In this course, you'll get an in-depth look at the SQL SELECT statement and its main clauses. The course focuses on big data SQL engines Apache Hive and Apache Impala, but most of the information is applicable to SQL with traditional RDBMs as well; the instructor explicitly addresses differences for MySQL and PostgreSQL. By the end of the course, you will be able to • explore and navigate databases and tables using different tools; • understand the basics of SELECT statements; • understand how and why to filter results; • explore grouping and aggregation to answer analytic questions; • work with sorting and limiting results; and • combine multiple tables in different ways. To use the hands-on environment for this course, you need to download and install a virtual machine and the software on which to run it. Before continuing, be sure that you have access to a computer that meets the following hardware and software requirements: • Windows, macOS, or Linux operating system (iPads and Android tablets will not work) • 64-bit operating system (32-bit operating systems will not work) • 8 GB RAM or more • 25GB free disk space or more • Intel VT-x or AMD-V virtualization support enabled (on Mac computers with Intel processors, this is always enabled; on Windows and Linux computers, you might need to enable it in the BIOS) • For Windows XP computers only: You must have an unzip utility such as 7-Zip or WinZip installed (Windows XP’s built-in unzip utility will not work)

Teaching 11 Courses in Excel and Data Analysis! OVER 100,000 visitors visit my blog ExcelDemy dot com every month!! OVER 134,141 successful students have already taken my online courses since November 2015 with 8,019 total Reviews!!! ************************************************************************************** What students are saying about this course? ~ Very clear, concise explanation of basic and more advanced statistical Excel functions - Donna M Knapp ~ This is an excellent well taught course. The explanations are clear and concise. The course moves at a comfortable pace. I learned a lot from this course and shouldn't have any difficulty applying the concepts to future projects. Well done. - Bill Hengen ************************************************************************************** Welcome to my brand new course on Data Analysis in Excel with Statistics: Get Meanings of Data . I want to start with a quote from Daniel Egger. He is a professor at Duke University. He says: “No commercial for-profit company that is in a competitive market can remain profitable or even survive over the next five years without incorporating best practices for business data analytics into their operations.” So learning how to analyze data will be the most valuable expertise in your career in the next five years. Excel will analyze and visualize data easily – this is why Excel is created and this is why Excel is the most popular spreadsheet program in the world. Microsoft Company has added new data analysis features, functions, and tools in every new version of Excel. Before going into the course: I want to warn you about something. Excel is just a tool. To analyze data you will use this tool. But analyzing data requires that you know some basic statistics and probability theories. Most of the statistics and probability concepts that are necessary to analyze data effectively are covered in your undergraduate level courses. But in this course, at first, I have discussed the theory at first, then I have advanced to teach you how to use that theory in business with the help of Excel. Let’s discuss now what I will cover in this course. It is tough to build a course on data analysis using Excel as so many topics are there to be covered. So I have used the guidelines of the Project Management Institute (PMI) to create this course. The topics I am going to cover in this course are: Overview of Data analysis: I will start with an overview of the data analysis. I will describe how you will calculate common measures of your data, I will introduce you to the central limit theory and then I will provide my advice for minimizing error in your calculations. Visualizing Data: Then I will teach you how to visualize your data using histograms, how to identify relationships among data by creating XY Scatter charts and forecast future results based on Existing data. Building Hypothesis: Then I will show you how to formulate a null and alternative hypothesis, how to interpret the results of your analysis, and how to use the normal, binomial, and Poisson distributions to model your data. Relationships between data sets: Finally I will show you how to analyze relationships between data sets using co-variance, how to identify the strength of those relationships through correlation, and then I will introduce you to Bayesian analysis. Case Study: Summarizing Data by Using Histograms Case Study: Summarizing Data by Using Descriptive Statistics Case Study: Estimating Straight-line Relationships Case Study: Modeling Exponential Growth Case Study: Using Correlations to Summarize Relationships Case Study: Using Moving Averages to Understand Time Series Analyzing Business data is a must need expertise for every employee of a company. Your company will not survive another five years if it does not take serious business data. And you could be the best employee in your company to direct the business in the smartest way. So keep learning business data analysis with this course.

This is the second course in the four-course specialization Python Data Products for Predictive Analytics, building on the data processing covered in Course 1 and introducing the basics of designing predictive models in Python. In this course, you will understand the fundamental concepts of statistical learning and learn various methods of building predictive models. At each step in the specialization, you will gain hands-on experience in data manipulation and building your skills, eventually culminating in a capstone project encompassing all the concepts taught in the specialization.

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!