In the capstone, students will build a series of applications to retrieve, process and visualize data using Python. The projects will involve all the elements of the specialization. In the first part of the capstone, students will do some visualizations to become familiar with the technologies in use and then will pursue their own project to visualize some other data that they have or can find. Chapters 15 and 16 from the book “Python for Everybody” will serve as the backbone for the capstone. This course covers Python 3.

Welcome to Data Analysis Analytics Bootcamp content powered by TakenMind. Are you interested to learn how zetabytes of data are processed by top tech companies to analyse data inorder to boost their business growth? Well, for a beginner you are at the right place and this is the most probably the right time for you to learn this. The average data scientist today earns $123,000 a year, according to Indeed research. But the operating term here is “today,” since data science has paid increasing dividends since it really burst into business consciousness in recent years. This course has its base on financial Analysis and the following concepts are covered: Python Fundamentals Pandas for Efficient Data Analysis NumPy for High Speed Numerical Processing Matplotlib for Data Visualization Pandas for Data Manipulation and Analysis Seaborn Data Visualization Worked-up examples. Learn how to analyze data using Python. This course will take you from the basics of Python to exploring many different types of data. You will learn how to prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, predict future trends from data, and more! You will learn how to: Import data sets Clean and prepare data for analysis Manipulate pandas DataFrame Summarize data Build machine learning models using scikit-learn Build data pipelines Data Analysis with Python is delivered through lecture, hands-on labs, and assignments. It includes following parts: Data Analysis libraries: will learn to use Pandas DataFrames, Numpy multi-dimentional arrays, and SciPy libraries to work with a various datasets. We will introduce you to pandas, an open-source library, and we will use it to load, manipulate, analyze, and visualize cool datasets. Then we will introduce you to another open-source library, scikit-learn, and we will use some of its machine learning algorithms to build smart models and make cool predictions.

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!

This course provides an introduction to basic data science techniques using Python. Students are introduced to core concepts like Data Frames and joining data, and learn how to use data analysis libraries like pandas, numpy, and matplotlib. This course provides an overview of loading, inspecting, and querying real-world data, and how to answer basic questions about that data. Students will gain skills in data aggregation and summarization, as well as basic data visualization.