A course is a unit of education. Typically, students take several courses while pursuing a degree. However, some courses can be taken by themselves to give certification or bridge the gap between degrees. This learning unit usually lasts a few weeks to a year.
What are courses in big data? They are courses designed to teach students how to analyze and understand large data sets. Participants may learn how to reveal trends, patterns and associations after analyzing big data. Introductory courses may take a more basic look at this subject, while advanced courses can delve into specifics of big data. Scholars studying computer science, engineering, artificial intelligence or management information systems may have to take some courses in this subject. The layout of a course can vary from school to school, but could include lectures, discussions and exams.
Studying big data gives participants a great foundation for critical-thinking, analyzation and organization skills. These sought-after abilities may make graduates more competitive applicants. In some cases, these well-honed abilities may lead to higher starting salaries.
The cost of a course can be different at each university. Sometimes the cost even depends on the course itself, its duration and the subject matter. Applicants who want to know the tuition may need to contact the school they are hoping to attend.
Graduates who spent a lot of time studying big data may have a variety of career opportunities, including information systems engineer, data scientist, network system architect, big data developer, software engineer, technology researcher and data analyst. Some students may have a completely different career outlook based on their degree and previous work history.
No matter where you live, you may be able to enroll in these types of courses. Many universities have online and on-campus programs. To learn more, search for your program below and contact directly the admission office of the school of your choice by filling in the lead form.
Data Science, Business Analytics, Data Analysis, Data Mining, Tableau, Statistics, Modeling, Regression, SQL, SSIS. [+]
Data Science, Business Analytics, Data Analysis, Data Mining, Tableau, Statistics, Modeling, Regression, SQL, SSIS.What will I learn? Perform all the steps of a complex Data Science project correctly Create Visualizations in Tableau Doing Data Mining in Tableau Understand how to apply the chi-square test Apply the Ordinary Least Squares method to make Linear Regressions Evaluate all types of models thanks to the R-Squared Evaluate all types of models thanks to Adjusted R-Squared Create a Simple Linear Regression Model Create a Multiple Linear Regression Model Create Dummy Variables Interpreting the coefficients of Multiple Linear Regression Read Outputs of Linear Regression Models Use Backward Elimination, Forward Selection, and Bidirectional Elimination methods to create statistical models Create a Logistic Regression Model Integrate the intuition of Logistic Regression Analyze the False Positives & False Negatives and understand the difference Read a Confusion Matrix Create a Robust Model of Geo-Demographic Segmentation Transform independent variables for modeling Derive independent variables for modeling Check the presence of multicollinearity using VIF (Variance Inflation Factor) Have an intuition of multicollinearity Use the Cumulative Accuracy Profile (CAP) to evaluate models Build the CAP curve in Excel Use the Training Set and the Test Set to build robust models Draw insights from your CAP curve Understanding the Odds Ratio Getting business insights from the coefficients of a Logistics Regression Understand what pattern deterioration looks like Apply three levels of model maintenance to prevent model deterioration Install and use SQL Server Install and use Microsoft Visual Studio Shell Clean up data... [-]
A self-paced course that helps you to understand the various Statistical Techniques from the very basics and how each technique is employed on a real-world data set to analyze and conclude insights. [+]
A self-paced course that helps you to understand the various Statistical Techniques from the very basics and how each technique is employed on a real-world data set to analyze and conclude insights.
The self-paced Statistics Essentials for Analytics Course is designed for the learners to understand and implement various statistical techniques. These techniques are explained using dedicated examples. The use case is taken up at the end of each module and insights are gathered, thus at the end of the course, we have a Project which is consistently worked upon throughout the course.Course Objectives
After the completion of this course at Edureka, you should be able to:
Learn various statistical techniques like Sampling Methods, Conditional Probability, Bayesian Theorem, etc.... [-]
Edureka's AWS Training will introduce the participants to explore and master AWS concepts and services offered by AWS. [+]
Edureka's AWS Training will introduce the participants to explore and master AWS concepts and services offered by AWS.
Participants will be offered guidance and will share a lot of demos on each topic with real-time examples of high availability, load balancing, data redundancy and day to day operations in managing cloud services.
Participants will also get to implement one project towards the end of the course.Course Highlights 12 Instructor-led Interactive online classes of 3 hours each. Assignments at end of each class to reinforce the concepts Project work at end of the course to give a complete hands-on experience to participants in the implementation of various AWS services. Lifetime access to Class recordings and presentations on edureka! Learning Management System (LMS). Lifetime access to installation guides, sample codes and project documents edureka! Learning Management System (LMS). 24x7 online support team available to help participants with any technical queries they may have during the course. A skill certificate in AWS Development Training from edureka! Course Objectives ... [-]
Edureka's Real-Time Analytics with Apache Kafka course is designed to provide knowledge and skills to become a successful Kafka Big Data Developer. It will encompass the fundamental concepts like Kafka cluster, Kafka API to advance topics such as Kafka integration with Hadoop, Storm, Spark, Maven etc. [+]
Edureka's Real-Time Analytics with Apache Kafka course is designed to provide knowledge and skills to become a successful Kafka Big Data Developer. It will encompass the fundamental concepts like Kafka cluster, Kafka API to advance topics such as Kafka integration with Hadoop, Storm, Spark, Maven etc. The course also covers installation & configuration of Kafka and other components like Hadoop, Spark, Storm, Maven etc.Course Objectives
After the completion of Real-Time Analytics with Apache Kafka course at Edureka, you should be able to:Understand Kafka and its components Set up an end to end Kafka cluster along with Hadoop and Yarn cluster Integrating Kafka with real-time streaming systems like Spark & Storm Describe the basic and advanced features involved in designing and developing a high throughput messaging system Use Kafka to produce and consume messages from various sources including real-time streaming sources like Twitter Understanding the insights of Kafka API Work on a real-life Project, implementing Twitter streaming with Kafka, Hadoop & Storm Who should go for this course? ... [-]
Edureka’s Big Data Hadoop online training is designed to help you become a top Hadoop developer. [+]
Edureka’s Big Data Hadoop online training is designed to help you become a top Hadoop developer.
During this course, our expert instructors will help you:Master the concepts of HDFS and MapReduce framework Understand Hadoop 2.x Architecture Setup Hadoop Cluster and write Complex MapReduce programs Learn data loading techniques using Sqoop and Flume Perform data analytics using Pig, Hive, and YARN Implement HBase and MapReduce integration Implement Advanced Usage and Indexing Schedule jobs using Oozie Implement best practices for Hadoop development Work on a real-life Project on Big Data Analytics Understand Spark and its Ecosystem Learn how to work in RDD in Spark Who should go for this Hadoop Course? ... [-]
This Spark training will enable learners to understand how Spark executes in-memory data processing and runs much faster than Hadoop MapReduce. Learners will master Scala programming and will get trained on different APIs which Spark offers such as Spark Streaming, Spark SQL, Spark RDD, Spark MLlib and Spark GraphX. [+]
This Spark training will enable learners to understand how Spark executes in-memory data processing and runs much faster than Hadoop MapReduce. Learners will master Scala programming and will get trained on different APIs which Spark offers such as Spark Streaming, Spark SQL, Spark RDD, Spark MLlib and Spark GraphX. This Edureka course is an integral part of Big Data developer's learning path.Course Objectives
After completing the Apache Spark training, you will be able to:Understand Scala and its implementation Master the concepts of Traits and OOPS in Scala programming Install Spark and implement Spark operations on Spark Shell Understand the role of Spark RDD Implement Spark applications on YARN (Hadoop) Learn Spark Streaming API Implement machine learning algorithms in Spark MLlib API Analyze Hive and Spark SQL architecture Understand Spark GraphX API and implement graph algorithms Implement Broadcast variable and Accumulators for performance tuning Project Who should go for this Course? ... [-]
The management of Big Data today is key and the way to enhance its value is through business intelligence. [+]
Deal with large numbers and key information in this Big Data Course Are interested in understanding and protecting big data? Do you want to utilize any data that have been presented to you to its full extent? This Innovative Course will solve all of your problems! [+]
Understanding Big Data is CPD certified and CiQ Gold accredited. This makes it perfect for anyone trying to learn potential professional skills.
As there is no experience and qualification required for this course, it is available for all students from any academic backgrounds.... [-]
Understand how blockchain works, where the technology has come from and why it will empower energy customers like never before. [+]
This online course studies the incredibly disruptive potential of blockchain technology in the energy sector. You will look at how blockchain works and start to understand the background, value proposition and geopolitical context that brought it to the center of everyone’s attention.
The course will offer a clear overview of how and why blockchain will take over the energy sector, optimizing old processes and empowering the customers like never before. This will create a new paradigm, where users can buy and sell energy from and to each other.What topics will you cover? What is blockchain? Digital currencies The evolution and future of blockchain Smart contracts and decentralized applications Blockchain in the energy sector: the new paradigm P2P energy trading Blockchain applied to EV charging What will you achieve? ... [-]
From sources such as satellites, sensors, and social media, how can environmental data analytics benefit business and research?[+]
From weather fluctuations to the spread of an invasive species, what problems can scientists address by analyzing these vast data collections? What are the potential benefits of business, research, and our daily lives?
Find out about possible career paths and gain insights from industry experts and research scientists working on a range of current projects. From urban planning to monitoring wildlife, explore the complexities of managing and analyzing big data to seek innovative solutions.What topics will you cover?
Week 1: Introduction to big dataSources of big data, including satellites for looking at the surface of the Earth Environmental analytics and the link to big data Computing power, processing, and storage of environmental data ... [-]
Interested in increasing your knowledge of the Big Data landscape? This course is for those new to data science and interested in understanding why the Big Data Era has come to be. [+]
Interested in increasing your knowledge of the Big Data landscape? This course is for those new to data science and interested in understanding why the Big Data Era has come to be. It is for those who want to become conversant with the terminology and the core concepts behind big data problems, applications, and systems. It is for those who want to start thinking about how Big Data might be useful in their business or career. It provides an introduction to one of the most common frameworks, Hadoop, that has made big data analysis easier and more accessible -- increasing the potential for data to transform our world! At the end of this course, you will be able to: * Describe the Big Data landscape including examples of real-world big data problems including the three key sources of Big Data: people, organizations, and sensors. * Explain the V’s of Big Data (volume, velocity, variety, veracity, valence, and value) and why each impact data collection, monitoring, storage, analysis, and reporting. * Get value out of Big Data by using a 5-step process to structure your analysis. * Identify what are and what are not big data problems and be able to recast big data problems as data science questions. * Provide an explanation of the architectural components and programming models used for scalable big data analysis. * Summarize the features and value of core Hadoop stack components including the YARN resource and job management system, the HDFS file system and the... [-]
This course will introduce the core data structures of the Python programming language. We will move past the basics of procedural programming and explore how we can use the Python built-in data structures such as lists, dictionaries, and tuples to perform increasingly complex data analysis. [+]
This course will introduce the core data structures of the Python programming language. We will move past the basics of procedural programming and explore how we can use the Python built-in data structures such as lists, dictionaries, and tuples to perform increasingly complex data analysis. This course will cover Chapters 6-10 of the textbook “Python for Everybody”. This course covers Python 3. Chapter Six: Strings In this class, we pick up where we left off in the previous class, starting in Chapter 6 of the textbook and covering Strings and moving into data structures. The second week of this class is dedicated to getting Python installed if you want to actually run the applications on your desktop or laptop. If you choose not to install Python, you can just skip to the third week and get a head start. Unit: Installing and Using Python In this module you will set things up so you can write Python programs. We do not require the installation of Python for this class. You can write and test Python programs in the browser using the "Python Code Playground" in this lesson. Please read the "Using Python in this Class" material for details. Chapter Seven: Files Up to now, we have been working with data that is read from the user or data in constants. But real programs process much larger amounts of data by reading and writing files on the secondary storage on your computer. In this chapter, we start to write our first... [-]
Learn how predictive analytics tools can help you gain insights from big data. Collecting big data is just the first step; once you have it, how do you make sense of it? This free online course will show you how predictive analytics tools can help you gain information, knowledge and insights from big data. [+]
Learn how predictive analytics tools can help you gain insights from big data. Collecting big data is just the first step; once you have it, how do you make sense of it? This free online course will show you how predictive analytics tools can help you gain information, knowledge and insights from big data.
Over the next four weeks, experience the power of HPE’s Vertica Analytics platform as an applied tool. Using Vertica Analytics and a case study approach, apply built-in predictive analytics functions and algorithms – linear regression, logistics regression and k-means clustering – to derive insight from your data, helping to create opportunities for your organisation.What topics will you cover? Modelling, estimation and prediction Estimation and prediction using linear regression Estimation and prediction using logistic regression Estimation and prediction using k-means clustering What will you achieve? Describe big data analytics Identify solutions to big data problems Evaluate predictive data analysis models Assess the suitability of predictive models Model data using various predictive models Who is the course for? ... [-]
Learn how to apply selected mathematical modeling methods to analyze big data in this free online course.[+]
Have you ever wondered how mathematics can be used to solve big data problems? This course will show you how. Mathematics is everywhere, and with the rise of big data it becomes a useful tool when extracting information and analyzing large datasets.Learn how maths underpins big data analysis
We will begin by explaining how maths underpins many of the tools that are used to manage and analyze big data. We will show you how very different applied problems can have common mathematical aims, and therefore can be addressed using similar mathematical tools. We will then proceed to introduce three such tools, based on a linear algebra framework. These tools and the problems that they address are:... [-]
This free online course equips you for working with these solutions by introducing you to selected statistical and machine learning techniques used for analysing large datasets and extracting information. [+]
Many people have big data but only some people know what to do with it. Why? Well, the big problem is that the data is big—the size, complexity and diversity of datasets increase every day. This means we need new solutions for analysing data.
This course equips you for working with these solutions by introducing you to selected statistical and machine learning techniques used for analysing large datasets and extracting information. We also expose you to three software packages so you can develop your coding skills by completing practical exercises.What will you achieve? Identify big data application areas Explore big data frameworks Model and analyse data by applying selected techniques Demonstrate an integrated approach to big data Develop an awareness of how to participate effectively in a team working with big data experts Who is the course for? ... [-]