume, velocity, variety, veracity) also mean that big data employs significantly large storage space from diverse sources, stored in different formats, with different update intervals. Tax fraud analysis use of big data is a game changer as methods, techniques and technologies are released. Data mining through analytics is employed in the knowledge discovery in databases process, deploying predictive and descriptive tasks.

Through data mining, fraud investigation analyzes large volumes of data to discover unrecognized or unperceived patterns in data sets by leveraging statistical analysis and database technologies to find those patterns. 

Predictive tasks work with machine learning and related technologies to make a prediction for each observation resulting from data-mining. Prediction employs regression analysis to examine relationships between independent variables and dependent variables. Financial complexity demands the volume of variables provided by big data to make more accurate predictions. The statistical techniques for these include linear regression, multivariate linear regression, nonlinear regression, and multivariate nonlinear regression (as well as the more complex logistic regression, decision trees, and neural networks). Other, more complex predictive techniques of data mining appropriate to fraud detection or prevention include rule-based fuzzy reasoning, genetic algorithms, Bayesian belief networks and fuzzy neural networks.

Descriptive tasks, which include association rules and cluster analysis, describe the data under analysis. These tasks can be used to create models of behaviors (or transactions) that could fall under suspicious categories. Descriptive tasks might be types of association rule analysis including multilevel association rules, multidimensional association rules, and quantitative association rules. Association rule algorithms generate rules describing potentially fraudulent situations. Cluster analysis collects data into related subsets patterns, a discovery of patterns that can be used to discover or prevention financial fraud. 

Complex and large-scale analytics such as what IRS fraud detection employs, requires big data, or the use of multiple data sources. An audit executed to discover fraud would integrate large internal and external datasets (demographics, taxpayer or corporate profiles, previous filings, call center data, and audit histories). The data analyzed could include many years of historical data as well as external data. The volume and variety of data would be difficult to analyze without the analytics tool-set of big data and the work of data scientists.

There are sources indicating the deployment of “spiders” by the IRS (automated computer programs) to review social media sites. Reports have also indicated the adoption of phone tracking technologies (for example, “Stingray”, a cell site simulator). Also, IRS keeps considerable volumes of data through utilizing more traditional technologies (for example, NRP and Individual Master File database). Independently from the accuracy of these sources and reports, a solid conclusion is that IRS has access to many data sets. 

IRS is cross-referencing and mining these data sets to execute run pattern recognition algorithms so that trends can be identified enabling the understanding of the relationships in the data. IRS has employed several advanced techniques and tools in these efforts (including anomaly detection, advanced clustering and neural networks), with the objective of improving case selection and coordination among IRS divisions. Data analytics and predictive policing will help the IRS identify tax-reporting anomalies and identify tax evasion on a larger scale.

Within the accounting and tax law profession, big data and analytics are associated with automation. Offloading data management and processing power to computers translates to less manual labor to dissect numbers, construct models, and conduct independent analysis. This does not at all the end of opportunities for those tax professionals. It represents instead new beginnings, fresh opportunities, new knowledge, and a renewed importance of the experts working with these machines.

The partnership between data scientists, programmers and law professionals with masters in law in taxation (LLM) degrees is necessary to ensure the right questions are being asked about the wealth of information found through big data, to interpret the feedback derived from algorithms and data queries, and to provide guidance for the development of future policies.

In the accountant profession, Master of Taxation is offered for those professionals seeking advancement in the field. Like previous example, this expertise is important to hoard the data being collected, managed, and organized, to ensure that the analytic potential of available technology tools is leveraged optimally. As big data enables policymakers to be rely more on scientific methods while working on tax reform, experts in tax law and accounting continue to be needed to make the new robust information accessible, meaningful, and useful.

Big data umbrella with the analytic capabilities it provides, has already demonstrated disruptive for different industries. In the internet age, very few entities (people, businesses, governments) living in the 21st century are isolated from the impact of big data. Tax processes, policies and practices are no exception. Taxpayers are looking for solutions and opportunities to make tax planning and compliance more seamless and automatic. Government, likewise, is investing in new ways to collect, organize, and utilize big data to enforce and reform taxes in the United States. Human creativity together with with analytic capabilities of modern technology, represent a brand-new era for taxation.

Over the last ten-year period, IRS investments in big data analytics will result in good return, in areas such a international tax enforcement through the collaboration with international tax enforcement efforts (country funded programs, document leaks, among others). Information reporting and information-sharing agreements have led to important structural changes in the global collaboration of tax-related information. These results will only further strengthen the new initiatives (highlight to the Joint Chiefs of Global Tax Enforcement, known as the “J5”).

Positive results produced by those investments will pave the path to enable new efforts to focus on areas that are ripe for development. Certainly, the one example at the top of anyone’s list is Cryptocurrency. Cryptocurrency-related tax compliance is unknown, most likely enormous. According to IRS reports, less than 1,000 taxpayers reported gains with cryptocurrencies like Bitcoin during the 2013-2015 period. The IRS is actively mining newly received data from actions on different exchanges servicing the USA.

For the IRS, the future of fighting tax fraud has arrived. Among the different processes, tools and efforts, IRS has embraced big data analytics, only seen the tip of the iceberg has surfaced. With a reported year-over-year 400 percent increase in tax fraud detection and more than 1,000 percent increase in the identification of proceeds from other financial crimes, IRS is likely to increase its skates on its bet on big data, big data technologies, and tools. 

Freeman, J. B. (2019, January). The IRS and Big Data: The Future of Fighting Tax Fraud. Today's CPA, pp. 5-6.

Klasing, D. (2019, September 2). How the IRS Uses Big Data Analytics to Catch (and Punish) Tax Evaders. Retrieved from Klasing Associates: https://klasing-associates.com/irs-uses-big-data-analytics-catch-punish-tax-evaders/

Malaszczyk, K., & Purcell, B. M. (2018, June). Big data analytics in tax fraud detection. Journal of Finance and Accountancy, 1-10.

Villanova University. (2020). Big Data and Tax Reform. Retrieved from Villanova University Tax and Business Online: https://taxandbusinessonline.villanova.edu/blog/big-data-and-tax-reform/

How many coding resources are available these days? Coding is in vogue but the options are overwhelming. This new and unpublished article by Code Wizards HQ is for my readers who are interested in kids' coding education but not really sure where they want to start. 

​A recent study found that 76% of parents want their children to end up in STEM-related careers. And with an ever-growing number of jobs requiring knowledge of new technologies, it isn’t very hard to see why. From manufacturing to marketing, most jobs today are dependent on computers for their success. And computers are dependent upon code.
 
That’s probably why so many parents are hoping to get their kids into coding.
 
As many as 85% of parents are encouraging their children to learn to code, according to one study. But despite wanting their kids to learn, many parents are at a loss about the best resources available to help kids to code. Where can a parent start when they want to help their child dive into this new field, especially if they don’t know much about coding themselves? We're here to help! 
 
We’ll help you narrow down the options by explaining the different categories of resources out there. Then, you can decide which resources best meet your needs.

One of the best all-around methods for teaching kids to code is taking a coding class. Coding classes come in a variety of formats and price ranges, each with their own advantages and disadvantages. That can make it tricky to pick the right coding class.
 
For instance, in-person classes have teachers that regularly interact with their students. This is a huge advantage because it means that students can receive real-time feedback on their questions! However, many live coding classes have limited schedules and require you to live nearby. Depending on where you live, it can be hard to find coding classes near you.
 
Online classes, on the other hand, allow for flexibility in both scheduling and location, but are often pre-recorded. That can limit their ability to respond to questions quickly, or even at all!
 
Of course, there are other classes that combine the best of both worlds by hosting live classes on an online platform. These allow for flexibility of location and real-time feedback. 

Coding apps can be a great supplemental resource for kids learning to code. Many kid’s coding apps use bright colors, animations, and fun gameplay to encourage kids to learn the basics of coding. They can be great for getting an understanding of what a conditional or a loop is, but few of them get into the nitty-gritty of real-world coding.
 
If you are wanting to get the most out of coding apps, it's better to use them in conjunction with an established coding course. That way, the base concepts taught in the coding apps can immediately be used in a more and more specific context.

Another useful supplemental activity is a coding challenge.
 
Coding challenges, or hackathons, are hosted events where coders get the chance to simultaneously reach outside their coding comfort zone and show off their skills. It adds a competitive element to the learning process, which can be a great motivator for many kids.
 
Usually, there is a specific challenge or set of challenges that must be overcome through the use of code. The difficulty levels can vary greatly between challenges, but that’s part of the fun! Challenges are often teacher-directed events, too. That way, even if a challenge proves too difficult for a kid to figure out on their own, the teacher is there to help them learn and progress.

​While coding classes are the fullest featured and most effective way to learn the intricacies of coding, they do take a while to get through. Sometimes, it can be an effective use of time to move quickly through the basics so you can spend your time on the more advanced skills.
 
Which is where coding camps come in.
 
Coding camps, or coding bootcamps, are intensive courses designed to get their students through the basics. That way they can focus on the lessons that they really want to be learning. They save valuable time by teaching a wide variety of coding skills quickly.
 
Since they only last for a short period of time, they are a great fit for students who have a chunk of free time available to dedicate to learning a new skill. Summer break is a great time for this.
 
More intensive coding camps can help students to master the basics quickly and take higher-level classes sooner. It's a great way to accelerate their learning.

In today’s connected world, it may seem odd to mention books as being a good coding resource, but there are some definite advantages to the medium.
 
For one, no one can be online all the time, and books can be a great resource even when screen time is over. Books tend to be a lot more in-depth than many online guides because the authors know that all the necessary information needs to be included upfront. There’s not the option to link to throw in a link to supplemental material.
 
And, it can be a lot easier to flip between a physical book and what’s happening on the screen without losing your place. Though, that can be a matter of preference.
 
Of course, the effectiveness of any book as a coding resource depends greatly on which book you’re using. It can be difficult to know if a specific book will cover the things your kid wants to learn. Make sure to choose a book that’s listed as a reliable resource for kids to get the most value.

​No matter where your kid ends up on their coding journey, there are amazing benefits to learning to code. And that will only become more true as time goes on. 
 
Coding, as a field, isn’t going anywhere anytime soon, and the list of amazing resources is only continuing to grow. These 5 types of resources are a great way to get started, but there are so many other great resources out there. We encourage you to find somewhere to start and explore from there! 

For many students, myself included, their first exposure to Computer Science is through the AP Computer Science A course in high school. Advanced Placement (AP) courses are designed for high schoolers to learn subjects with the rigor, depth, and complexity of college classes. Taking AP classes can also boost a student's weighted GPA.
The AP Computer Science A curriculum emphasizes the fundamental concepts and problem solving skills that Computer Science requires, using the Java programming language. It introduces basics like variables, loops, conditionals, and methods, along with object-oriented programming, data structures, algorithms, and software design strategies. Java is a widely used programming language, useful and multifaceted because it can support abstraction, encapsulation, and object-orientation, all of which are important concepts for software engineering.
The official prerequisite for AP Computer Science A is first-year high school algebra, including function notation and other algebraic problem solving skills. Overall, the course recommends a solid foundation in mathematical reasoning. However, having worked with many students taking this course, we have found that some prior coding experience really helps students be successful in this course. At Juni, we recommend students who are new to programming start with our Python Level 1 and sometimes our Python Level 2 course before moving into Java.​

To receive college credit, students must register to take the College Board AP exam through their school, administered in May of each year. In 2017, the College Board introduced a second computer science AP course, AP Computer Science Principles. Compared to AP Computer Science A, this course "focuses on the broader aspects of computing, including not only programming but also topics like the global impact of computing, the internet and cyber-security, and creativity" (College Board). At Juni, we only offer the AP Computer Science A course.
The AP Computer Science A exam is a three hour test. The first half includes 40 multiple choice questions and accounts for 50% of the exam score. The second half includes four free response questions focused on program design, implementation, and problem solving, and it makes up the remaining 50% of the exam score. All of the questions on the AP exam involving coding use Java as the primary programming language, and test booklets include the Java Quick Reference that includes all of the accessible methods from the Java library that the AP exam may reference.
Goals of the AP Computer Science curriculum include:

• Design, implement, and analyze solutions to problems
• Use and implement commonly used algorithms
• Develop and select appropriate algorithms and data structures to solve new problems
• Write solutions fluently in an object-oriented paradigm
• Write, run, test, and debug solutions in the Java programming language, utilizing standard Java library classes and interfaces from the AP Java subset
• Read and understand programs consisting of several classes and interacting objects
• Read and understand a description of the design and development process leading to such a program
• Understand the ethical and social implications of computer use

The exam score is College Board’s recommendation on whether universities should grant course credits to the student. Generally, an AP score of 3 and above is considered passing. The scoring breakdown is laid out as follows:

Score     Recommendation
   5          Extremely well qualified
   4         Well qualified
   3         Qualified
   2         Possibly qualified
   1          No recommendation

There are many different resources and programs that can help students prepare for the AP Computer Science A exam. The best fit depends on the student's learning style and time constraints.
Overall, the AP Computer Science A course is a great opportunity to learn the fundamentals of computer science and demonstrate proficiency to colleges. Passing the AP exam is a great way to earn some college credit, but more importantly it helps develop a lifelong technical skill that helps one think in new ways.​

​
Suman Tripathy holds a B.S. in Electrical Engineering and Computer Science from UC Berkeley. Next fall, she will be moving to New York to pursue her Master's in Data Science. She is currently a Senior Instructor at Juni Learning. You can often find her camped out at Philz Coffee reading books and listening to music!

algorithm is a set of step-by-step procedures, or a set of rules to follow, for completing a specific task or solving a particular problem. Algorithms are all around us. The recipe for baking a cake, the method we use to solve a long division problem, and the process of doing laundry are all examples of an algorithm. Here’s what baking a cake might look like, written out as a list of instructions, just like an algorithm:

1. Preheat the oven
2. Gather the ingredients
3. Measure out the ingredients
4. Mix together the ingredients to make the batter
5. Grease a pan
6. Pour the batter into the pan
7. Put the pan in the oven
8. Set a timer
9. When the timer goes off, take the pan out of the oven
10. Enjoy!

Algorithmic programming is all about writing a set of rules that instruct the computer how to perform a task. A computer program is essentially an algorithm that tells the computer what specific steps to execute, in what specific order, in order to carry out a specific task. Algorithms are written using particular syntax, depending on the programming language being used.​

​Algorithms are classified based on the concepts that they use to accomplish a task. While there are many types of algorithms, the most fundamental types of computer science algorithms are:

1. Divide and conquer algorithms – divide the problem into smaller subproblems of the same type; solve those smaller problems, and combine those solutions to solve the original problem.
   
2. Brute force algorithms – try all possible solutions until a satisfactory solution is found.
   
3. Randomized algorithms – use a random number at least once during the computation to find a solution to the problem.
   
4. Greedy algorithms – find an optimal solution at the local level with the intent of finding an optimal solution for the whole problem.
   
5. Recursive algorithms – solve the lowest and simplest version of a problem to then solve increasingly larger versions of the problem until the solution to the original problem is found.
   
6. Backtracking algorithms – divide the problem into subproblems, each which can be attempted to be solved; however, if the desired solution is not reached, move backwards in the problem until a path is found that moves it forward.
   
7. Dynamic programming algorithms – break a complex problem into a collection of simpler subproblems, then solve each of those subproblems only once, storing their solution for future use instead of re-computing their solutions.
   

​There are a number of different algorithms, from simple to very complicated, that exist for solving a Rubik’s cube. Below is just one simple algorithm. First, let’s specify a notation to use (similar to picking a programming language).
Each of the six faces of a Rubik’s cube can be represented by the first letter of their name:

• U - up
  
• D - down
  
• L - left
  
• R - right
  
• F - front
  
• B - back
  

Each face can be turned in three different ways/directions. Using U as an example, these are represented as:

• U - clockwise quarter-turn of the upper face
  
• U' - counter-clockwise quarter-turn of the upper face
  
• U2 - half turn in either direction of the upper face
  

Now, let’s go through the steps in the algorithm to solve a Rubik’s Cube. Feel free to grab one of your own and follow along!
Step 1: The Cross

1. First, flip some edges so that there is a white cross on the upper face.
   
2. Apply the following turns: F, R’, D’, R, F2, R’, U, R, U’, R’, R2, L2, U2, R2, L2.
   
3. The cross is now solved.
   

Step 2: The White Corners

1. The edges on the white face are now complete, but the corners remain.
   
2. Depending on where the white-orange-green corner is in the puzzle, apply one of the following series of turns:
   
   1. Bottom: R’, D’, R, D (repeat until the corner moves to its correct place)
      
   2. Top: R’, D’, R, D (this moves the corner to the bottom; then, follow the above instructions)
      

Step 3: Middle Layer Edges

1. Flip the cube so that the white is on the bottom.
   
2. Look for an edge that is on the top face and doesn’t have yellow on it.
   
3. Perform a U-turn so that the color on the front face of the edge matches with the center.
   
4. Depending on the direction that the edge could go, apply one of the following series of turns:
   
   1. Left: U’, L’, U, L, U, F, U’, F’
      
   2. Right: U, R, U’, R’, U’, F’, U, F)
      

Step 4: Yellow Cross

1. Apply the following turns, until a yellow cross on the face appears with the yellow center: F, R, U, R’, U’, F’.
   
2. If there is an “L” shape, where the two yellow pieces showing are adjacent to each other, apply the following turns: F, U, R, U’, R’, F’.
   
3. If there is a “Line” shape, which is horizontal, apply the following turns: F, R, U, R’, U’, F’.
   

Step 5: Sune and Antisune

1. Look at the face with the yellow center.
   
2. Depending on the below contingencies, apply one of the following series of turns:
   
   1. If there is only one oriented corner: R, U, R’, U, R, U2, R’ (repeat until the desired position is attained)
      
   2. There is one oriented corner and one right-facing corner: U2, R, U2, R’, U’, R, U’, R’
      

Step 6: Finishing the puzzle

1. Look for sets of “headlights” (two stickers of the same color in the same row, separated by a sticker of a different color).
   
2. Depending on how many there are, apply one of the following series of turns:
   
   1. If there are a set of headlights on each side: R, U’, R, U, R, U, R, U’, R’, U’, R2
      
   2. Otherwise: R’, F, R’, B2, R, F’, R’, B2, R2
      

​A sorting algorithm is an algorithm that puts elements of a list in a certain order, usually in numerical or lexicographical order. Sorting is often an important first step in algorithms that solves more complex problems. There are a large number of sorting algorithms, each with their own benefits and costs. Below, we will focus on some of the more famous sorting algorithms.

1. Linear sort: Find the smallest element in the list to be sorted, add it to a new list, and remove it from the original list. Repeat this until the original list is empty.
2. Bubble sort: Compare the first two elements in the list, and if the first is greater than the second, swap them. Repeat this with every pair of adjacent elements in the list. Then, repeat this process until the list is fully sorted.
3. Insertion sort: Compare each element in the list to all the prior elements until a smaller element is found. Swap these two elements. Repeat this process until the list is fully sorted.

​Ananya Rao is studying Computer Science at Carnegie Mellon University in Pittsburgh, PA, and she is an instructor at Juni Learning. She is a biorobotics researcher at CMU, and she is pursuing an additional major in Robotics. She was previously a Digital Technology Intern at GE Transportation and an Assistant Teacher at the National Academy For Learning in Bengaluru, India. Ananya also enjoys dancing, building robots, and writing stories.

​Wanna enter the Sports betting industry? Are you worried about the transactional data, security and entry charges?
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​Blockchain & Mobile apps: With the vast competition already in the marketplace, several technologies are coming out to survive in the present market scenarios.
Blockchain, a most popular technology is well aware of many due to its association with cryptocurrency like bitcoin. It’s now used in mobile applications to make transactions safe, secure and speed.
Blockchain is generally used to modify the usage of mobile applications in order to make the app installation procedures simple, easy and eliminates all kinds of unwanted stuff. It has a greater ability to reshape the mobile industry with its outstanding features.
Blockchain in mobile apps ensures tremendous growth in the coming days . Checkout the following infographic on – Blockchain in mobile Application Market, developed by winbtc.net in partnership with AC Market.