Courses

In this video (Collection Framework in Java) we will discuss about the most popular collection framework in Java and also cover the basics of Java that are required! So, if you’ve just finished learning the basics of Java. The question now is, what do you do now? How can you continue to keep developing your coding skills using Java? This video will answer each and every question that you may have on your mind. It will give you some inspiration and ideas about Java and its applications. In the future, you can get started working on a new Java project and apply your knowledge of Java you learned here on some advanced projects. In this tutorial, we will learn about what is collection framework, why do we use then, and in the end, we will cover an end to end project to cover all the concepts

Explore 12 essential Laravel collection methods in this comprehensive video tutorial. Learn how to leverage the power of Laravel collections, which offer enhanced functionality compared to standard PHP arrays. Discover techniques for creating method chains to efficiently map and reduce arrays. Follow along as the instructor demonstrates each method, including all(), avg(), chunk(), combine(), contains(), count(), diff(), dd(), flip(), forget(), each(), map(), filter(), pluck(), isEmpty(), and isNotEmpty(). Gain practical insights through examples, including database setup, seeding, and chaining multiple methods together. Master these powerful collection methods to streamline your Laravel development process and write more efficient code.

Explore the power of Clojure collections in this course. Delve into lists, vectors, maps, sets, and sequences. Master operations like adding and removing items, accessing, and transforming data efficiently. This course is geared to enhance your ability to manage and process data, boosting your Clojure programming skills.

In today's fast-paced world of programming, handling and organizing data efficiently is a challenge that every developer faces. In this course, Python Collections, you’ll learn how to optimize and enhance your data handling skills in Python. First, you’ll explore the foundational aspects of Python's built-in container types: lists, dictionaries, tuples, and sets, understanding their unique characteristics and best use cases. Next, you’ll discover advanced dictionary types such as defaultdict, OrderedDict, and Counter, diving into their specialized functionalities that streamline common programming tasks. Finally, you’ll learn how to leverage specialized collection classes like namedtuple, ChainMap, and deque, enhancing the efficiency and readability of your code. When you’re finished with this course, you’ll have the skills and knowledge of Python's container data types needed to create elegant, efficient solutions in your programming projects.

Explore the evolution of Java's Collections Framework in this 51-minute Devoxx conference talk. Dive into recent enhancements, focusing on Java 9 additions like JEP 269's Convenience Factory Methods. Learn about minor streams improvements and get a glimpse of potential future updates to Collections and Streams APIs. Gain insights from Stuart Marks, a Principal Member of Technical Staff in Oracle's Java Platform Group, as he shares his expertise on core libraries, including Collections, Lambda, and Streams. Understand how the Collections Framework, introduced in 1998, continues to adapt and remain relevant in modern Java development.

Explore the evolution and enhancements of Java's Collections Framework in this comprehensive Devoxx conference talk. Delve into the lambda-capable default methods introduced in Java 8, upcoming improvements for Java 9 including JEP 269 Convenience Factory Methods, and potential future developments. Learn about enhanced for-loops, Iterable interfaces, and new Map methods. Examine design goals, implementation issues, and rationale behind changes to Collections. Gain insights from Stuart Marks, a Principal Member of Technical Staff in Oracle's Java Platform Group, as he shares his expertise on core libraries projects, deprecation mechanisms, and the ongoing refinement of this fundamental Java component.

Learn how to maintain structured collections of data in C# and choose the right collection class for a wide variety of programming scenarios.

Almost every application in the world needs to manipulate data in some sort of collection. This course will teach you how to use Scala's sophisticated collection types and take advantage of the functional programming style Scala is renowned for. If you’re a programmer targeting the JVM, you'll be familiar with Java and its collection types. In this course, Scala Collections, you'll learn about Scala's power and its collections that will help your next application. First, you'll discover Scala lists and how to find elements in them. Next, you'll explore collection types and how to choose which is right for you. Finally, you'll learn how to transform elements and simplify complex computations. When you're finished with this course, you'll have the knowledge required to use Scala's collections and some of the functional programming idioms that power them.

This eight-course Professional Certificate from IBM prepares you with job-ready skills for an entry level data analyst role. Position yourself competitively and power your data analyst career for a job in a thriving market or leverage foundational data skills to explore problems in an increasingly data-driven professional world. The U.S. Bureau of Labor Statistics projects a growth rate of 20% in the data analytics industry until 2028. You will learn the core principles of data analysis, participate in hands-on skills-based practice, and gain the knowledge to help companies make smarter business decisions. You will work with a variety of data sources, project scenarios, and data analysis tools, including Excel, SQL, Python, Jupyter Notebooks, and Cognos Analytics. These online learning tools will offer you practical experience with data manipulation and the application of analytical and data visualization techniques. This Professional Certificate does not require any prior programming or statistical skills, and is suitable for learners with or without college degrees. A willingness to learn, basic computer literacy, comfort working with numbers, high school math, and a desire to enrich your profile with valuable skills is all you need to get started in this course. With the IBM Data Analyst Professional Certificate you will develop the confidence and portfolio to begin a career as an associate or junior data analyst by learning the essential skills that help professionals in a wide range of job functions and industries explore business problems.

Clean up messy data, uncover patterns and insights, and communicate your findings. You'll start with an introduction to data analysis tools, including Jupyter Notebook, NumPy, pandas, and Matplotlib. Using these tools, you will ask questions about data and answer them through data collection, exploration, wrangling, and visualization. This intermediate-level program includes real-world projects where you will choose your own datasets, research questions, and analysis approach. As you progress through the program, each course will repeat the data analysis process while introducing more advanced techniques, such as applying data imputation to fill in missing data and applying appropriate encodings when developing data visualizations.

The goal of the Security Analyst Nanodegree Program is to equip students with the skills required to be successful in an analyst role.

The US Bureau of Labor Statistics projects a 23% increase in cybersecurity jobs to 2033. The demand for cybersecurity analysts is growing ever stronger! What do cybersecurity analysts do? Cybersecurity analysts protect an organization's digital assets by identifying, analyzing, and mitigating security threats, ensuring data and systems remain secure against cyberattacks. This IBM Cybersecurity Professional Certificate has been designed by industry experts to provide you with the job-ready cybersecurity and digital forensic skills you need to jumpstart your cybersecurity career. No prior cybersecurity experience is required. To begin, you’ll build your foundational knowledge of cybersecurity essentials, tools, and technologies, operating systems, networking fundamentals, and the basics of cyberattacks. You’ll explore database fundamentals and vulnerabilities and learn about the steps a cybersecurity analyst would take to prevent, manage, and mitigate cybersecurity attacks. Plus, you’ll learn about cybersecurity architecture, compliance frameworks, standards, and regulations. You’ll also explore gen AI for cybersecurity and build valuable skills and hands-on experience in penetration testing, incident response, forensics, and threat intelligence. You’ll work on real-world projects developing cybersecurity plans and compliance frameworks, giving you great practical work to talk about in interviews. Plus, you’ll use real-world case studies that demonstrate that you can: Apply incident response methodologies and forensics practices. Use industry-specific security tools. Apply cybersecurity industry standards and best practices that mitigate risks, enhance security, and ensure compliance through audit processes. Use gen AI tools to boost cybersecurity effectiveness and productivity. Investigate a real-world security breach by identifying the attack, vulnerabilities, costs, and prevention. Take advantage of the insights provided by seasoned industry experts to guide your job search, resume creation, and Interviewing to help you land your first cybersecurity role. This program will also prepare you to take and earn a 30% discount on the CompTIA Security+ and CySA+ certification exams. After successfully completing this program, you’ll have an industry-recognized IBM Professional Certificate that you can add to your resume and portfolio. If you’re looking to kickstart your cybersecurity career as a sought-after analyst, ENROLL TODAY, and look forward to being job-ready in less than 6 months!

Understand Data Security What you'll learn: Deep knowledge of organization’s IT Systems.IT Security threat analysis and management.IT Security maintenance, training and supportIT security fixes, tools, countermeasures, testing plus upgrade paths This course is designed for anyone who wants to understand the analysis of data security. More than ever, information security analysts are needed to rescue companies when they have been hacked or breached, as well as to put in place controls and counter-measures to avert cyber attacks.Gain the skills required to detect and mitigate information and cyber security threats and vulnerabilities. The security analyst role is an entry point to many other security specialties and is highly in-demand in today’s evolving threat environment.In the fast moving world of cyber security, with new threats evolving daily, up to date knowledge and skills that involves protecting systems and networks of companies and other assets containing mission-critical data as well as designing and developing new security solutions routinely to stay ahead of the hackers and cyber attackers.Learn the essential prerequisites for your career success! ENROLL TODAY!

Step into the fast-growing world of data analytics with the Data Analyst program. This comprehensive, beginner-friendly series of courses from Microsoft will equip you with essential skills to collect, clean, model, and visualize data using Power BI—a leading business intelligence platform. Through hands-on projects and interactive lessons, you’ll learn how to turn raw data into actionable insights, build compelling dashboards, and effectively communicate trends and findings to stakeholders. Whether you're aiming to launch a new career in data analysis or upskill in your current role, this program offers the practical knowledge and tools to succeed in today’s data-driven job market.

Hands-On Data Analysis with SQL What you'll learn: Analyse and manipulate dataCreate Database and TablesAnalyse data using aggregate functionsAnalyse data using analytic functionsAnalyse data using ranking functionsAnalyse data using various operatorsAnalyse data using sub queriesAnalyse data using views A SQL data analyst is a data scientist that uses the SQL language to interpret data. This analysis is used to improve business information and provide new insights to managers.SQL data analysts design scripts and programs to perform specific database functions and to analyse and maintain data. Some SQL Data Analyst Duties and ResponsibilitiesDuties vary for SQL data analysts based on the specific needs of the hiring company and the amount of data being analysed. However, these core tasks are commonly associated with this job:Integrate DataSQL data analysts integrate data from multiple data sets into o:ne database system to streamline information.Create Database FunctionsSQL data analysts program specific database functions to create database triggers and design data tables.Design ReportsAfter reviewing and analysing data, SQL data analysts design reports to show statistics and other information about specific database topics.Clean DatabaseSQL data analysts remove dead data files and other irrelevant information to free up database space.Write ScriptsSQL data analysts write scripts to analyse data and perform specific data queries.Resolve Data IssuesSQL data analysts isolate and resolve issues with data and databases.Add DataSQL data analysts collect data from multiple sources and add it to the database. We will be performing data analysis using SQL on relational databases like: MySQL, PostgreSQL and MS SQL Server.A relational database is a type of database. It uses a structure that allows us to identify and access data in relation to another piece of data in the database. Often, data in a relational database is organized into tables. A relational database includes tables containing rows and columns. For example, a typical business order entry database would include a table that describes a customer with columns for name, address, phone number and so forth.SQL or Structured Query Language is a critical tool for data professionals. It is undoubtedly the most important language for getting a job in the field of data analysis or data sciences. Millions of data points are being generated every minute. And raw data does not have any story to tell. After all this data gets stored in databases and professionals use SQL to extract this data for further analysis.

Complete Data Analyst Training: Python, NumPy, Pandas, Data Collection, Preprocessing, Data Types, Data Visualization What you'll learn: The course provides the complete preparation you need to become a data analystFill up your resume with in-demand data skills: Python programming, NumPy, pandas, data preparation - data collection, data cleaning, data preprocessing, data visualization; data analysis, data analyticsAcquire a big picture understanding of the data analyst roleLearn beginner and advanced PythonStudy mathematics for PythonWe will teach you NumPy and pandas, basics and advancedBe able to work with text filesUnderstand different data types and their memory usageLearn how to obtain interesting, real-time information from an API with a simple scriptClean data with pandas Series and DataFramesComplete a data cleaning exercise on absenteeism rateExpand your knowledge of NumPy – statistics and preprocessingGo through a complete loan data case study and apply your NumPy skillsMaster data visualizationLearn how to create pie, bar, line, area, histogram, scatter, regression, and combo chartsEngage with coding exercises that will prepare you for the jobPractice with real-world dataSolve a final capstone project The problemMost data analyst, data science, and coding courses miss a critical practical step. They don’t teach you how to work with raw data, how to clean, and preprocess it. This creates a sizeable gap between the skills you need on the job and the abilities you have acquired in training. Truth be told, real-world data is messy, so you need to know how to overcome this obstacle to become an independent data professional.The bootcamps we have seen online and even live classes neglect this aspect and show you how to work with ‘clean’ data. But this isn’t doing you a favour. In reality, it will set you back both when you are applying for jobs, and when you’re on the job.The solutionOur goal is to provide you with complete preparation. And this course will turn you into a job-ready data analyst. To take you there, we will cover the following fundamental topics extensively.Theory about the field of data analyticsBasic PythonAdvanced PythonNumPyPandasWorking with text filesData collectionData cleaningData preprocessingData visualizationFinal practical exampleEach of these subjects builds on the previous ones. And this is precisely what makes our curriculum so valuable. Everything is shown in the right order and we guarantee that you are not going to get lost along the way, as we have provided all necessary steps in video (not a single one skipped). In other words, we are not going to teach you how to analyse data before you know how to gather and clean it.So, to prepare you for the entry-level job that leads to a data science position - data analyst - we created The Data Analyst Course.This is a rather unique training program because it teaches the fundamentals you need on the job. A frequently neglected aspect of vital importance.Moreover, our focus is to teach topics that flow smoothly and complement each other. The course provides complete preparation for someone who wants to become a data analyst at a fraction of the cost of traditional programs (not to mention the amount of time you will save). We believe that this resource will significantly boost your chances of landing a job, as it will prepare you for practical tasks and concepts that are frequently included in interviews.The topics we will cover1. Theory about the field of data analytics2. Basic Python3. Advanced Python4. NumPy5. Pandas6. Working with text files7. Data collection8. Data cleaning9. Data preprocessing10. Data visualization11. Final practical example 1. Theory about the field of data analyticsHere we will focus on the big picture. But don’t imagine long boring pages with terms you’ll have to check up in a dictionary every minute. Instead, this is where we want to define who a data analyst is, what they do, and how they create value for an organization.Why learn it?You need a general understanding to appreciate how every part of the course fits in with the rest of the content. As they say, if you know where you are going, chances are that you will eventually get there. And since data analyst and other data jobs are relatively new and constantly evolving, we want to provide you with a good grasp of the data analyst role specifically. Then, in the following chapters, we will teach you the actual tools you need to become a data analyst.2. Basic PythonThis course is centred around Python. So, we’ll start from the very basics. Don’t be afraid if you do not have prior programming experience.Why learn it?You need to learn a programming language to take full advantage of the data-rich world we live in. Unless you are equipped with such a skill, you will always be dependent on other people’s ability to extract and manipulate data, and you want to be independent while doing analysis, right? Also, you don’t necessarily need to learn many programming languages at once. It is enough to be very skilled at just one, and we’ve naturally chosen Python which has established itself as the number one language for data analysis and data science (thanks to its rich libraries and versatility).3. Advanced PythonWe will introduce advanced Python topics such as working with text data and using tools such as list comprehensions and anonymous functions.Why learn it?These lessons will turn you into a proficient Python user who is independent on the job. You will be able to use Python’s core strengths to your advantage. So, here it is not just about the topics, it is also about the depth in which we explore the most relevant Python tools.4. NumPyNumPy is Python’s fundamental package for scientific computing. It has established itself as the go-to tool when you need to compute mathematical and statical operations.Why learn it?A large portion of a data analyst’s work is dedicated to preprocessing datasets. Unquestionably, this involves tons of mathematical and statistical techniques that NumPy is renowned for. In addition, the package introduces multi-dimensional array structures and provides a plethora of built-in functions and methods to use while working with them. In other words, NumPy can be described as a computationally stable state-of-the-art Python instrument that provides flexibility and can take your analysis to the next level.5. PandasThe pandas library is one of the most popular Python tools that facilitate data manipulation and analysis. It is very valuable because you can use it to manipulate all sorts of information - numerical tables and time series data, as well as text.Why learn it?Pandas is the other main tool an analyst needs to clean and preprocess the data they are working with. Its data manipulation features are second to none in Python because of the diversity and richness it provides in terms of methods and functions. The combined ability to work with both NumPy and pandas is extremely powerful as the two libraries complement each other. You need to be capable to operate with both to produce a complete and consistent analysis independently.6. Working with text filesExchanging information with text files is practically how we exchange information today. In this part of the course, we will use the Python, pandas, and NumPy tools learned earlier to give you the essentials you need when importing or saving data.Why learn it?In many courses, you are just given a dataset to practice your analytical and programming skills. However, we don’t want to close our eyes to reality, where converting a raw dataset from an external file into a workable Python format can be a massive challenge.7. Data collectionIn the real world, you don’t always have the data readily available for you. In this part of the course, you will learn how to retrieve data from an API.Why learn it?You need to know how to source your data, right? To be a well-rounded analyst you must be able to collect data from outside sources. This is rarely a one-click process. This section aims at providing you with all the necessary tools to do that on your own.8. Data cleaningThe next logical step is to clean your data. This is where you will apply the pandas skills acquired earlier in practice. All lessons throughout the course have a real-world perspective.Why learn it?A large part of a data analyst’s job in the real world involves cleaning data and preparing it for the actual analysis. You can’t expect that you’ll deal with flawless data sources, right? So, it will be up to you to overcome this stage and clean your data.9. Data preprocessingEven when your dataset is clean and in an understandable shape, it isn’t quite ready to be processed for visualizations and analysis just yet. There is a crucial step in between, and that’s data preprocessing.Why learn it?Data preprocessing is where a data analyst can demonstrate how good or great they are at their job. This stage of the work requires the ability to choose the right statistical tool that will improve the quality of your dataset and the knowledge to implement it with advanced pandas and NumPy techniques. Only when you’ve completed this step can you say that your dataset is preprocessed and ready for the next part, which is data visualization.10. Data visualizationData visualization is the face of data. Many people look at the data and see nothing. The reason for that is that they are not creating good visualizations. Or even worse – they are creating nice graphs but cannot interpret them accurately.Why learn it?This part of the course will teach you how to use your data to produce meaningful insights. At the end of the day, data charts are what conveys the most information in the shortest amount of time. And nothing speaks better than a well crafted and meaningful data visualization.11. Practical exampleThe course contains plenty of exercises and practical cases. In the end, we have included a comprehensive practical example that will show you how everything you have learned along the way comes nicely together. This is where you will be able to appreciate how far you have come in your journey to becoming a data analyst and starting your data career.What you getA program worth $1,250Active Q&A supportAll the knowledge to become a data analystA community of aspiring data analystsA certificate of completionAccess to frequent future updatesReal-world trainingGet ready to become a data analyst from scratchWhy wait? Every day is a missed opportunity.Click the “Buy Now” button and become a part of our data analyst program today.

Learn how to work with collections in the Python programming language in this hands-on, interactive course with coding challenges in CoderPad.

Understanding how to use collections properly is a vital part in writing effective C# applications. In this course, Advanced C# Collections, you'll learn how to use the full range of Microsoft collections and understand how to ensure collection elements are unique using sets, along with customizing dictionary key lookup and sorting of elements. First, you’ll develop an understanding of the principles of collection scalability, which is important to ensure performance stays acceptable when dealing with very large collections. Then, you'll discover how to make your code around collections more robust by using collection interfaces to decouple code, and read-only and immutable collections to protect your data. Finally, you'll delve into using collections in a multi-threaded scenario using the concurrent collections. By the end of this course, you'll have the knowledge needed to effectively choose the most appropriate collection for any reasonable situation, and leverage that collection following good practices.

Dive into a comprehensive tutorial series on the Java Collections Framework, exploring its various components and their practical applications. Learn how to effectively use ArrayList, LinkedList, HashMap, SortedMap, Set, Queue, and Iterator interfaces. Master the art of implementing custom objects in collections, sorting with Comparators, and understanding natural ordering with the Comparable interface. Discover techniques for deciding which collection type best suits your needs and tackle complex data structures. Gain hands-on experience by implementing the Iterable interface and downloading web pages, equipping you with essential skills for efficient data management in Java programming.

Learn about and gain hands-on experience with the Java Collections framework.