Big Data Analytics Using Spark course will be helping the students in analyzing the big data sets using the mechanisms of MapReduce, Jupyter notebooks and spark technologies. The ten week Big Data Analytics Using Spark course is the fourth in a series of the “Micro Master” programme by edX. Students will be learning about the difficulties that arise in the parallel computation system. Students will be taught both unsupervised and supervised methods of performing machine learning functions on data sets in the Big Data Analytics Using Spark programme.
The candidates will also be learning about the machine learning library (MLLiB) mechanism in detail and in a comprehensive manner. Big Data Analytics Using Spark by edX will be conducted by experts of computer science. During the course, students will be provided with assignments and video lectures for comprehensive learning. The students will gain and build experience in the machine learning environment using the various tools available. The Big Data Analytics Using Spark certification will be guiding the candidates advanced level.
Python for Data Science certification will be guiding the students to learn about the topics of- python tools and open source including the concerts of git, pandas, matplotlib, and others. This program is a part of the series program under “Data Science MicroMasters” Here the students will be specifically learning to use- git, matplotlib, NumPy, pandas, Jupyter notebooks, and pythons. The tools will be learned by the students so that they can use them in solving complex algorithms and data science problems easily. Once the candidate completes Python for Data Science training they will be learning to find solutions within large datasets by the usage of python tools for importing data, exploring the data, analyzing the data, learning from it, and lastly visualizing the given data. The course will be helping the students to formulate easily shareable reports. Python for Data Science certification course will make the students a part of their world-wide community of data scientists who are working in the field of “data science”.
Probability and Statistics in Data Science using Python by edX will be helping students to learn about the probabilistic and statistical approaches that pertain to data science using the tool python. The course is a part of the “Data Science MicroMasters program” and will be dealing with mathematical theories in detail. Probability and Statistics in Data Science using Python certification course will be covering concepts like- dependence, PCA, regression, correlation, entropy, variables, and MDL. The course will also be making the students' foundation in statistics and probability more strong. They will be learning to apply the theory of Jupiter in their course. Probability and Statistics in Data Science using Python training will further help the candidates to enhance their skills on the subject of “Data Analysis and statistics''. Throughout the course, they will be guided by the experts and allowed to pursue the cases at their own time. By dedicating 10-12 hours every week the participants can very essay complete their course.
In Machine Learning Fundamentals by edX using real-world problem statements students will learn to figure out the features of topics in corpus, classify the given images, group people according to various profiles depending on their personality and also capture the semantic structure of words available. With the information learnt in the Machine Learning Fundamentals training the students will learn to analyze different types of data and also they will be able to build predictive and descriptive models.
The Machine Learning Fundamentals certification has the following included in its curriculum- conditioned probability, linear models, random forests, reduction, bagging and many other topics that deals with the domain of machine learning. The examples that will be provided in the Machine Learning Fundamentals programme will be in the language of python and will use Jupyter notebooks. The candidates will also be practicing various types of algorithms using data science and its applied tools. The Machine Learning Fundamentals online course will provide students with both discriminative and generative models.
Java is an object-oriented programming language used worldwide for both the development of applications as well as websites. Java is the key ingredient behind Google Maps, Internet Routers and what not which makes knowledge about how Java works and its Data Structure a must when looking for opportunities in the Cyber World.
Advanced-Data Structure in Java by Coursera offers all that is required to master the computer language. This course is designed to teach candidates how to analyze and develop algorithms and study complex data structures, which in turn will help them resolve real-world problems of the field.
The contents of the course will be lined with videos, exercises, pre-course quizzes and multiple readings all of which have concepts being tied to the final event; making a route planning application. This course ensures to open a lot of doors for the candidates’ careers and thus, is exactly what the opportunists need.
Mathematics and computer science have entrenched relationships as math forms an important background in the field of computer science and computer science also has a lot of applicability math. As math is abstract, it helps in learning all the other programming languages.
Mathematical thinking is critical in all the crucial areas of computer science like bioinformatics, data science, machine learning, algorithms, computer graphics, etc. In this course, Mathematical Thinking in Computer Science by Coursera, you will be taught the most significant and paramount tools used in discrete mathematics like recursion, logic, examples, induction, optimality.
In this course, we apply a practical approach: try-this-before-we-explain-everything. Due to such a practice, you will be solving a plethora of interactive and mobile-friendly puzzles. These are carefully designed to give you a chance and opportunity to invent many of the crucial ideas and concepts by yourself.
The certificate programme on Algorithmic Toolbox by Coursera is an intermediate level course that will cover basic ideas for problems arising in the practical applications of computational problems, algorithmic techniques, greedy algorithms, Sorting and searching, Dynamic programming, and divide and conquer. The course offers a lot of theory and understanding of the burning questions in programming. It helps candidates develop an understanding of how to solve the most commonly arising problem and how they can break it into pieces and solve them recursively, Further how they can sort data and how it can help in searching, when it is ok to proceed greedily and how genomic studies use dynamic programming.
The candidates will be given enough practice in solving the computational problems, implement the solutions efficiently, design new algorithms, and ensure the easy and smooth running of the programs. This course forms a part of specialisation in Data structures and algorithm specialisation, for candidates willing to learn more about the topic and further continue to take the specialization too.
For candidates and professionals willing to learn about the new challenges in programming and better equip themselves for staying relevant in the industry this course on Algorithmic Toolbox will offer a much better understanding of the subject and help them take the next step in their career.
The soul of programming is said to be data structures and algorithms. Data structures aim to hold the data while the algorithms aim to solve the problem using the data. Data structures are the key to the computer algorithms which help the programmers to manage the data efficiently. The perfect selection of data helps to enhance the efficiency of the computer programme.
Computer science in this era is all about sorting and computing from given data. So, it is necessary to have a powerful knowledge about data structure, it will help one deal with different ways of arranging, storing and processing the data. It helps in utilizing maximum space and also decreases the complexity in deletion of data, addition as well as insertion of data.
To be a successful data processor one should acquire all the skills appropriately and should master their skills that will make them stand out of the crowd. Each and every company searches for an employee who has the ability to overcome all the problems and keep up to the needs and the requirements of the company. This is the platform where they can shape their skills for the same.
Here most standard combinatorial settings are addressed in the Combinatorics and Probability course that can help answer questions of this nature. In particular, this course focuses on improving the ability to differentiate these settings from algorithmic problems in real life. This will assist candidates to apply new knowledge in practice. Besides that recursive counting technique that is necessary for algorithmic implementations is also addressed here.
The Combinatorics and Probability course is part of Introduction to Discrete Mathematics for Computer Science Specialisation. There are total of five courses in it and Combinatorics and Probability is the second one. The language of computer science is known as Discrete Mathematics. In many areas including data science, software engineering and machine learning, one needs to work fluently. Via a fun approach, candidates are introduced to this language. Firstly they will solve several interactive puzzles that are specially designed especially for this online specialisation. Then actual solving methods and significant ideas will be introduced to them. It is sure that candidates will gain a deeper understanding in this way and will appreciate the beauty of the underlying concepts better. Programming examples, projects and problems are integrated into the specialisation in order to get the knowledge closer to IT applications.
Algorithms on graphs is a much-used concept in navigation services and has thus found supreme applicability in the current scenario. Whether it be road networks, social networks or computer networks, this domain offers a lot of scope for learning, especially for engineers. To think of the fastest time to commute, people often resort to a connected set of computers which are strewn through an efficient algorithm or a dense network. This also helps in detecting communities and leaders on Facebook.
Algorithms on Graphs course is one another valuable offering by Coursera which delves deep into the insights of graphs and its varied properties. Candidates will learn traversing graphs and orderly traversing of the same to perform meaningful tasks. Discussion on shortest paths algorithms from basic level used to open doors till faster ones used in Google Maps will be conducted. Pursuing this course will also help candidates in pursuing Fast Shortest Routes industrial capstone projects if they wish to.
This course concludes with minimum spanning trees utilised in road planning and telephone and computer networks.