Just this morning Microsoft announced a new program in conjunction with EdX that has several courses to teach you all things AI.
There are several intersting courses that make up this program, including the math used and ethics.
I'm excited to start this program soon and am sure I will learn a lot from it.
Microsoft has two data related certifications in their Microsoft Professional Program - one for Data Science and another for Big Data. I recently completed the one for data science and want to share my experience to others interested in doing the same.
The data science program consists of 10 courses, with one being an orientation and the last being a final capstone project. In order to get credit you do need to receive a passing grade (70% for all courses other than the capstone in which you need 76%), and to purchase a verified certificate. The certificates cost $99 so the whole course will cost you $990. A good chunk of change if your employer won't reimburse you on it, so I would definitely check that first before committing the time.
The main course materials cover the following:
The courses all differ in how they do the grades, but usually they have either a lab or quiz after each section and a final exam at the end of the course. The questions usually allow you two chances to get the correct answer, but some may only offer one.
Most of the courses are easy as long as you pay attention to the lectures and actually do the labs. The instructors were all knowledgable and presented the materials well, however, I think there was an issue with the materials involving the math and statistics.
To go over the math portions to try to explain a concept, they showed slides of the formulas and just talked about what the formulas mean...and that's basically it. I don't think this was needed at all since the concepts were explained much better once they were put as demos. Just going over formulas isn't the best way to teach any kind of math. Showing the applications of it, though, is. Then if you need to go back to the formula you can while relating it to the application.
Speaking of which, the statistics course was the hardest for me. I barely made a passing grade. The course structure was slightly different
A couple of things that may be useful to you if you do decide to take this course:
Would I take this course again if I needed to? I probably would, but I love to learn. I did learn a lot of things, such as tuning models in Azure Machine Learning, how awesome PowerBI is and all the things you can do with it, and time series analysis concepts.
In the end, you get a certificate that you can share on LinkedIn or to your boss. However, keep in mind that there's still tons more to learn and projects to work on to futher solidify the concepts from this course.
A lot of programming books teach you about a new framework, language, or computer science theory, but very few teach you how to actually be effective at your day job. The book, The Effective Engineer is a great one to learn all the ways in which you can be the best developer you can be.
This book goes through three main points:
Each of these has some really good and actionable insights on how you can become better as a programmer.
As Carol Dweck mentions in her book Mindset, the most successful people have a certain mindset in terms of how they view their learning. They believe that your brain can be changed and you can learn anything once you put in the work. That's definitely true for programmers.
A large part of being a software developer is that you are continuously learning new things. Whether new frameworks, languages, or software theory, there is no shortage of things to learn.
A majority of the time this learning comes from actual work on the job. No longer being able to learn on the job is a reason a lot of developers change jobs, so it's definitely cared about. Sometimes, though, that learning may need to be supplemented outside of the job. This is where you hear some developers doing projects and learning during nights and weekends.
The book goes through a few ways developers can actually do this learning, such as reading code from developers who are more experienced and to have them review your own code.
One of my favorite authors, Tim Ferriss often talks about being efficient vs. being effective to help boost productivity:
Focus on doing the right things (efficiency) vs doing things well (being efficient).
Doing things to be efficient may include items such as replying to emails, updating status reports more than they need to be, and other tasks that make it seem like you're busy but doesn't actually contribute much to the project you're working on.
That's where prioritization comes in. Once you take some time each day or week to prioritize what needs to be done, you'll have a clear way to move forward with the actual project.
This is similar to Getting Things Done in that you define clear tasks and prioritize the ones that need to be done. I would also argue to break up bigger tasks into much smaller ones.
Now that we have the right mindset for our productivity, we also need to know how to execute them. A few things from the book stand out in what I believe will help the most.
Do you use Visual Studio for all of our development? Then knowing as much as you can about it can save you so much time in terms of keyboard shortcuts or knowing when to move to the command line for certain tasks. Speaking of the command line, knowing PowerShell or bash can save you a ton of time as there are snippets and commands that can do a lot that you may not even know of.
All too often you, as a developer, will get asked by your boss or a manager how long a certain task will take. How often have you said, "I don't know?" Improving your estimation skills is an easy way to be thought of as a senior programmer.
The main way to improve this is to just keep track of your tasks. Once you are asked to estimate you can refer back to your tasks and get an idea of how long it took you to do something similar.
There's also the book Software Estimation that can give a lot more details in improving your estimation details.
To be the best programmer you can be is to build long term value for your company and your clients. The book offers some great ways in order to help you build that long term value.
How often have you come across this scenario: you implement a new feature and the deadline is very close, however your boss wants you to go ahead and get it in to start testing. You mention how many more tests you need to write, yet your boss says to not worry about that and just check it in. Often referred to as technical debt, us programmers can perfectionists in that we want our code to be perfect before we send out for all to work on and see.
Having this type of balance on having your work be perfect vs. being practical in your work can be beneficial in terms of providing value to your company or clients.
I think the biggest thing you can do for long term value is investing in the team. This can be done in many ways, such as the following:
Of course there are other ways to help invest in your team, but these are some of the ways that can have the most impact.
This book is definitely worth reading for a lot of ideas on how you can easily improve your effectiveness as a programmer. For even more you can checkout the book's blog.
When I went to college I had to take math. My degree is actually in Math and Computer Science. Why the math part, you ask? Well, the folks at the school figured that the math will help with a lot of the logic that comes with programming.
Of course, there's also that age old question when you first start learning programming...is there a lot of math involved? The answer is it depends. If you're planning to get a doctorate and do a lot of research, then you'll most likely use a lot of it. However, if you're just working for a company, then chances are you won't need hardly any math*. If you're learning data science then some math is essential in order to gain insights from data or to understand machine learning algorithms.
In this post, we'll go over the most common types of math you may want to be familiar with to get the most out of your programming, and where you can start learning these concepts.
Discrete math is used quite a bit in programming. Whether it's understanding the theory of how integers work in programming languages or using boolean logic, you've probably come across it before.
Coursera comes to the rescue here as there may not be too much around discrete math.
Yep, even plain old algebra you probably didn't like in high school can be helpful. I actually have an example of this: back when I was doing tax software I needed to use some basic algebra to create a function. Of course, tax software relies heavier on math than most other software, so it's not unusual. Knowing that bit of algebra that I needed, though, really helped when creating the function and I may have had to spend a good bit of time Googling for some help.
Kahn Academy is well known for their math courses and they definitely have a good algebra one.
For a book, Practical Algebra looks to be a good one to brush back up on your algebra.
Statistics is needed for data scientists, not only to help get insights from your data but to also make sure variables that seem correlated to each other actually are statistically significat rather than not. That's not the easiest to do even if you plot your data. Statistics will give you a big advantage in understanding your data and how it can answer any questions you can throw at your data.
Need to see if an A/B test has a significant difference? Then statistical hypothesis testing can be of a great help.
Coursera has a great intro to data course that has really helped me out in learning some of the basics of statistics. The book they recommend, too, is actually really good.
As for books, I recommend a couple to start with. Practical Statistics is a great introduction. If you want to focus more on the data science side of statistics then Practical Statistics for Data Scientists is a great one to get.
If you want to get a bit advanced, then I definitely recommend Introduction to Statistical Learning. This is mainly for getting deep in the machine learning algorithms, but still is an interesting read. You can also read it for free online instead of getting a physical copy. There's an even more advanced version of this too with The Elements of Statistical Learning, which is also available for free online.
Linear algebra is a bit of a niche in programming. The only place I've really seen it used is for machine learning algorithms. Linear algebra, is mainly matrix manipulation.
Khan Academy has some linear algebra courses that you can take. This is probably the most complete of them I've seen around.
For a book, mostly what you'll see are text books. That's not all bad, but sometimes a more general book is helpful. In that case there's Linear Algebra for Dummies.
While math isn't necessary for programming, I believe it can certainly help with the logic like my university thought. Learning the extra math also made me appreciate it more for what all math can teach us about the world. Also, don't think you're not good at math. This old post from Steve Yegge explains more about the way math is taught in schools and what we can do about it as programmers. You're not bad at math, you just need a better way to learn it.
* Though, depending on the company you work for you may need it. For example, working for a financial institution may involve some knowledge of math.
Pluralsight has certainly grown over the years. I remember when it first started and it had only courses that were just .NET based and maybe a few others within the Microsoft stack. Now they cover, not only a wide range of development topics, but a big range of creative, admin, and business topics. With that, several videos about how you can get better at your career and as a developer have come up. Here are my favorite courses that I feel give great, actionable advice on how to get better.
This is a great course that is very actionable. Cory goes into several things that you can use right after you watch it on how to make yourself an outlier, or an above average developer. Of course, it's going to be some work, but all that work will pay off. I tend to refer to this course when I need a bit of extra motivation to be an outlier.
The Play by Play is an interesting concept. Usually it's where you follow a developer as they are making an application or doing something similar to their day-to-day programming and they talk through what they're doing and what they're thinking. In this case, it's more of an informal discussion where Troy Hunt and Lars Klint discuss how they update their brand and image to rise above average and help get themselves noticed. They talk about their experiences so you can learn from them and incorporate them into your own career.
Programming is mostly a job of learning. Developers are constatly learning new technologies and frameworks. In this course, Iris Classon and Scott Allen help you learn how to learn in terms of programming. Probably the best section is the "Learning Plan" section. You can't really learn that much if you don't have a plan, from going through tutorials, practicing, and retaining what you've done.
One of the best things to do while being a programmer is to, well, get involved. There are a ton of ways to get invovled and this course details those ways. From the usual getting into open source projects to participating in local user groups. Why get involved? Getting involved will help your personal brand, which the first two courses go into much deeper. Getting involved gets you known around the community. Getting involved with the local community lets you meet people easier and, who knows, you could be talking with a member of the community you meet and come up with the next innovation in programming.
Programming is a very dyanmic career. The reason developers are always learning is that new technologies and frameworks are always coming out for us to learn. One of those will become very popular and you would need to get up-to-speed quite quickly to meet the needs of a client or employer. This course was done in 2015 and predicts quite a few things that are still emerging, such as virtual reality, big data or data science, and artificial intelligence.
UPDATE: Dan Appleman just released an awesome new course on keeping up with technology. It's done very well and goes well with the other courses in this post.
PluralSight definitely offers a lot and new courses are added each day. While I wish they offered more in terms of exercises or projects, they are top notch in terms of video tutorials. The ones I highlighted in this post are great to help you use the other more techical courses that they offer. I hope you get as much out of these courses as much as I have!
Watching and reading programming tutorials are great! They give a curated view of a concept or new technology that may take hours longer to understand than going at it alone. However, much like math, programming is not a spectator sport. You need to practice in order to better understand the programming concept or technology in a real program.
There are a lot of ways one can do this, though, without having to fully set up an environment for each language or framework you want to learn.
Here are 10 sites that you can use to start learning new languages and other programming concepts.
Code Wars profile view.
This is one of my favorite sites to go to, especially when I want some practice with a new language. They have most of the common languages already supported with a few more in beta so most likely you can get some practice in here.
All of the "katas", as they call the challenges, have always been challenging for me. Plus seeing other people's solutions I'm sure to learn something new.
Hacker Rank is another code challenge site, but a tad bit different. They also have competitions throughout the year where you can participate and see how you rank among other competitors. I've also seen some companies use this site as a first pass for interviewing candidates.
The nice thing about this site is that it pretty much emphasizes algorithms and data structures, which I consider two things that you could get the most out of if you practice it.
TopCoder compete login page.
TopCoder is similar to HackerRank, but you can actually get paid for the challenges you compete in. This is a good way to see how you compare with other developers in the challenges and to challenge yourself to get better at certain types of programming, such as data structures, math, and string manipulations.
Kaggle is more suited for data scientists, but it's a place where you can find data to play around with. They also host their own competitions that can pay out.
You don't need to use this for data science, though. Since they offer a lot of data sets you can use them in for other applications as well. Want to create a web app with some of the data? Go for it!
Project Euler is probably one of the first sites with programming challenges and puzzles. This site is composed of mathematical problems that you can solve in code and the further you go the harder the math problems.
Very similar to Project Euler, Rosalind gives challenges in regards to problems in bioinformatics. These challenges may be considsered a bit better since they solve more real world problems than just arbitrary math problems in Project Euler.
This is a fun one, especially if you are really interested in game programming. This site gives you the opportunity to code exercises as part of a Real Time Strategy game with JavaScript. Game programming is a totally different beast than doing web sites or any other business applications as it's a different way of thinking and experience that goes with it. This is especially great to get started with some game programming when you don't necessarily have a project of your own to work on.
CodeChef is similar to TopCoder where they host several competitions and you can get paid for completing them. CodeChef holds those competitions monthly so you'll always have a chance to see how you stand against other programmers. There are definitely plenty of problems to practice before going into a competition and they are all ranked from easy to hard.
CodinGame is fairly similar to how Code Wars works, but the challenges are a bit more of a game. They are more turn based so with each "turn" you get new input and you must give new output based off of it. Similar to Screeps above, the challenges are like mini games you help code. When you run your test cases you can see your code in action as the game is being played, so you get some instant, and visual, feedback to how your code performs.
Ok, so this isn't much of an exercise site, but I feel that it's important enough to mention in this post. Why? Working on open source projects is one of the best things you can do as a developer, and this site helps make it easy. A lot of projects have been creating an "up-for-grabs" label on GitHub to let newer contributors get into their projects a lot easier by fixing easier bugs. Doing these will help you become a regular contributor to the project which will have more real world experience than doing other algorithmic or mathematical exercises.
Jonathan is a normal software developer residing in Columbia, SC. During the day he creates software using the Microsoft technology stack: by night he's doing martial arts or off learning and experiencing new things. He can also be found taking long walks around the parking lot or just sitting around reading.
Jon is a random software developer in North Carolina developing in C# for web and machine learning with ML.NET. I also post videos on YouTube.
