Today I learned Market Participants. SEC maintains standards for fair, orderly and efficient markets by regulating a number of securities market participants.
Market participants:
Today I learned that cryptocurrency is essentially virtual money that has it's own exchange rate. However, there are no physical bills or coins, only electronic transactions indicating that a quantity of money has trade hands.
Bitcoin is more than a cryptocurrency used as a payment or for investors to hold and hope for value increases. There is an entire ecosystem at work behind a cryptocurrency, There are many of these ecosystems working on the internet today, but because Bitcoin was the first, it's essential to understand makes it work and how.
Some Key Facts:
You access your bitcoin using a wallet, public key, and private keys
The Bitcoin blockchain is a database of transactions secured by encryption and validated by peers. The blockchain is not stored in one place; it is distributed across multiple computers and systems within the network. These systems are called nodes. Every node has a copy of the blockchain, and every copy is updated whenever there is a validated change to the blockchain.
The blockchain consists of blocks, which store data about transactions, previous blocks, addresses, and the code that executes the transactions and runs the blockchain. To understand the blockchain, its vital to understand blocks first.
When a block on the blockchain is opened, the blockchain creates the block hash, a 256-bit number that encodes the following information:
The nonce: a randomly generated 32-bit number
Bitcoin mining is the process of validating transactions creating a new block on the blockchain. Mining is conducted by software applications that run on computers or machines designed specifically for mining called Application Specific Integrated Circuits.
The hash is the focus of the mining programs and machine. they are working to generate a number that matches the block hash. The programs randomly generate a hash and try to match the block hash, using the nonce as the variable number, increasing it every time a guess is made. The number of hashes a miner can produce per second is it's hash rate. Mining programming across the network generate hashes. The miners complete to see which one will solve the hash first, the ones that does receives the bitcoin reward, a new block is created, and the process repeats for the next group of transactions.
Today I learned types of orders, types of brokerage accounts, and executing an order. The most common types of orders are market orders, limit orders, and stop-loss orders. There are four types of orders. A market order is an order to buy or sell a security immediately. This type of order guarantees that the order generally will execute at or near the current bid (for a sell order) or ask (for a buy order) price. However, it is important for investors to remember that the last-traded price is not necessarily the price at which a market order will be executed. A limit order is an order to buy or sell a security at a specific price or better. A buy limit order can be executed at the limit price or higher. A stop order also known as a stop-loss order is an order to buy or sell a stock once the price of the stock reaches the specified price, known as the stop price. When the stop price is reached, a stop order becomes a market order. A buy stop order is entered at a stop price above the current market price. Investors generally use a buy stop order to limit a loss or protect profit on a stock that they have sold short. A sell stop order is entered at a stop price below the current market price.
There are two types of brokerage accounts. A cash account is a type of brokerage account in which the investor must pay full amount for securities purchased. In a cash account, you are not allowed to borrow funds from your broker to pay for transactions in the account. A margin account is a type of brokerage account in which your brokerage firm can lead your money to buy securities, with the securities in your portfolio serving as collateral for the loan. As with any other loan, you will incur interest costs when you buy securities on margin.
There are risks involved in purchasing securities on margin. For example if you buy on margin and the value of your account immediately. It can also sell any of the securities to sell. even if the brokerage notifies you that you have a certain number of days to cover the shortfall, it still may sell your securities before then. A brokerage firm may at any time change the threshold at which customers are subject to a margin call.
When you place an order to buy or sell stock, you might not think about where or how your broker will execute the trade. Where and how your order is executed can impact the overall cost of the transaction, including the price you pay for the stock.
Today I learned stocks. A stock is a security that represents a fractional ownership in a company. When you buy a company's stock, you're purchasing a small piece if that company, called a share. Investors purchase stocks in companies they think will increase in value. If that happens company's stock increases in value as well. The stock can be sold for a profit. When you own stock in a company, you're called a shareholder because you share in the company's profit. Public companies sell their stock through a stock market exchange, like Nasdaq or the New York Stock Exchange. For companies issuing stock can be a way to raise money to pay off debt, launch new products, or expand their operations. Investors can invest in stocks as a way to grow their money and outpace inflation overtime. As a shareholder you can make money when stock prices rise, you may earn dividends when the company distributes earnings, and some shareholders can vote at shareholder meetings. Investors can buy and sell shares through stockbrokers. The stock exchanges track the supply and demand of each company's stock, which directly affects the stock's price. Stock prices fluctuate throughout the day, investors who own stock hope that over time the stock value will increase in value. Companies can lose value or go out of business completely. When this happens stock investors may lose all or part of their investment. stocks carry more risk than some other investments, but also have the potential to reap higher rewards. Stock investors earn money in two ways:
You can buy individual stocks through an online broker. The process of opening a brokerage account is similar to opening a brank account. The commissions charged by online brokers for stock trades vary, so it's important to shop around. There are two main types of stocks, common and preferred. The main differences between common stock and preferred stock are dividends and voting rights. Most investors own common stock in a public company. Common stock may pay dividends but, dividends are not guaranteed , and the amount of the dividend is not fixed. Preferred stocks typically pay fixed dividends, so owners can count on a set amount of income from the stock each year. Owners of preferred stock also stand at the front line when it comes to the company's earnings. Excess cash distributed by dividend is paid to preferred shareholders first, and if the company goes bankrupt, prefer stock owners receive any liquidation of assets before common stock owners.
Today I learned beat making. To begin beat making you'll need a handful of tools like, sample library, DAW, Beat Sequencer , MIDI controller, and mixing and mastering software. Sample library is a very a very helpful tool when learning how to craft good beats. The samples available to you will affect your composition decisions. Recording your own sample can be pretty dull, so you should consider using sample databases like Splice or Sounds.com. They will provide quick and broad access to a lot of sonic possibilities. You can do research to find a DAW that works best for you. This software will allow you to stack multiple instrument tracks to make your own unique beat. Some DAWs come with their own beat sequencer built in. You can use BreakTweaker for a step-by-step tutorial on how to build a beat. Using BreakTweaker you can arrange your samples on a grid and loop it over several bars. This can save you a lot of time so you don't have to copy, past, click, and drag all of your samples around in your DAW. BreakTweaker comes with cool additional features that allow you to shred, splice and alter each individual note placed on the grid. You can set a drum groove by clicking MIDI notes into your DAW, being able to play the drum groove which will help to internalize the process of creating rhythms. It'll be easier to create beats that resonate with people when you can feel the rhythm, rather than simply conceptualizing it visually. It is recommended that you use a MIDI controller that you can plug into your computer and use to trigger your samples. Mixing and mastering software will help bring a professional polish to your beats. In the beat, Neutron can help glue all the elements together into one cohesive sounding beat. Tonal Balance Control allows you to check your mix, and Ozone is used to master the final beat.
GarageBand
Today I learned 5 software options for making music at home that anyone can use. First is Garageband, you can use GarageBand on a MacBook and it is a great DAW to get started on. It's simple and straightforward, and it comes with a wide variety of samples and instruments to supplement and aid your own creativity. It's pretty limited in its overall functionality. Apple uses this program to upsell its more in-depth DAW, Logic Pro but, it should give you a sense of what you want out of a DAW in order to help you make a more informed purchase down the road. "The Drummer" feature of GarageBand is one of the best virtual groove-maker instruments, It's able to automatically make MIDI music within a human feel without much adjustment.
Tracktion T7
The older versions of Tracktion T7 are released for free to encourage people to buy their most recent release. You may not get all the new and cool features of the latest release you'll still have a complex DAW that'll allow you to do more sound sculpting than GarageBand. The biggest difference between Tracktion T7 and GarageBand is that Tracktion has MDI-out functionality, which means you'll be able to send MIDI tracks to your hard synthesizers and drum machines, giving you more tools and fewer limitations to make the music you want.
Klevgrand SyndtSphere
Klevgrand SyndtSphere is a new software that is perfect for people who want to get into synthesizers. There are over 70 presets to choose from, and you can make between sounds using a sphere at the middle of the interface. The sounds morph depending on the proximity of the sphere to the specific sound.
Audacity
Audacity looks similar to a DAW but, it's actually a digital audio editor. Its main purpose is to manipulate and edit audio, rather than help you organize different tracks into a coherent song or mix it to perfection. You can use Audacity to manipulate samples, edit podcast and spoken-word audio, digitize tapes and other analog audio recordings, or put a quick master on finished tracks. Audacity is easy to pick to up and use straight away , and has a great variety of efforts and tools for you to explore before you level those effects up with plugins in other DAWs.
Giada
Giada is a new loop machine that can be used as a drum machine, a loop station, and a live sampler. This software is typically used by DJs and other musicians while they perform to make and manipulate beats on the fly. Its looping and quantization mechanics make it a very easy to use piece of software for onstage usage. You can use the live sampler with outside sources, meaning if you already have a DAW you like you can keep using it and just use Giada alongside it for the live looping capabilities. This software also has a built-in wave editor which is quite powerful. It’s free and takes up almost no space on your hard drive.
A music/recording producer, is someone who assists an artist with their recording project. They are the creative and technical leader, commanding studio time and coaching artists, and popular genres typically creates the song's very sound and structure. If you're passionate about music becoming a music producer may be a fulfilling career. Music producers can take on many aspects of recording from writing lyrics to composing a melody. Music producers create the beats, tunes, harmonies, sound effects, and chords that combine to make a song. They ensure the music flows and transitions effectively. Producers work in all genres of music, including pop, electronic dance music, country, rock, hip-hop, theater and jazz, to name a few. As a artist you can take the route of both recording and performing music in hopes of recognition. Many popular artists started off producing before making it to the big stage. Some big time artists that started off producing are Bruno Mars, Lady Gaga, and Pharrel Williams. To strive in music production it takes a combination of talent, skills, and ability to make connections. The most common degree for this field is a bachelors degree, and music production doesn't require any licensure/certification. Some key skills in music production are, musical aptitude, strong listening and interpersonal skills, familiarity with audio equipment and software. The median salary for all music directors and composer is approximately $51,670 annually. If you're interested in becoming a music producer, you'll have to look into specific labels for guidelines as some may not accept unsolicited demos; music producer requirements vary from label to label. You should know what study for music production job opportunities before conducting a search. For common bachelors degree in music, courses cover the basic skills needed.
Sample courses within the following program include:
By purchasing a DAW(Digital Audio Workstation) someone can practice and build their skills to pursue music producing gigs as a side job, or to get some hands-on experience to go with their degree. A DAW is a workstation that includes hardware and software to create, edit, mix and export song. These programs can include hundreds of sounds and effects to work with; they include the piano, saxophone, nylon, guitar, and many more. The price range depends on the features included, though basic DAWs can be found for under $100 and are easily installed on a computer or laptop.
Today i learned many things about the music industry. There are many music business degrees and different types of careers involved in the music industry. The music business requires understanding of finance, micro- and microeconomics, and data analytics. Apps like TikTok SoundCloud, Instagram and Spotify have changed the ways listeners consume music and the way it is shared and advertised. Recording engineers and video/sound engineers are two of the highest- paid careers in the music industry. Music teachers can use their creative skills and music training to develop aspiring young minds, they can work in schools of for themselves. Musicians are only one of the many moving parts that help the music industry run. The music industry needs more than instruments and performers, there are multiple paths you can take to be a part of it. A degree in music business consists of three components, analytical, musical, and managerial education, according to Larry Miller, director of the music business program at New York University Steinhardt. The analytical component includes understanding the international music business marketplace, macro- and microeconomics, mathematics, marketing, financial accounting and management, entertainment and organizational analysis. The musical section teaches music theory like aural comprehension, music history, keyboard skills, and music in contemporary world culture. Managerial training includes music publishing, concert management, entrepreneurship, writing, media planning, finance and entertainment law. When choosing a music, your goal is to be as close to the industry as possible just in case an opportunity arises as you're pursuing your degree. You can gain an important advantage by securing internship and important opportunities at nearby record label or companies. Four top cities for music are New York City, Los Angeles, Chicago, and Nashville. There may be difficulties while pursuing careers in music. The business side of music may not always be as glamourous as the performance. You may struggle if you're not familiar with the day-to-day of your field. The work might be more making phone calls and filling out paper work than following your passion, which can be discouraging. Hence, why internships and job shadowing are important part of the process. They allow you to experience the work and uncover the parts you don't like. Some jobs you can get with a music business degree are, video/sound engineer, recording engineer, or music director/conductor and many more. As a video/sound engineer you are responsible for mapping the sound and voice effects for video games. Equally lucrative positions within this niche include recording and scoring composers. The median salary of this job is around $40,000 to over $120,000. Recording engineers record, edit, and mix sound for artists or music companies. They manage the artistic and technical aspects of the recording session. The median salary of this job is around $25,000 to $150,000.Music directors or conductors job is to lead orchestras and choirs. They arrange music for their performers and lead rehearsal and performances. The median salary of this job is around 43,000.
Today I learned how to use Google Analytics and what it does. Google Analytics allows you to track the activity of visitors on your website. Google Analytics is the most popular free web analytics service. Google Analytics allows you to see how many visitors are on your website, where they're coming from, how long they're spending on your website, what pages they're clicking on, and also provides information like their age, gender, country/city etc. When you install google analytics onto your wordpress website you'll get a special code that'll be inserted into your website to help with tracking. You don't need to know any coding to get started. The code will track where your visitors are coming from that way you can see which ad or platform is generating the most or least traffic. It can also tell you which ads are working better than others and lots of other helpful data like that. When visitors are on your website google analytics can provide you with information like the bounce rate, exit%, entrance, page value and more. With this you can figure out what needs to be improved on your website so you can keep people purchasing on your website. Having information of the age group and gender of people that go on your website can help you target the best majority demographic and grow your website.
Today at Techie Youth I learned Wordpress. Wordpress is the most popular free content management system used to build and maintain websites. Managing your website content within a CMS typically minimizes any technical skills required of the person who adds the content. Almost every website that has many people contributing content uses some kind of CMS to manage the data, and to provide a user-friendly interface to add and edit website content. Wordpress is free because it is an open source software meaning, there's thousands of software engineers out there that are working on it everyday to enhance it. In wordpress you are able to choose from around 11,000 different themes/template layouts that'll determine the look and style of your website; You are able to modify all of the text including fonts and font sizes. You can create buttons, upload your images and videos, and much more. You also have access to over 55,000 plugins and widgets to help optimize the functionality of your website. This is for people that may want to start a store or open up a gallery or whatever it is on their website building your website with wordpress is all online so you can build your website from anywhere as long as you have access to internet connection. When the internet was still somewhat new the only way to make a website was to use code in HTML or PHP format and your web browser would interpret the code into colors, text and spaces that would form your website. Wordpress works just like this without the need to manually code. When you make changes on your website it updates your website automatically. If you're looking to make your first website you can use wordpress.com to make a free website with their web hosting but, there are a few catches.
Catches like:
If you want to make your own website with your own unique domain name and no limitations, you'll want to be self hosted and use wordpress.org and download their free software. This will allow you to:
If you want to create a self-hosted website with wordpress, you can go to createaprowebsite.com where there is a free step-by-step guide that will walk you through the entire process of creating your first website with wordpress.
Today I learned version control. Version control is a system used to manage the files on your website. The primary benefit (among many features) is the flexibility of retaining a history of all the changes made to a group of files , so no data can ever be accidentally lost, deleted or overwritten. Currently, Subversion (SVN)and GIT are two version control programs that dominate the market and one or the other is used in nearly every website project that utilizes file versioning. Github is amongst the largest repositories of free open-source software, and is built around contributing developers using GIT. SVN is a centralized version control system that is a tool that allows you to version control files and collaborate on files. SVN deployed with visual SVN server gives us server environment within which to maintain your files and you add to this a graphical user interface called tortoise SVN. This give you a simple and quick way for individuals to collaborate on files and version control these files. SVN was developed and released by the Apache foundation and has been around for nearly 20 years. Overlaying this there is a server component called visual SVN server which allows you to graphically manage your servers installation where you create repositories, manage your users, and manage the files that are under control by SVN. Tortoise SVN is a visual client that allows you to manage your files on your laptop, desktop, and check or commit those files into the server and check out these files from the server and manage them locally on your machines. It's vital to remember that the core of all of this is SVN. The problem you are trying to solve with these tools are, firstly, the version control of files, it means that you want to have a file that starts out as a first version(version one); you make changes to that file and save a copy of that file as the second version( version two). If you want to go back to your original version you just open the first version of the document.
Today I learned some of knowledge about app developers. To become an app developer you will need to acquire a lot of new knowledge and skills. This line of work expects a lot from you and requires you to stay abreast of every trend and new technology. Many years ago with the invention of computers, a lot of people lost their jobs because what they did in days, the computer did in seconds. In mobile app development it is crucial that you learn to stay relevant, this means learning new things and acquiring knowledge on an ongoing basis. You can learn to create apps in the programming language exclusive for IOS and the programming language exclusive for Android. Learning multiple programming languages is something most top app developers and every mobile app development company in the USA should take into consideration. Dedicated developers should be fluent in Java, Kotlin for Android, objective-C and swift for IOS apps. The ability to use all of these programming languages will set you apart as an app developer. Apart from learning programming languages for both Android and IOS apps, developing skills in both platforms will definitely get you spotted. Every web and mobile development app company is looking for a developer with the abilities to diversify across different platform and provide the same level of expertise and it will be a skill every developer needs to possess. It saves a lot of time and helps you make way more money than you originally would have made. The reason we have different web and mobile development companies is because there's the need to have apps on the Apple Store and Play Store for any smart phone. A mobile app development company will prosper if the app developer knows a bit about the business aspect of app creation. There are people who thrive and make a living from stealing other peoples work. Many apps have the option of filling in personal data and information, all of this should be guarded properly. Every web and mobile development company needs to have developers experienced in data security. They'll need to include a fail safe and procedures to follow to make an app tightly secure.
A project managers job is to ensure each member of a team knows what they're doing and that each task gets completed correctly within the timeframes given. The main qualification that is recognized is the Princeton qualification which basically stands for projects in controlled environments which is what project management is. There are two different levels, you can do the foundation or the practitioner. The former allows you to be a part of the project team, and the latter allows you to lead a project team. Most jobs that you go for for project management will have this approved every requisite for you to be able to do it.
Soft Skills:
Hard Skills:
A typical project will entail a face-to-face meeting with a client and anyone relevant to the project team. They will gather their requirements which is everything they expect to receive by the end of the project. Using that the project manager will put together the project initiation document which is essentially an outline of what is expected and not expected. Most importantly, a list of various dependencies, risks, constraints and things like that because most of the time within a project reducing the risk is the main thing and hardest thing to get done. After that the document is sent through to the client to sign off. Once it's signed off then the process of putting the actual solution together for the client begins. Once the ideas and your goal is put in to place you start putting it together properly. Then you go through many rounds of testing with the client so they can ensure that they're getting exactly what they asked for. Then you sign off the project and move on to the next.
Today at Techie Youth I learned many things about algorithmic thinking. Algorithms are the core of successful and efficient development. As you learn code you'll use them, you'll be asked about them in interviews. They will likely be part of your day-to-day development work. To learn common algorithms is very helpful but, getting used to algorithmic thinking is even better. Algorithms and data structures are bugbears of the software development world. Traditionally educated developers were probably taught about them in one or two classes. Self taught or boot camp developers often aren't exposed to them at all. For most beginner developers, algorithms and data structures are the sources of a lot of anxiety and imposter syndrome. To think algorithmically is a mind shift from how we usually think. It is more of a systematic way of thinking through problems and solutions in a way that's similar to how a computer would run. we all have unconsciously built up shortcuts, assumptions, and rules of thumb that we use to help us solve everyday problems. For example, for a simple task of sorting 10 numbers, we can look at them and tell quickly what the order should be and arrange the numbers correctly. However, we're not used to breaking our thought process down into its individual steps and translating that to what computers can do. For example, computer can't jump to general spots in a dictionary to find a word based on its spelling. A computer need very specific instructions on where to start. Like all skills, thinking algorithmically is learnable and just takes practice. It's similar to getting a feel for how to organize code classes in object-oriented design. You do your best, and iterate on the solution to improve the weaknesses that you find later.
Algorithmic thinking is about:
Today I learned about Algorithms. An algorithm is a set of steps to accomplish a task. Finding good algorithms and knowing when to apply them will allow you to write interesting and important programs. There are many different types of algorithms that are used in different situations. For example, Google Hangouts uses audio and video compression algorithms to transmit live video across the internet quickly. Another example is, Google Maps figures out how to get from Dallas, Texas to Orlando, Florida using a route finding algorithm. Also, Pixar colors a 3D model of a character based on the lighting in a virtual room by using a rendering algorithm. Nasa chooses how to arrange the solar panels on the international space station and when to rearrange them by using an optimization and scheduling algorithm. These algorithms are more complex then our everyday algorithms but, they boil down to the same thing, a set of steps to accomplish a task. By applying the right algorithm you can save yourself some effort and make your programs faster. Computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm. This allows the computer to search through the huge tree of possible moves. If you have a game similar to checkers, you might be able to use algorithms based on these techniques. It is important to know how to design new algorithms as well as how to analyze their correctness and efficiency. In the biological sciences new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs. In physics, algorithms simulate climate and weather patterns. In other algorithms search and analyze the vast data about stars in the universe that's collected by automated space telescopes. Across all the sciences, and even on websites like Khan Academy, efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions. In any areas you are interested in new algorithms will allow massive computational power to be harnessed to do things that people really need and care about. Not all algorithms are created equal. The two most important things you want your algorithm to do is solve a problem and to do so effectively. You usually want your algorithm to give you an answer that is always correct. However, you can live with an algorithm that doesn't give the correct or best answer because creating perfect algorithms take a long time. For example if you wanted a program that would determine the most efficient route for a truck that delivers packages , starting and ending at a depot, it would take weeks to run going through all the possibilities. You can make a program that would give you a route that is good, it doesn't need to be the best. How do you measure the efficiency of an algorithm? You could time how long it takes to run the code but, that would only tell you about the particular implementation in a certain programming language on a particular computer and just for the input it was given. Instead, computer scientists use a technique called asymptotic analysis. This allows algorithms to be compared independently of a particular programming language or hardware. So you can conclusively say that yes, some algorithms are more efficient than others.
Today I learned how to get machine learning a job without a degree. it is necessary for you to show that you have sufficient experience to make up for the lack of a degree. you can show that you have relevant experience by completing machine learning projects, doing well in machine learning competitions, contributing to open source projects, attending hackathons or creating your own machine learning blog. Most machine learning positions require a masters or bachelor degree in a quantitive field with the ability to show relevant experience. Getting a machine learning job without a degree may be difficult when you're competing with others who have a degree. Showing relevant experience will be vital to get your foot in the door. Machine learning makes use of many skills, and many of them will be tested in coding challenges for interviews. To succeed in most machine learning roles you are required to have knowledge of calculus, statistics, linear algebra, SQL, programming, data structures and algorithms, and different machine learning models. Two courses were recommended to learn machine learning. The first is Andrew Ng's course which doesn't assume any prerequisite knowledge. The second is machine learning with Python by MIT that has prerequisites of probability, linear algebra and programming in Python. In data science and other machine learning interviews, it is common to be tested on SQL, data structures and algorithms. You can practice these skills a lot on the website Leetcode. One place you can find many different machine learning competitions is the Kaggle website. On Kaggle there are usually ranked machine learning competitions that have prize money. Doing well in these competitions will allow you to include as part of your resume and it'll help show that you have an adequate understanding of machine learning. The actual process of applying the machine learning models only makes up a small part of what most machine learning jobs involve. The datasets found in these competitions are normally clean datasets where you won't need to do much data cleaning yourself. Whereas, in a job setting you'll likely be spending more time data cleaning than actually selecting and applying the different machine learning models. One way of showing your ability to clean the data would be to build your own machine learning projects. When making your own machine learning projects it is important to show that you have good understanding of the model building workflow. This includes:
Contributing to open source projects is another way to show you have experience in machine learning. By contributing to open source projects you'll be able to show your capability to work as part of a team on a large project. You will also be able to get a lot of feedback on your code from very knowledgeable people on the topic which may help you improve as a programmer. If you want to contribute to open source projects, MyBridge shows you all of the current open source projects on Github. You can also attend hackathons where people meet for several days to code a solution for a challenging problem from scratch. In the programming community, hackathons have begun to surpass job fairs in terms of showcasing yourself as a credible job prospect. Large companies often send their own engineers to these events to see if they would be a good fit for the company. This would be a great way to show your ability to work effectively in machine learning and very helpful if you don't have a degree. This additional project will showcase your ability to work on machine learning problems as a team to potential employers. You can find hackathons on meetup.com. You can also try data science boot camps. They often help you find a job after completion. However, boot camps may promise to teach you data science in a few months so you can go off and get a job that'll pay you around $100,000 which is unlikely to happen. Here are some things you may want to keep in mind while searching for a boot camp:
Those are aspects of a boot camp that would be worth spending your money on.
7/15
Source: towrdasdatascience.com/Donal Byrne
Today I learned how to get an AI job with little or no experience. Although the title says no experience, no one is going to hire someone with absolutely no experience. You can provide yourself with required experience with personal projects, hackathons, coding challenges, and open source projects. You'll definitely need to have ML projects in your Github. This is the first thing recruiters look at after your CV. Your ML project doesn't need to be big, flashy, or innovative, it just needs to display your understanding on the topic and give indication that you're able to work/research independently with acceptable coding standards. A few things to focus on while building a Github project are: the project should take no longer than a month to complete, make sure your code is clean, modular and commented, provide a "Read Me" and other documentation for your code such as technology used, reference tutorials, dependencies etc., and lastly if possible provide units tests for key parts of the code base. Hackathons are great for many reasons, they forces you to go out and build something while getting to meet more experienced people and can you put it on your growing CV/Portfolio. You should try to find AI specific hackathons, but also go to general software hackathons and try to put an AI spin on your project. You can try meeting new groups in your area focusing on AI or software development in general by going on meetup.com. Similar to hackathons, coding challenges force you to build a practical application of what you've learned which is worth a lot when applying for your ML job. As an added bonus, these competitions are pretty fun and the added sense of competition can be a really good motivator. You can take a look at places like Kaggle, CodinGame and Halie.io. Open Source Projects are the closest thing to real world experience that you can get, short of actually getting a job as an ML developer. Open source projects will give you real insight into production level code and will teach you important skills like debugging, versioning control, developing with other people and lots of ML(depending on the project).
7/6
Source: Code Academy, Joshua Fluke
Good Afternoon,
Today at Techie Youth I learned the fundamentals of Professional Web Development. I started to use Visual Studio Code and began to develop knowledge on the basics of HTML and CSS. I started with learning what a tag and an attribute is. A tag is an element that changes the look of content or performs action. Some common tags are: <head>, <body>,<title> and many more. An attribute is the description of a tag. I gained knowledge of CSS tags such as <style> and <link>. I discovered what a rule is and what targeting is. A rule consists of 3 things; selector, property, and value. Targeting allows you to target every matching tag on the page. For example you can target every "h1" tag on the page by typing "h1" in the "selector" space. Some forms of property are color, opacity, background, display, border, padding, margin and so many more. The 2 forms of value that I am aware of are percentage and pixels. In conclusion, I believe I learned a lot today and that web development is very interesting and complex.
7/7
Sources: Jesse Showalter, Joshua Fluke
Good afternoon,
Today I acquired knowledge of Web Development Roles. Web developers are people who design and build websites. They are responsible for the appearance of the site and technical features. Two types of developers are front-end and back-end developers. There two major differences between the two types of developers. Front-end developers typically get payed less than back-end workers. There is a lot of competition in front-end development which drives the price down. When working in this profession reaching in to different departments can be very beneficial to you and others. For example, you can build job skills, acquire networking ability, get familiar with team engagement, and also have more people on your side that can validate your work and knowledge. I have gained knowledge of what front-end and back-end interviewers may ask you. Front-end interviewers may ask things such as: can you write a join, can you write some sort of sequel report, are you familiar with sequel injection and how to prevent it, and many other questions that relate to front-end development work. Back-end interviews may ask you what is the CSS box model, they may ask how would you address the front-end to be more mobile responsive, and finally what would you use to make something more modern. You must be aware that no matter what your title is the interviewer may ask about non-related questions to your job title to test your extensive ability.
After studying front-end and back-end development, I began to explore the differences between Web Design and Web Development. These two jobs are very different but, they correlate. Web design refers to both the creation of the aesthetic portion of the website and it's usability. Tools that web designers use are: Photoshop, Sketch and Interface design tools. It is important to know the tools, principles, and foundation of design like color, space, line, and form. Web developers practice the producing of HTML, CSS and Java Script for a website or web application so that a user can see and interact with them directly. Web developers spend majority of their time using client side codes/languages. tools that developers use are HTML, CSS and Java Script. A lot of developers drip into back end development, they would speak more server side languages like PHP or Ruby and different server side kind of technologies. These are the things that you will need to know to become a web developer. Web designers spend majority of their time trying to make visual sense and visually communicate the message of the brand or company or site; mostly doing visual things like framing, mood boarding, creating brands, logos, websites and animations. Web developers spend majority of their time in code, receiving visual and documentation from the web designer. The developer should work very closely with the designer to ensure that the functionality is everything that the site, client or project demands but, is also on par with the styles that the designer has handed over. You may wonder which one is for you, design or development. If you are a person who enjoys the visual aspect of things or expressing your problem solving through visual modes of solution like using color, typography and photography then design may be a more suitable profession for you. If you are a person who likes to analyze things and enjoys doing puzzles face down then being a developer may be for you. Maybe dabble and try a little of both to see which one fits you and follow it. Since these things are so closely related in the creative or tech kind of world you have the ability to change your mind and have more flex to decide where you want to be.
7/8
Source: 365 Data Science
Greetings,
Today I learned about Data Architect. Data Architect is one of the most coveted data science job roles. Roles that Data Architecture consist of are, Data Analyst, BI analyst, Data Engineer, and Data Scientist. Data Architects create the database from scratch. They design the way dat will be retrieved, processed and consumed. Data Architects are technical experts who adjust dataflow management and data storage strategy to a wide range of businesses and solutions. They're in charge of continually improving the way data is accumulated. In addition, data architects control access to data. Data Architects are also responsible for design patterns, data modeling, service-oriented integration, and business intelligence domains. They often partner with other data scientists and IT professionals to reach the company's data strategy goals. A data architect constantly seeks out innovations to provide improved data quality and reporting, eliminate redundancies, and provide better data collection sources, methods, and tools. Data architects have competitive salaries. According to Glassdoor's salary report, the average annual pay for entry level Data Architects in the U.S. is approximately $104,000. While professionals with 4-6 years experience make more than $125,000 per year and can easily get bonuses in the region of $10,000 per year. In the United Kingdom, as a Data Architect early in your career, you can earn an average total compensation of £45,000. A mid-career Data Architect with 5-9 years of experience can get as much as £55,000, including bonuses and overtime pay. The Data Architect role is on the rise with its increasing importance for enterprises and their business success.
7/11
Source: Career Watch/ Stephen Hack
Greetings,
Today I gained knowledge of System Administrators. System Admins are critical parts of almost every organization. System Admins are responsible for the day-to-day operations of computer networks. They organize, install and support an organizations computer systems. They manage local area networks, intranets, network segments, servers, desktops, and mobile equipment. System Admins make certain that email and data storage networks work properly. They often participate in decisions about purchasing hardware and software. To become a System Admin you usually need a bachelor's degree in computer science or related field. It is possible to become a System Admin without a degree but, that would be much more challenging. In the year 1999 according to the government, 204,680 jobs for System Admins. The number of jobs weren't really affected by the Great Recession around 2008; the number of jobs did recede a little bit recently, from 2016-2018. By the year 2018, the government recorded 366,250 jobs for System Admins which anticipates that over a 20 year time period the number of jobs rose by 161,570 jobs. The highest amount of System Admin jobs from 1999-2018 was 376,820 jobs around the years 2016-2017. The unemployment rate for System Admins hit a high of 6% in 2002 after the ".com" burst; and a second high of 7% in 2009 during the Great Recession. Since then the unemployment rate has been falling. Recently in 2018, unemployment has been hovering around 2%. Healthcare is utilizing IT more and more and this will lead to a greater number of assist admins but, one threat to System Admin jobs is cloud computing. Cloud computing could raise the productivity of System Admins resulting in fewer of them. This is pretty controversial and debatable and we'll see over the next couple of years whether cloud computing will disrupt the number of jobs for System Admins. The government is predicting an addition of about 11,000 System Admin jobs over the next 10 years. This is more than Database Admins but, it is far less than say Software Developers. In 1989, the average based salary for a System Admin was approximately $50,000. By the year 2018 this national base salary had been risen to approximately $92,000. Wages rose for System Admins by about $42,000 over a period of 20 years. Many people would say the starting salary would be anywhere between a zeroth and twenty-fifth percentile for pay so, this would be between a little under $51,000 a year and up to the twenty-fifth percentile which is about $64,000. Well how much do the top 10% of System Admins earn? They start around $130,000 a year and go up from there. The average salary is around $92,000 per year but, certain metro areas pay much more than this. There are five metro areas that pay System Admins more money. The first area is Washington, DC, where the average System Admin earns about $102000 per year. The second area is Bridgeport, Connecticut where the average salary for System Admins is around $103,370. Next is San Francisco, Bay Area, where the average System Admin salary is approximately $105,000. Next we have Baltimore, Maryland, where the average salary for System Admins is around $111,000. Lastly, in San Jose, California the average salary for a System Admin is about $116,000 per year.
7/12
Source: Simplilearn
Today I learned release management is the process of managing, planning, scheduling and controlling a software build through different stages and environments; including testing and deploying software releases. DevOps emphasizes the collaboration and communication of software developers and IT professionals. DevOps dedicated resources required to oversee the process of release cycles. DevOps release cycle should deploy releases into operation and establish effective use of the service in order to deliver value to the customer. Release and deployment management also ensures handover to service operations takes place and that suitable training and documentation exists to ensure ongoing support of the new service. Slow cadence release window is a period of time during which one or more teams may release into production. A release slot is subset within that release window and may be the entire window during which a team may deploy their solution into production. Advantages of slow cadence release window are consistent release cadence to business stakeholders and predictable release date targets for delivery teams. a major disadvantage of slow cadence release window is that development teams are constrained is difficult to implement continues delivery strategies. The idea of a release train is that every team involved with that "train" has the same release cadence. This strategy is commonly used in large programs, or teams of teams, where the individual teams are each working on part of a larger whole. A potential disadvantage is that release trains may also suffer from the bottleneck problems of slow cadence release windows. Another disadvantage are the bottleneck problems arising due to slow cadence release windows. Quick cadence addresses some of the requirements of continues deployment. here there are many release windows and many release slots. The increased slots allow for finer control on dependencies. The increased windows allow for more frequent releases. This makes the quick cadence release window less likely to suffer from the bottleneck challenges associated with slow cadence release windows and release trains. Continuous Release Availability is the only model that allows an implementation of continuous delivery. With this model teams may release solutions into production whenever required. In order to support this paradigm several modern DevOps practices must be implemented. Some of these are fully automated deployment, fully automated regression tests feature toggles and self recovering components. There are other methodologies besides these that allow continuous delivery in a release management framework. Some DevOps techniques for release management are, use infrastructure as code, destroy all your servers, all the time and zero in or zero-downtime deployments. The days of manual configuring of Infrastructure are long gone because manual configuration is error-prone and tracking a manual process through documentation is cumbersome. Errors resulted in countless wasted hours in debugging the new configuration. Written documents are also subject to emissions, misinterpretations and are frequently out-of-date with respect to a dynamic environment. Infrastructure as code is the process of managing and provisioning server infrastructure through machine readable definition files. Just as the software development team provides you with the source code for your application, your infrastructure development team provides you with the machine definition scripts for your infrastructure. Just like software source code these can be maintained through a version control system and become part of the git repository and can also be automatically propagated through a continues delivery through a continuous delivery system like any other piece of software.
7/13
Sources: Atlassian, Analytic Steps
Today I gained knowledge of the role Product Management. Product management is the business process of planning, developing, launching, and managing a product or service. Finding the role of product management is really complex; there are some reasons for this. The first reason is that product management has had a long history. In 2012, Ben Harvester defined the role of a product manager as a CEO of a product which was standard for many years. He was getting at the responsibilities of a product manager; everything from motivating the team, defining the problem and defining what success looks like. Since then the product manager as a CEO has been critiqued. Product managers don't really have any reports so they often have to influence without any authority. Both of these views highlight different aspects of product management . The CEO view highlights the areas of responsibility, were as the alternate view highlights the method by which a product manager can go through to achieve an outcome. The second reason defines that product management is different because of team culture. Teams can look different and change radically depending on the size of the organization. For example, in a small organization you'll see a product manager do whatever they need to do to get the job done; everything from research to analytics. However, as an organization grows you'll see them hiring dedicate teams to something like research. I also learned a little about Artificial Intelligence(AI). Artificial Intelligence is the practice of computer recognition, reasoning and action. It is all about bestowing machines the power of simulating human behavior, notably cognitive capacity. Artificial Intelligence, Machine Learning, and Data Science are all related to each other. AI executes tasks intelligently that yield in generating huge accuracy, adaptability, and productivity for the entire system. Tech decision-makers are looking for more ways to adequately implement AI technologies into their businesses to draw interference and add values to them. For example AI, in the media industry, is used at large scales, such as in social media, in automated journalism, etc. Another example, can be seen at AI in banking applications like chatbots, mobile banking, fraud detection, customer engagement etc.
7/14
Sources: Analytics Step, GlassDoor.com, ComputerScienceDegreeHub.com
Today I further my knowledge on Artificial Intelligence. AI has various fundamental application incorporating NLP, healthcare, automotive, gaming, speech recognition, finance, vision system, etc. This is required to design expert systems equipped with the knowledgeable practice that is proficient to acquire, manifest, decipher and justify to its users. It is also required for stimulating devices to identify results for complicated issues like humans do and implement them in the mode of algorithms in computers. There are 6 major branches of AI. The first is Machine Learning, machine learning is the study of computer algorithms that improve automatically through experience. It allows us to categorize, decipher and estimate data from a given dataset. Machine learning algorithms are created by complex mathematical skills that are coded in a machine language in order to make a complete ML system. Next is Neural network, it is either made of biological neurons or artificial neurons for solving AI problems. Neural network generally consists of three layers, input layer, hidden layer, and output layer. next is Robotics, robotics is a combined field of science and engineering for construction, designing, and applications of robots. Robotics deals with computer systems for their control, intelligent outcomes, and valuable data transformation. Next is an Expert System which refers to a computer system that mimics the decision-making intelligence of a human expert. Expert Systems were considered the first successful model of AI software. Expert Systems are built to deal with complex problems like reasoning through the bodies of proficiency. Next is Fuzzy Logic, fuzzy logic is a method of reasoning that resembles human reasoning. Fuzzy Logic is also used for reasoning about naturally uncertain concepts. Lastly, there is Natural Language Processing(NLP). NLP depicts the developing methods that assist in communicating with machines using human languages like English. NLP tasks are text translation, sentiment analysis, and speech recognition. AI systems extend capability by increasing in size and complexity. AI analysis are continuously attempting to build up software systems for diverse applications like automatic learning, knowledge, natural language, and speech recognition. According to Glassdoor.com the estimated total pay for a AI Engineer is $121,578 per year in the remote area, with an average salary of $94,468 per year. These numbers represent the median of the ranges from their proprietary Total Pay Estimate model and based on salaries collected from their users. The estimated additional pay is $27,109 per year. Additional pay can include cash bonus, commission, tips, and profit sharing. According to CompuerScienceDegreeHub.com, if you are interested in AI you are required to have understanding of specific education on math, technology, logic, and engineering perspectives. Written and verbal skills are also vital to convey how AI tools and services are successfully employed within industry settings. If you're interested in acquiring these skills you should investigate the various career choices available within the field.
7/15
Source: towrdasdatascience.com/Donal Byrne
Today I learned how to get an AI job with little or no experience. Although the title says no experience, no one is going to hire someone with absolutely no experience. You can provide yourself with required experience with personal projects, hackathons, coding challenges, and open source projects. You'll definitely need to have ML projects in your Github. This is the first thing recruiters look at after your CV. Your ML project doesn't need to be big, flashy, or innovative, it just needs to display your understanding on the topic and give indication that you're able to work/research independently with acceptable coding standards. A few things to focus on while building a Github project are: the project should take no longer than a month to complete, make sure your code is clean, modular and commented, provide a "Read Me" and other documentation for your code such as technology used, reference tutorials, dependencies etc., and lastly if possible provide units tests for key parts of the code base. Hackathons are great for many reasons, they forces you to go out and build something while getting to meet more experienced people and can you put it on your growing CV/Portfolio. You should try to find AI specific hackathons, but also go to general software hackathons and try to put an AI spin on your project. You can try meeting new groups in your area focusing on AI or software development in general by going on meetup.com. Similar to hackathons, coding challenges force you to build a practical application of what you've learned which is worth a lot when applying for your ML job. As an added bonus, these competitions are pretty fun and the added sense of competition can be a really good motivator. You can take a look at places like Kaggle, CodinGame and Halie.io. Open Source Projects are the closest thing to real world experience that you can get, short of actually getting a job as an ML developer. Open source projects will give you real insight into production level code and will teach you important skills like debugging, versioning control, developing with other people and lots of ML(depending on the project).