I recently began studying for technical interviews. I thought I’d begin a blog series and break apart data structure and algorithm questions from beginner to intermediate in as simple terms as possible. Hopefully, I’ll be able to help out some people along the way.
After landing my first technical interview with Asana last month, I realized that I was extremely underprepared. I had no idea what I had gotten myself into. Over the course of the next month, I began to tackle algorithms every morning using a Udemy course. Whether or not I could solve those questions immediately did not matter to me, but the mentality was to keep trying and keep learning so that eventually I’ll be able to solve them.
The first question that came to my mind after I graduated from Flatiron and began looking into data structure and algorithm courses was: why should I even bother learning this? After all, no software engineer is going to actually need these super complex algorithms to create their code… right? Well not so surprisingly, it isn’t actually all about code. Sure, understanding how to manipulate data demonstrates foundational coding knowledge. But really understanding how to perform complex search algorithms, or navigate through a tree of data, demonstrates to interviewers that not only are you fluent in the coding language you’re using, but also that you’re dedicated to software engineering. Furthermore, it shows a willingness to learn things that are complex and not give up. Understanding data structures and algorithms can also reveal your knowledge of time/space complexity, two core concepts in building applications. In my next blog in this series, I’m going to try and explain time and space complexity, as both are crucial to understanding why data structures and algorithms are necessary. So to sum that answer up, being well-versed with data structures and algorithms is key to demonstrating your skills as a software engineer.
#algorithms #coding #programming #software-engineering #data-structures
If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition
The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.
This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.
As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).
This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.
#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management
If I ask you what is your morning routine, what will you answer? Let me answer it for you. You will wake up in the morning, freshen up, you’ll go for some exercise, come back, bath, have breakfast, and then you’ll get ready for the rest of your day.
If you observe closely these are a set of rules that you follow daily to get ready for your work or classes. If you skip even one step, you will not achieve your task, which is getting ready for the day.
These steps do not contain the details like, at what time you wake up or which toothpaste did you use or did you go for a walk or to the gym, or what did you have in your breakfast. But all they do contain are some basic fundamental steps that you need to execute to perform some task. This is a very basic example of algorithms. This is an algorithm for your everyday morning.
In this article, we will be learning algorithms, their characteristics, types of algorithms, and most important the complexity of algorithms.
Algorithms are a finite set of rules that must be followed for problem-solving operations. Algorithms are step-by-step guides to how the execution of a process or a program is done on a machine to get the expected output.
No one would follow any written instructions to follow a daily morning routine. Similarly, you cannot follow anything available in writing and consider it as an algorithm. To consider some instructions as an algorithm, they must have some specific characteristics :
1. Input: An algorithm, if required, should have very well-defined inputs. An algorithm can have zero or more inputs.
2. Output: Every algorithm should have one or more very well-defined outputs. Without an output, the algorithm fails to give the result of the tasks performed.
3. Unambiguous: The algorithm should be unambiguous and it should not have any confusion under any circumstances. All the sentences and steps should be clear and must have only one meaning.
4. Finiteness: The steps in the algorithm must be finite and there should be no infinite loops or steps in the algorithm. In simple words, an algorithm should always end.
5. Effectiveness: An algorithm should be simple, practically possible, and easy to understand for all users. It should be executable upon the available resources and should not contain any kind of futuristic technology or imagination.
6. Language independent: An algorithm must be in plain language so that it can be easily implemented in any computer language and yet the output should be the same as expected.
1. Problem: To write a solution you need to first identify the problem. The problem can be an example of the real-world for which we need to create a set of instructions to solve it.
2. Algorithm: Design a step-by-step procedure for the above problem and this procedure, after satisfying all the characteristics mentioned above, is an algorithm.
3. Input: After creating the algorithm, we need to give the required input. There can be zero or more inputs in an algorithm.
4. Processing unit: The input is now forwarded to the processing unit and this processing unit will produce the desired result according to the algorithm.
5. Output: The desired or expected output of the program according to the algorithm.
Suppose you want to cook chole ( or chickpeas) for lunch. Now you cannot just go to the kitchen and set utensils on gas and start cooking them. You must have soaked them for at least 12 hours before cooking, then chop desired vegetables and follow many steps after that to get the delicious taste, texture, and nutrition.
This is the need for algorithms. To get desired output, you need to follow some specific set of rules. These rules do not contain details like in the above example, which masala you are using or which salt you are using, or how many chickpeas you are soaking. But all these rules contain a basic step-by-step guide for best results.
We need algorithms for the following two reasons :
1. Performance: The result should be as expected. You can break the large problems into smaller problems and solve each one of them to get the desired result. This also shows that the problem is feasible.
2. Scalability: When you have a big problem or a similar kind of smaller problem, the algorithm should work and give the desired output for both problems. In our example, no matter how many people you have for lunch the same algorithm of cooking chickpeas will work every single time if followed correctly.
Let us try to write an algorithm for our lunch problem :
1. Soak chickpeas in the night so that they are ready till the next afternoon.
2. Chop some vegetables that you like.
3. Set up a utensil on gas and saute the chopped vegetables.
4. Add water and wait for boiling.
5. Add chickpeas and wait until you get the desired texture.
6. Chickpeas are now ready for your lunch.
The real-world example that we just discussed is a very close example of the algorithm. You cannot just start with step 3 and start cooking. You will not get the desired result. To get the desired result, you need to follow the specific order of rules. Also, each instruction should be clear in an algorithm as we can see in the above example.
#algorithms in data structure #data structure algorithms #algorithms
Companies across every industry rely on big data to make strategic decisions about their business, which is why data analyst roles are constantly in demand. Even as we transition to more automated data collection systems, data analysts remain a crucial piece in the data puzzle. Not only do they build the systems that extract and organize data, but they also make sense of it –– identifying patterns, trends, and formulating actionable insights.
If you think that an entry-level data analyst role might be right for you, you might be wondering what to focus on in the first 90 days on the job. What skills should you have going in and what should you focus on developing in order to advance in this career path?
Let’s take a look at the most important things you need to know.
#data #data-analytics #data-science #data-analysis #big-data-analytics #data-privacy #data-structures #good-company
CVDC 2020, the Computer Vision conference of the year, is scheduled for 13th and 14th of August to bring together the leading experts on Computer Vision from around the world. Organised by the Association of Data Scientists (ADaSCi), the premier global professional body of data science and machine learning professionals, it is a first-of-its-kind virtual conference on Computer Vision.
The second day of the conference started with quite an informative talk on the current pandemic situation. Speaking of talks, the second session “Application of Data Science Algorithms on 3D Imagery Data” was presented by Ramana M, who is the Principal Data Scientist in Analytics at Cyient Ltd.
Ramana talked about one of the most important assets of organisations, data and how the digital world is moving from using 2D data to 3D data for highly accurate information along with realistic user experiences.
The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment, 3D data for object detection and two general case studies, which are-
This talk discussed the recent advances in 3D data processing, feature extraction methods, object type detection, object segmentation, and object measurements in different body cross-sections. It also covered the 3D imagery concepts, the various algorithms for faster data processing on the GPU environment, and the application of deep learning techniques for object detection and segmentation.
#developers corner #3d data #3d data alignment #applications of data science on 3d imagery data #computer vision #cvdc 2020 #deep learning techniques for 3d data #mesh data #point cloud data #uav data