Functional Magnetic Resonance Imaging (fMRI) is the most widely used technique for investigating the living, functioning human brain as people perform tasks and experience mental states. It is a convergence point for multidisciplinary work from many disciplines. Psychologists, statisticians, physicists, computer scientists, neuroscientists, medical researchers, behavioral scientists, engineers, public health researchers, biologists, and others are coming together to advance our understanding of the human mind and brain. This course covers the design, acquisition, and analysis of Functional Magnetic Resonance Imaging (fMRI) data, including psychological inference, MR Physics, K Space, experimental design, pre-processing of fMRI data, as well as Generalized Linear Models (GLM’s). A book related to the class can be found here: https://leanpub.com/principlesoffmri.

This course prepares you to deal with advanced clinical data science topics and techniques including temporal and research quality analysis.

Ever felt that you’re missing out on the data race? Don’t know the difference between a variable and a vegetable? Then our Analytics for Beginners course is just for you! What is this course about? The motive of the course is to introduce absolute beginners, or novices to the fundamentals of analytics by using examples from daily life. Using a combination of real-life stories, interviews with analytics experts and journalistic articles presented in a breezy narrative style, Analytics for Beginners will ensure that you will never look at numbers and data in the same way again. How will you do that? By understanding how companies like Netflix, Amazon, Facebook or even great individuals like Archimedes or Florence Nightingale used analytics in their chosen spheres of work, the course will convey the relevance and importance of using analysis in everyday life. In terms of prior requirements, the viewer will only need a working knowledge of Excel, with logical and reasoning skills. A basic understanding of regression is also covered in the course. Mathematics skills are not required! Who is this course for? Are you a working professional or a stay-at-home mom? A young go-getter or an old has-been? A small business owner or a multi million dollar corporate hi-flier? The point is that none of it matters - because whoever you are and whatever you do, analytics can help you do it better! Why should I do this course? We’re not going to pretend that you can run logistic regressions and crack warehousing solutions at the end of this course. But what this course can do is open your eyes to the wonderful world of analytics opportunities and introduce you to some of its career possibilities. Do you use a case study? Keeping with the light-hearted tone of the course, the over-arching case study covered is based on the game of cricket. You can read up more about how the game is played, on its Wikipedia entry. The case study seeks to determine who is the best cricketer amongst Indian cricketing greats - Sachin Tendulkar, Rahul Dravid and Saurav Ganguly by using regression analysis. This ensures the overall flavor of the course remains light and fun, even if the analytics does require a little data work.

The Problem Data scientist is one of the best suited professions to thrive this century. It is digital, programming-oriented, and analytical. Therefore, it comes as no surprise that the demand for data scientists has been surging in the job marketplace. However, supply has been very limited. It is difficult to acquire the skills necessary to be hired as a data scientist. And how can you do that? Universities have been slow at creating specialized data science programs. (not to mention that the ones that exist are very expensive and time consuming) Most online courses focus on a specific topic and it is difficult to understand how the skill they teach fit in the complete picture The Solution Data science is a multidisciplinary field. It encompasses a wide range of topics. Understanding of the data science field and the type of analysis carried out Mathematics Statistics Python Applying advanced statistical techniques in Python Data Visualization Machine Learning Deep Learning Each of these topics builds on the previous ones. And you risk getting lost along the way if you don’t acquire these skills in the right order. For example, one would struggle in the application of Machine Learning techniques before understanding the underlying Mathematics. Or, it can be overwhelming to study regression analysis in Python before knowing what a regression is. So, in an effort to create the most effective, time-efficient, and structured data science training available online, we created The Data Science Course 2021. We believe this is the first training program that solves the biggest challenge to entering the data science field – having all the necessary resources in one place. Moreover, our focus is to teach topics that flow smoothly and complement each other. The course teaches you everything you need to know to become a data scientist at a fraction of the cost of traditional programs (not to mention the amount of time you will save). The Skills 1. Intro to Data and Data Science Big data, business intelligence, business analytics, machine learning and artificial intelligence. We know these buzzwords belong to the field of data science but what do they all mean? Why learn it? As a candidate data scientist, you must understand the ins and outs of each of these areas and recognise the appropriate approach to solving a problem. This ‘Intro to data and data science’ will give you a comprehensive look at all these buzzwords and where they fit in the realm of data science. 2. Mathematics Learning the tools is the first step to doing data science. You must first see the big picture to then examine the parts in detail. We take a detailed look specifically at calculus and linear algebra as they are the subfields data science relies on. Why learn it? Calculus and linear algebra are essential for programming in data science. If you want to understand advanced machine learning algorithms, then you need these skills in your arsenal. 3. Statistics You need to think like a scientist before you can become a scientist. Statistics trains your mind to frame problems as hypotheses and gives you techniques to test these hypotheses, just like a scientist. Why learn it? This course doesn’t just give you the tools you need but teaches you how to use them. Statistics trains you to think like a scientist. 4. Python Python is a relatively new programming language and, unlike R, it is a general-purpose programming language. You can do anything with it! Web applications, computer games and data science are among many of its capabilities. That’s why, in a short space of time, it has managed to disrupt many disciplines. Extremely powerful libraries have been developed to enable data manipulation, transformation, and visualisation. Where Python really shines however, is when it deals with machine and deep learning. Why learn it? When it comes to developing, implementing, and deploying machine learning models through powerful frameworks such as scikit-learn, TensorFlow, etc, Python is a must have programming language. 5. Tableau Data scientists don’t just need to deal with data and solve data driven problems. They also need to convince company executives of the right decisions to make. These executives may not be well versed in data science, so the data scientist must but be able to present and visualise the data’s story in a way they will understand. That’s where Tableau comes in – and we will help you become an expert story teller using the leading visualisation software in business intelligence and data science. Why learn it? A data scientist relies on business intelligence tools like Tableau to communicate complex results to non-technical decision makers. 6. Advanced Statistics Regressions, clustering, and factor analysis are all disciplines that were invented before machine learning. However, now these statistical methods are all performed through machine learning to provide predictions with unparalleled accuracy. This section will look at these techniques in detail. Why learn it? Data science is all about predictive modelling and you can become an expert in these methods through this ‘advance statistics’ section. 7. Machine Learning The final part of the program and what every section has been leading up to is deep learning. Being able to employ machine and deep learning in their work is what often separates a data scientist from a data analyst. This section covers all common machine learning techniques and deep learning methods with TensorFlow. Why learn it? Machine learning is everywhere. Companies like Facebook, Google, and Amazon have been using machines that can learn on their own for years. Now is the time for you to control the machines. ***What you get*** A $1250 data science training program Active Q&A support All the knowledge to get hired as a data scientist A community of data science learners A certificate of completion Access to future updates Solve real-life business cases that will get you the job You will become a data scientist from scratch We are happy to offer an unconditional 30-day money back in full guarantee. No risk for you. The content of the course is excellent, and this is a no-brainer for us, as we are certain you will love it. Why wait? Every day is a missed opportunity. Click the “Buy Now” button and become a part of our data scientist program today.

PLEASE READ BEFORE ENROLLING: 1.) THERE IS AN UPDATED VERSION OF THIS COURSE: "PYTHON FOR DATA SCIENCE AND MACHINE LEARNING BOOTCAMP" 2.) IF YOU ARE A COMPLETE BEGINNER IN PYTHON-CHECK OUT MY OTHER COURSE "COMPLETE PYTHON MASTERCLASS JOURNEY"! CLICK ON MY PROFILE TO FIND IT. (PLEASE WATCH THE FIRST PROMO VIDEO ON THIS PAGE FOR MORE INFO) ********************************************************************************************************** This course will give you the resources to learn python and effectively use it analyze and visualize data! Start your career in Data Science! You'll get a full understanding of how to program with Python and how to use it in conjunction with scientific computing modules and libraries to analyze data. You will also get lifetime access to over 100 example python code notebooks, new and updated videos, as well as future additions of various data analysis projects that you can use for a portfolio to show future employers! By the end of this course you will: - Have an understanding of how to program in Python. - Know how to create and manipulate arrays using numpy and Python. - Know how to use pandas to create and analyze data sets. - Know how to use matplotlib and seaborn libraries to create beautiful data visualization. - Have an amazing portfolio of example python data analysis projects! - Have an understanding of Machine Learning and SciKit Learn! With 100+ lectures and over 20 hours of information and more than 100 example python code notebooks, you will be excellently prepared for a future in data science!

This is an introductory course designed to help business professionals and others learn predictive analytic skills that can be applied in a business setting. Since it is designed for business professionals it doesn't delve too deeply into the mathematics of the statistical models. We do the following case studies on Rapidminer software: B2B Churn of an office supply distributor, Market Basket Analysis of a retail computer store, Customer Segmentation of a customer database and Direct Marketing. The following models are used: Linear Regression, Logistic Regression, Association Rules, K-means Clustering and Decision Trees. Through these practical case studies we generate actionable business insights!

Data analysis is critical in business. Get ahead in your career with this important skill. Management depends on decision making and problem solving.   They depend on analytical findings. Not only do we need good sources of data, but we need skills that allow us to interpret and report the results. Discover techniques and best practices for analysis by learning the analytical process.

This course helps you learn simple but powerful ways to work with data. It is designed to be help people with limited statistical or programming skills quickly become productive in an increasingly digitized workplace. In this course you will use R (an open-sourced, easy to use data mining tool) and practice with real life data-sets. We focus on the application and provide you with plenty of support material for your long term learning. It also includes a project that you can attempt when you feel confident in the skills you learn.

Challenges are multifarious. Overwhelming nos. of transactions, loss of conventional (paper) audit trail, system based controls, ever increasing and complex compliance requirements are amongst the prime reasons why traditional methods of collecting and evaluating evidence (like vouching and verification) are no longer adequate. The auditor can no longer treat Information Systems as a ‘Black Box’ and audit around it. His methods and techniques have to change. This change is what the world calls today, ‘Assurance Analytics’ i.e. data analysis from an ‘audit perspective’. Using advance features of MS Excel, the auditor can access client’s data from their databases and analyse it to discharge the onerous duty cast on him. Since over 15 years, CA Nikunj Shah has been perfecting these techniques of ‘assurance analytics’. These include digital analysis techniques like Benford’s Law, Relative Size Factor Theory (RSF) and Pareto’s 80-20 rule that have enabled auditors and forensic investigators to identify control failures and over rides, detect non-compliance with laws, zero down on questionable transactions and identify red flags lost in millions of transactions. It is like quickly finding the needle in a hay stack!! In this unique course, your favourite instructor shall share the best of his research, auditing and training experience. The participants shall learn, step-by-step, the nuts-and-bolts details of using advance features of Microsoft® Excel coupled with the instructor’s insights to apply them in real-world audit situations. Each section shall equip participants with assurance analytic techniques using real-world examples and learn-by-doing exercises.

Welcome to Data Analytics Foundations for Accountancy II! I'm excited to have you in the class and look forward to your contributions to the learning community. To begin, I recommend taking a few minutes to explore the course site. Review the material we’ll cover each week, and preview the assignments you’ll need to complete to pass the course. Click Discussions to see forums where you can discuss the course material with fellow students taking the class. If you have questions about course content, please post them in the forums to get help from others in the course community. For technical problems with the Coursera platform, visit the Learner Help Center. Good luck as you get started, and I hope you enjoy the course!