Data analysis has recently emerged as a very important focus for a huge range of organizations and businesses. R makes detailed data analysis easier, by making advanced data exploration and insight accessible to anyone interested in learning it. This course's hands-on approach will help you perform data analysis. You will learn to perform social network analysis, to uncover hidden insights and trends from data. Later you will perform geospatial analysis to bring data into action with the easy-to-follow examples featured in the video course. By the end of this course, you will mastered quickly adapting the example code for your own needs, thus saving yourself the time-consuming task of constructing code from scratch. About the Author Viswa Viswanathan is an associate professor of Computing and Decision Sciences at the Stillman School of Business in Seton Hall University. After completing his PhD in Artificial Intelligence, Viswa spent a decade in Academia and then switched to a leadership position in the software industry for a decade. During this period, he worked for Infosys, Igate, and Starbase. He embraced Academia once again in 2001. Viswa has taught extensively in diverse fields, including operations research, computer science, software engineering, management information systems, and enterprise systems. In addition to teaching at the university, Viswa has conducted training programs for industry professionals. He has written several peer-reviewed research publications in journals such as Operations Research, IEEE Software, Computers and Industrial Engineering, and International Journal of Artificial Intelligence in Education. He authored a book entitled Data Analytics with R: A Hands-on Approach. Viswa thoroughly enjoys hands-on software development, and has single-handedly conceived, architected, developed, and deployed several web-based applications. Apart from his deep interest in technical fields such as data analytics, Artificial Intelligence, computer science, and software engineering, Viswa harbors a deep interest in education, with a special emphasis on the roots of learning and methods to foster deeper learning. He has done research in this area and hopes to pursue the subject further. Viswa would like to express deep gratitude to professors Amitava Bagchi and Anup Sen, who were inspirational during his early research career. He is also grateful to several extremely intelligent colleagues, notably Rajesh Venkatesh, Dan Richner, and Sriram Bala, who significantly shaped his thinking. His aunt, Analdavalli; his sister, Sankari; and his wife, Shanthi, taught him much about hard work, and even the little he has absorbed has helped him immensely. His sons, Nitin and Siddarth, have helped with numerous insightful comments on various topics. Shanthi Viswanathan is an experienced technologist who has delivered technology management and enterprise architecture consulting to many enterprise customers. She has worked for Infosys Technologies, Oracle Corporation, and Accenture. As a consultant, Shanthi has helped several large organizations, such as Canon, Cisco, Celgene, Amway, Time Warner Cable, and GE among others, in areas such as data architecture and analytics, master data management, service-oriented architecture, business process management, and modeling. When she is not in front of her Mac, Shanthi spends time hiking in the suburbs of NY/NJ, working in the garden, and teaching yoga. Shanthi would like to thank her husband, Viswa, for all the great discussions on numerous topics during their hikes together and for exposing her to R and Java. She would also like to thank her sons, Nitin and Siddarth, for getting her into the data analytics world.

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.

Are you ready to start your path to becoming a data manipulation and visualization expert? Do you know nothing about data manipulation and visualization but want to know them well enough so that you can implement projects? Are you worried that learning data manipulation and visualization is going to be tough? Do you ever wonder if Data Scientists having a good understanding of data manipulation and visualization have a bright future? Listen to what the most relevant people have to say: “Data Science will automate jobs that most people thought could only be done by people.” ~ Dave Waters “Just as electricity transformed almost everything 100 years ago, today, I actually have a hard time thinking of an industry that I don’t think Artificial Intelligence/Data Science will transform in the next several years.” ~ Andrew Ng “A breakthrough in Data Science would be worth ten Microsofts.” ~ Bill Gates Data is, without a doubt, the new electricity. Over the next several years, data is going to transform everything. And guess what? Data manipulation and visualization skills are at the core of Data Science. There are lots of courses and lectures out there regarding data manipulation and visualization. But this course is different! This course follows a step-by-step and straightforward methodology. In every new tutorial, you will build on what you have already learned and move one extra step forward. You are then assigned a small task that you solve at the beginning of the next video. You start by first learning the theoretical part of a data visualization concept. Then, you implement everything practically, using Python. We are using Python as a programming language because it has a lot of demand in the market. Plus, it is a super easy and efficient language. Python is the hottest programming language nowadays if we talk about machine learning or data science, and thus for data manipulation and visualization. You don’t need to worry if you don’t know Python. We have a course for absolute beginners on our channel as well. And by the way, you need not know a lot of Python for this course. You will explore packages and will rely mostly on one-liners as compared to lengthy codes. You will also implement a lot of mini-projects in live coding sessions in which you will gain a complete look and feel on how to implement modules in data manipulation and visualization. With over 12 hours of HD video lectures divided into 75+ small videos and detailed code notebooks for every lecture, this is one of the most comprehensive courses for data manipulation and visualization on Udemy! You’ll not only learn how to solve problems in data manipulation and visualization, but you will also be equipped with the right tools in your hand! Here are just a few of the topics that you will be learning: Introduction to Data Structures Importance of NumPy How Pandas efficiently processes large data files Data Preparation Visualizations using multiple packages Interactive visualizations using multiple packages Process Covid-19 real data file using Pandas Geographic data mapping Plotting directly from Pandas and much, much more! Enroll in this course and become a data manipulation and visualization expert today!

If you want to learn simple, effective techniques to create impactful Excel Dashboards and Data Analysis, this course is ideal. Updated in 2021 with full high definition video , this course teaches you practical skills you can apply in your work environment immediately. Just take a look at the recent comments and reviews! "Presentation is great! Very practical solutions for everyday business analysis challenges." "I walked away with a lot much practical knowledge. Really excited to apply this knowledge in my work. I have taken other courses from Ian and never disappointed." "One of the best courses I have taken on Udemy!!! Very easy to understand, not long and drawn out and focuses on what is relevant in the work place. Will definitely recommend it to my team." "Really enjoyed the course, so many practical tips, which are easily explained. Thanks" In this course, you will learn the BEST techniques and tools for turning data into MEANINGFUL analysis using Excel This course is lead by Ian Littlejohn - an international trainer, consultant and data analyst with over 125 000 enrollments & 100 000 students on Udemy. Ian specializes in teaching data analysis techniques, Excel Pivot Tables, Power Pivot, Microsoft Power BI, Google Data Studio & Amazon Quicksight & his courses average over 4.5 stars out of 5. **** Life-time access to course materials and practice activities.  **** The Complete Introduction to Business Data Analysis teaches you how to apply different methods of data analysis to turn your data into new insight and intelligence . The ability to ask questions of your data is a powerful competitive advantage , resulting in new income streams, better decision making and improved productivity.  A recent McKinsey Consulting report has identified that data analysis is one of the most important skills required in the American economy at the current time. During the course you will understand why the form of analysis is important and also provide examples of analysis using Excel. The following methods of analysis are included: Key Metrics Comparison Analysis Trend Analysis Ranking Analysis Interactive Dashboards Contribution Analysis Variance Analysis Pareto Analysis Frequency Analysis Correlations The Complete Introduction to Business Data Analysis is designed for all business professionals who want to take their ability to turn data into information to the next level . If you are an Excel user then you will want to learn the easy-to-use techniques that are taught in this course. This course is presented using Excel in Office 365.  However, the course is also suitable for: Excel 2013 Excel 2016 Excel 2019 Please note that this course does not include any complicated formulas, VBA or macros.  The course utilizes drag and drop techniques to create the majority of the different data analysis techniques.

COURSE ABSTRACT This course aims to provide a comprehensive introduction to the SAS analytic software for Windows. Through a mixture of lectures and in-class examples, quizzes, and take-home assignments, students will gain experience using the SAS system for data manipulation, management and analysis. You will also expect A LOT of extracurricular learning materials for self-pace learning, treat it as a BONUS! Emphasis will be placed on the skills and techniques necessary for efficient data manipulation, management and analysis. It is designed for students with little to no background with SAS, and an understanding of the basic statistical concepts. This will be an excellent choice for your first SAS introduction course for your data analysis career. Plus, you will get a FREE course - SAS Data Issue Handling and Good Programming Practice (check out in the bonus lecture)!!! WHAT DO I EXPECT? A comprehensive course design from SAS basics to statistical analysis Many in-class examples, exercises and take-home assignment Master various techniques for data importing Solid understanding of variable attributes, and learn various character/numeric functions IF-THEN/ELSE statements Do loop and counter variables Master DATA step with Concatenation, Merge, etc. Exposed to several useful PROC step (PRINT, SORT, TRANSPOSE, etc.). Descriptive statistics procedures (MEANS, UNIVARIATE, FREQ) Hypothesis testing (UNIVARIATE, TTEST, ANOVA) Correlations (CORR) Regression (REG) PREREQUISITE COURSES AND KNOWLEDGE: No SAS background required; Basic knowledge of statistics is preferred.

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.