<strong>Many junior developers dream of making it to one of the larger tech companies, but, to be honest with you, getting your first job is never easy. It is, in fact, one of the hardest things in your life and you need to put your best effort to find a job in your dream company.</strong>
Many junior developers dream of making it to one of the larger tech companies, but, to be honest with you, getting your first job is never easy. It is, in fact, one of the hardest things in your life and you need to put your best effort to find a job in your dream company.
The single most important reason for failing those coding job interviews is the lack of knowledge and practice. It pretty obvious that if you don’t know what to learn then you are bound to fail, hence it becomes increasingly important that you prepare hard in advance.
Unfortunately, I learned this a little too late, after spoiling my chances at Microsoft and Amazon, but you don’t need to. You can learn from my experience and prepare better for your programming job interviews.
So, the big question is, how do you prepare for coding/programming job interviews? Which subjects should you read up on? Which questions will you need to solve? How do you deal with coding and other technology related questions?
When I was hunting for my first job there wasn’t much help available; we were totally reliant on our textbooks of programming languages and data structure to prepare for interviews, but things have changed in last 10 years.
Nowadays, you not only have dedicated books to prepare for the coding interview, like Crack the Coding Interview Questions, but you have online courses and Coding Bootcamps to practice for coding interviews.
I really like the boot camps because of their methodology, focus, and rigorous practice but they are a bit expensive and not every computer science graduate who is looking for a job can afford that.
Another option is online courses like Software Engineer Interview Unleashed, which are both cheap and provide you similar kind of interactive guidance you get in coding boot camps.
In this article, I am going to share some of the best online courses to prepare for coding/programming job interviews, and based upon your experience and skill set, you can choose one or two courses from this list to prepare for your next job interview.
Most of these courses are focused on data structure and algorithms, which are the most important topics for any coding interview but they also teach you problem-solving and other aspects of Job interview e.g. questions from a programming language like Java and C++, Database, and SQL concepts, Linux commands, etc.
Once you have gone through one of these online training courses, you would have enough knowledge to take on your job interview as well know where to go for further improvement.
This is probably the best coding interview course for Java programmers. Though no programming language is required, if you don’t know Java, the author will teach you.
You will also learn how to reduce the code complexity from one Big-O level to another level, an important skill to impress the interviewer.
Furthermore, you will learn different types of data structures and how to choose the right data structure to solve a problem.
Remember, choosing the right data structure can drastically improve the CPU and memory profile of an application.
For example, using a set to solve duplicate elements problem make it a lot easier. You will also learn how to find Big-O for every data structure.
By the end, you will be able to write code that runs faster and uses low memory. You also will learn how to analyze problems using one technique many programmers forgot to prepare.
This is an ideal course for all levels of programmers, particularly Java programmers. If you are looking for a good programming/coding interview course in Java, this is the one. You can use it to start from scratch or just refresh your knowledge before going to interview.
This is one of the great courses for coding interviews, created by a former Google Interviewer. If you are a software engineer and you are looking for a job on big tech giants like Google, Facebook, SnapChat, or Airbnb, then this is the right course for you.
It is specially designed for college graduates and junior developers who are looking for a job in big technology companies and startups.
You will not only learn data structure and algorithms and other technical information required for an interview but also you will get a chance to see actual examples of phone and onsite interviews and see how they are evaluated.
This one is another good course for preparing technical job interviews. In this course, instructor John Sonmez, author of best-selling book, Soft Skills: The software developer’s life manual has explained what it takes to clear a coding interview.
In this course, you will not only learn how to solve a coding challenge on-the-spot but also learn how to tackle tricky questions from interviewer with respect to complexity and improvement.
The course also includes a blazing fast boot camp for computer science questions about data structures, algorithms, bit manipulation, and concurrency. Overall, a complete package for preparing software job interviews.
It also provides practical tips on dealing with system design interview, which is sometimes hard for beginners given their lack of experience in designing a real-world system.
This is another good interview refresher kind of course for Java and C programmers. Similar to the previous course it also covers essential concepts like pointers, string, linked list, sorting, bit manipulation, data structure, and system design.
Most of the solutions are given in the C programming language and some are given in Java.
This is a good course for fresh engineer graduate and experienced programmers who want to brush up their data structure and algorithm concepts before going for interviews.
Many coding problems can be easily solved if you know dynamic programming but I have found many developers doesn’t even know about it, including some experienced ones.
This course will teach you dynamic programming to improve your algorithms knowledge and prepare for the software engineering coding interview.
You will also learn several 1-dimensional and 2-dimensional dynamic programming problems and how to derive the recurrence relation and write a recursive solution to it, then write a to the problem and code it up in a few minutes.
Some of the dynamic programming problems covered in this course are:
Overall a good course to learn Dynamic programming. You can take this course even if you are not preparing for a coding job interview, just to improve your knowledge of dynamic programming and algorithms. The course uses both Java and Python, so its useful for both Java and Python developers.
This is a data structure, algorithm, and coding interview course specially designed for Python developers. It’s one of the modern course and focuses on things like Github and LinkedIn profile to impress recruits.
It also helps you to create a great resume, which much programmers neglect. Remember, it’s your resume that gives you a chance for an interview, if it’s not good, you won’t even get an interview call.
In this course, you will not only learn all major data structures and algorithms but also ace coding interviews after preparing for the course’s mock interviews. Overall, one of the best coding interview course for Python programmers.
No interview preparation is done until you solve some of the most common questions asked in job interviews. This course provides how to solve 11 such questions in a step-by-step manner.
It’s a short course and I recommend you to take only with other course but it’s good for learning how to approach a completely unknown problem based on your existing knowledge of data structure and algorithms.
SQL queries and database related questions are very common in programming job interviews, hence it’s important for a computer science graduates or programmer to prepare SQL questions in advance.
This course provides 200+ SQL queries and questions for programming job interviews.
This course is particularly for Java programmers or developers who are applying for Java development job.
Since Java is vast it’s not possible to prepare everything, especially in a short duration of time and that’s where this course rocks.
It provides a good sample of 200+ Java interview questions from different areas of Java. Along with this, you can also see my list of 140+ Java Questions from last 5 years, which I have collected myself and with friends and colleagues.
This set is a good representation of what kind of Java questions you can expect in real interviews.
That’s all about some of the best courses to prepare for coding/programming job interviews. As I said, the key to success in the coding interview is an ability to think through the problem and code in real time.
You need a lot of practice to get that. Thankfully, there are a lot of websites where you can practice coding questions. Once you have gone through one of these courses, you can try solving my list of 50 coding problems.
These are some of the best courses to prepare for programming interviews. They will teach you data structure and algorithms that help you to do really well in your interview.
I have also shared a lot of programming interview questions on my blog, so if you are really interested, you can always go there and search for them.
These common coding, data structure, and algorithm questions are the ones you need to know to successfully interview with any company, big or small, for any level of programming job.
If you are looking for a programming or software development job in 2018, you can start your preparation with this list of algorithms and job interview courses.
A good knowledge of data structure and algorithms is important for success in coding interviews and that’s where you should focus most of your attention.
Other Programming Interview Resources you may like:
Thanks, You made it to the end of the article … Good luck with your programming interview! It’s certainly not going to be easy, but by following these courses, you are one step ahead than other candidates.
Originally published by javinpaul at https://dev.to/javinpaul/10-data-structure-algorithms-sql-and-java-courses-to-crack-any-programming-job-interview-11f6
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In this "Python Tutorial: Data Science vs. Web Development" to provide a comparison on the two completely different purposes of using Python language and help understand that it is not necessary to know Python as a web programming language for doing data science in Python.
Python programming has various frameworks and features to expand in web application development, graphical user interfaces, data analysis, data visualization, etc. Python programming language might not be an ideal choice for web application development, but is extensively used by many organizations for evaluating large datasets, for data visualization, for running data analysis or prototyping. Python programming language is gaining traction amongst users for data science whilst being outmoded as a web programming language. The idea of this blog post is to provide a comparison on the two completely different purposes of using Python language and help understand that it is not necessary to know Python as a web programming language for doing data science in Python.Python for Data Science :
Organizations of all sizes and industries — from the top financial institutions to the smallest big data start-ups are using Python programming language to run their business.
Python language is among the popular data science programming languages not only with the top big data companies but also with the tech start up crowd. Python language ranks among the top 10 programming languages to learn in 2019.
Python language comes in the former category and is finding increased adoption in numerical computations, machine learning and several data science applications. Python language can do anything, excluding performance dependent and low level stuff. The best bet to use Python programming language is for data analysis and statistical computations. Learning Python programming for web development requires programmers to master various web frameworks like Django that can help the build websites whereas learning Python for data science requires data scientists to learn the usage of regular expressions, get working with the scientific libraries and master the data visualization concepts. With completely different purposes, programmers or professionals who are not knowledgeable about web programming concepts with Python language can easily go ahead and pursue data science in Python programming language without any difficulty.
Python is a 23-year-old powerful expressive dynamic programming language where a programmer can write the code once and execute it without using a separate compiler for the purpose. Python in web development supports various programming paradigms such as structured programming, functional programming and object oriented programming. Python language code can be easily embedded into various existing web application that require a programming interface. However, Python language is a preeminent choice for academic, research and scientific applications which need faster execution and precise mathematical calculations.
Python web programming requires programmers to learn about the various python web development frameworks, which can be intimidating because the documentation available for the python web development frameworks might be somewhat difficult to understand. However, it is undeniable that to develop a dynamic website or a web application using Python language, learning a web framework is essential.Python Web Development Frameworks
There are several Python web application frameworks available for free like-
Django is the python web development framework for perfectionists with deadlines. Python web development with django is best suited for developing database driven web applications with attractive features like automatic admin interface and a templating system. For web development projects that don’t require extensive features, Django may be an overkill because of its confusing file system and strict directory structure. Some companies that are using python web development with django are The New York Times, Instagram, and Pinterest.
It is a simple and lightweight solution for beginners who want to get started with developing single-page web applications. This framework does not support for validation, data abstraction layer and many other components that various other frameworks include. It is not a full stack framework and is used only in the development of small websites.
It emphasizes on Pythonic conventions so that programmers can build web applications just the way they would do it using object oriented Python programming. CherryPy is the base template for other popular full stack frameworks like TurboBears and Web2py.
There are so many other web frameworks like Pyramid, Bottle, and Pylons etc. but regardless of the fact, whichever web framework a python programmer uses, the challenge is that he/she needs to pay close attention to detailing on the tutorials and documentation.Why Web Development with Python is an impractical choice?
Python programming language probably is an impractical choice for being chosen as a web programming language –
Python for web development requires non-standard and expensive hosting particularly when programmers use popular python web frameworks for building websites. With PHP language being so expedient for web programming, most of the users are not interested in investing in Python programming language for web development.
Python language for web development is not a commonly demanded skill unlike demand for other web development languages like PHP, Java or Ruby on Rails. Python for Data science is gaining traction and is the most sought after skill companies are looking for in data scientists, with its increased adoption in machine learning and various other data science applications.
Python for web development has come a long way but it does not have a steep learning curve as compared to other web programming languages like PHP.
Why Python for Data Science is the best fit?
Python programming is the core technology that powers big data, finance, statistics and number crunching with English like syntax. The recent growth of the rich Python data science ecosystem with multiple packages for Machine learning, natural language processing, data visualization, data exploration, data analysis and data mining is resulting in Pythonification of the data science community. Today, Python data science language has all the nuts and bolts for cleaning, transforming, processing and crunching big data. Python is the most in-demand skill for data scientist job role. A data scientist with python programming skills in New York earns an average salary of $180,000Why data scientists love doing data science in Python language?
Data Scientists like to work in a programming environment that can quickly prototype by helping them jot down their ideas and models easily. They like to get their stuff done by analysing huge datasets to draw conclusions. Python programming is the most versatile and capable all-rounder for data science applications as it helps data scientists do all this productively by taking optimal minimal time for coding, debugging, executing and getting the results.
The real value of a great enterprise data scientist is to use various data visualizations that can help communicate the data patterns and predictions to various stakeholders of the business effectively, otherwise it is just a zero-sum game. Python has almost every aspect of scientific computing with high computational intensity which makes it a supreme choice for programming across different data science applications, as programmers can do all the development and analysis in one language. Python for data science links between various units of a business and provides a direct medium for data sharing and processing language.
Data analysis and Python programming language go hand in hand. If you have taken a decision to learn Data Science in Python language, then the next question in your mind would be –What are the best data science in Python libraries that do most of the data analysis task? Here are top data analysis libraries in Python used by enterprise data scientists across the world-
It is the foundation base for the higher level tools built in Python programming language. This library cannot be used for high level data analysis but in-depth understanding of array oriented computing in NumPy helps data scientists use the Pandas library effectively.
SciPy is used for technical and scientific computing with various modules for integration, special functions, image processing, interpolation, linear algebra, optimizations, ODE solvers and various other tasks. This library is used to work with NumPy arrays with various efficient numerical routines.
This is the best library for doing data munging as this library makes it easier to handle missing data, supports automatic data alignment, supports working with differently indexed data gathered from multiple data sources.
This is a popular machine learning library with various regression, classification and clustering algorithms with support for gradient boosting, vector machines, naïve Bayes, and logistic regression. This library is designed to interoperate with NumPy and SciPy.
It is a 2D plotting library with interactive features for zooming and panning for publication quality figures in different hard copy formats and in interactive environments across various platforms.
Learn Data Science | How to Learn Data Science for Free. In this post, I have described a learning path and free online courses and tutorials that will enable you to learn data science for free.
The average cost of obtaining a masters degree at traditional bricks and mortar institutions will set you back anywhere between $30,000 and $120,000. Even online data science degree programs don’t come cheap costing a minimum of $9,000. So what do you do if you want to learn data science but can’t afford to pay this?
I trained into a career as a data scientist without taking any formal education in the subject. In this article, I am going to share with you my own personal curriculum for learning data science if you can’t or don’t want to pay thousands of dollars for more formal study.
The curriculum will consist of 3 main parts, technical skills, theory and practical experience. I will include links to free resources for every element of the learning path and will also be including some links to additional ‘low cost’ options. So if you want to spend a little money to accelerate your learning you can add these resources to the curriculum. I will include the estimated costs for each of these.
The first part of the curriculum will focus on technical skills. I recommend learning these first so that you can take a practical first approach rather than say learning the mathematical theory first. Python is by far the most widely used programming language used for data science. In the Kaggle Machine Learning and Data Science survey carried out in 2018 83% of respondents said that they used Python on a daily basis. I would, therefore, recommend focusing on this language but also spending a little time on other languages such as R.
Before you can start to use Python for data science you need a basic grasp of the fundamentals behind the language. So you will want to take a Python introductory course. There are lots of free ones out there but I like the Codeacademy ones best as they include hands-on in-browser coding throughout.
I would suggest taking the introductory course to learn Python. This covers basic syntax, functions, control flow, loops, modules and classes.
Next, you will want to get a good understanding of using Python for data analysis. There are a number of good resources for this.
To start with I suggest taking at least the free parts of the data analyst learning path on dataquest.io. Dataquest offers complete learning paths for data analyst, data scientist and data engineer. Quite a lot of the content, particularly on the data analyst path is available for free. If you do have some money to put towards learning then I strongly suggest putting it towards paying for a few months of the premium subscription. I took this course and it provided a fantastic grounding in the fundamentals of data science. It took me 6 months to complete the data scientist path. The price varies from $24.50 to $49 per month depending on whether you pay annually or not. It is better value to purchase the annual subscription if you can afford it.
If you have chosen to pay for the full data science course on Dataquest then you will have a good grasp of the fundamentals of machine learning with Python. If not then there are plenty of other free resources. I would focus to start with on scikit-learn which is by far the most commonly used Python library for machine learning.
When I was learning I was lucky enough to attend a two-day workshop run by Andreas Mueller one of the core developers of scikit-learn. He has however published all the material from this course, and others, on this Github repo. These consist of slides, course notes and notebooks that you can work through. I would definitely recommend working through this material.
Then I would suggest taking some of the tutorials in the scikit-learn documentation. After that, I would suggest building some practical machine learning applications and learning the theory behind how the models work — which I will cover a bit later on.
SQL is a vital skill to learn if you want to become a data scientist as one of the fundamental processes in data modelling is extracting data in the first place. This will more often than not involve running SQL queries against a database. Again if you haven’t opted to take the full Dataquest course then here are a few free resources to learn this skill.
Codeacamdemy has a free introduction to SQL course. Again this is very practical with in-browser coding all the way through. If you also want to learn about cloud-based database querying then Google Cloud BigQuery is very accessible. There is a free tier so you can try queries for free, an extensive range of public datasets to try and very good documentation.
To be a well-rounded data scientist it is a good idea to diversify a little from just Python. I would, therefore, suggest also taking an introductory course in R. Codeacademy have an introductory course on their free plan. It is probably worth noting here that similar to Dataquest Codeacademy also offers a complete data science learning plan as part of their pro account (this costs from $31.99 to $15.99 per month depending on how many months you pay for up front). I personally found the Dataquest course to be much more comprehensive but this may work out a little cheaper if you are looking to follow a learning path on a single platform.
It is a good idea to get a grasp of software engineering skills and best practices. This will help your code to be more readable and extensible both for yourself and others. Additionally, when you start to put models into production you will need to be able to write good quality well-tested code and work with tools like version control.
There are two great free resources for this. Python like you mean it covers things like the PEP8 style guide, documentation and also covers object-oriented programming really well.
The scikit-learn contribution guidelines, although written to facilitate contributions to the library, actually cover the best practices really well. This covers topics such as Github, unit testing and debugging and is all written in the context of a data science application.
For a comprehensive introduction to deep learning, I don’t think that you can get any better than the totally free and totally ad-free fast.ai. This course includes an introduction to machine learning, practical deep learning, computational linear algebra and a code-first introduction to natural language processing. All their courses have a practical first approach and I highly recommend them.
Whilst you are learning the technical elements of the curriculum you will encounter some of the theory behind the code you are implementing. I recommend that you learn the theoretical elements alongside the practical. The way that I do this is that I learn the code to be able to implement a technique, let’s take KMeans as an example, once I have something working I will then look deeper into concepts such as inertia. Again the scikit-learn documentation contains all the mathematical concepts behind the algorithms.
In this section, I will introduce the key foundational elements of theory that you should learn alongside the more practical elements.
The khan academy covers almost all the concepts I have listed below for free. You can tailor the subjects you would like to study when you sign up and you then have a nice tailored curriculum for this part of the learning path. Checking all of the boxes below will give you an overview of most elements I have listed below.
Calculus is defined by Wikipedia as “the mathematical study of continuous change.” In other words calculus can find patterns between functions, for example, in the case of derivatives, it can help you to understand how a function changes over time.
Many machine learning algorithms utilise calculus to optimise the performance of models. If you have studied even a little machine learning you will probably have heard of Gradient descent. This functions by iteratively adjusting the parameter values of a model to find the optimum values to minimise the cost function. Gradient descent is a good example of how calculus is used in machine learning.
What you need to know:
Many popular machine learning methods, including XGBOOST, use matrices to store inputs and process data. Matrices alongside vector spaces and linear equations form the mathematical branch known as Linear Algebra. In order to understand how many machine learning methods work it is essential to get a good understanding of this field.
What you need to learn:
Vectors and spaces
Here is a list of the key concepts you need to know:
The third section of the curriculum is all about practice. In order to truly master the concepts above you will need to use the skills in some projects that ideally closely resemble a real-world application. By doing this you will encounter problems to work through such as missing and erroneous data and develop a deep level of expertise in the subject. In this last section, I will list some good places you can get this practical experience from for free.
“With deliberate practice, however, the goal is not just to reach your potential but to build it, to make things possible that were not possible before. This requires challenging homeostasis — getting out of your comfort zone — and forcing your brain or your body to adapt.”, Anders Ericsson, Peak: Secrets from the New Science of Expertise
Machine learning competitions are a good place to get practice with building machine learning models. They give access to a wide range of data sets, each with a specific problem to solve and have a leaderboard. The leaderboard is a good way to benchmark how good your knowledge at developing a good model actually is and where you may need to improve further.
The UCI machine learning repository is a large source of publically available data sets. You can use these data sets to put together your own data projects this could include data analysis and machine learning models, you could even try building a deployed model with a web front end. It is a good idea to store your projects somewhere publically such as Github as this can create a portfolio showcasing your skills to use for future job applications.
One other option to consider is contributing to open source projects. There are many Python libraries that rely on the community to maintain them and there are often hackathons held at meetups and conferences where even beginners can join in. Attending one of these events would certainly give you some practical experience and an environment where you can learn from others whilst giving something back at the same time. Numfocus is a good example of a project like this.
In this post, I have described a learning path and free online courses and tutorials that will enable you to learn data science for free. Showcasing what you are able to do in the form of a portfolio is a great tool for future job applications in lieu of formal qualifications and certificates. I really believe that education should be accessible to everyone and, certainly, for data science at least, the internet provides that opportunity. In addition to the resources listed here, I have previously published a recommended reading list for learning data science available here. These are also all freely available online and are a great way to complement the more practical resources covered above.
Thanks for reading!