Data Science:Data Mining & Natural Language Processing in R, Harness the Power of Machine Learning in R for Data/Text Mining, & Natural Language Processing with Practical Examples Created by Minerva SinghPreview this Course - GET COUPON CODE 100% Off Udemy Coupon .

Learn to carry out pre-processing, visualization and machine learning tasks such as: clustering, classification and regression in R. You will be able to mine insights from text data and Twitter to give yourself & your company a competitive edge. My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals.

In this course, we will discuss Azure Machine Learning in detail. You will learn what features it provides and how it is used. We will explore how to process some real-world datasets and find some patterns in that dataset. This course teaches you how to design, deploy, configure and manage your machine learning models with Azure Machine Learning. The course will start with an introduction to the Azure ML toolset and features provided by it and then dive deeper into building some machine learning models based on some real-world problems 4. Data Visualization with Python: The Complete Guide Data Visualization with Python: The Complete Guide.

The power of data is undeniable, especially organized data. This is why currently data scientists rake in an average salary of over $100,000! This is also why Big Data and Data Analytics have become hot topics in todays world. All things considered we have designed this course aimed at complete beginners as well as intermediate students who want to master the art of data analytics and learn exactly how to make sense of data! The course has been designed to help breakdown everything you need to understand exactly how to get started with Data Science.

Become a true data scientist & machine learning expert with full industry knowledge Apply different predictive models and machine learning algorithms into use cases in different business areas Present analytical results to various users Master Text Mining & Natural Language Processing (NLP) using Python & Spark for sentimental analysis Work on Python with SQL on SQLite, Redshift, SAS, MongoDB, Spark and other data sources Become industry expert in banking, marketing, credit risk and product-user recommender system Collect and analyze Big Data in different systems Use AWS and Azure for Cloud Computing Master fundamental Python programming Apply generic Object Oriented Programming (OOP) Conduct real world capstone projects to build up career path Master useful data engineering knowledge and skills Convert homework and practices into your own knowledge and skills Use all famous graphics tools such as matplotlib, plotly, seaborn and ggplot into data visualization Any one should be able to use computer including being able to install software Desire to learn Python, Data Science and Cloud Computing Prior exposure to programming languages will be helpful Basic knowledge and skills of math In this nearly 50 hours course, we will walk through the complete Python for starting the career in data science and cloud computing! This is so far the most comprehensive guide to mastering data science, business analytics, statistical tests & modelling, data visualization, machine learning, cloud computing, Big data analysis and real world use cases with Python. Data science career is not just a traditional IT or pure technical game – this is a comprehensive area, and above all, you must know why you conduct data analysis and how to deploy your results to generate values for the company you are working for or your own business. Therefore, this course not only covers all aspects of practical data science, but also the necessary data engineering skills and business model & knowledge you need in different industries. Whether you are working in financing, marketing, health companies, or you are running start-up, knowing the complete application of Python for data science and cloud computing is the must to achieving various business objective and looking insights into data.

Learn to carry out pre-processing, visualization and machine learning tasks such as: clustering, classification and regression in R. You will be able to mine insights from text data and Twitter to give yourself & your company a competitive edge. You'll start by absorbing the most valuable R Data Science basics and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in R. My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement R based data science in real life.

Learn to carry out pre-processing, visualization and machine learning tasks such as: clustering, classification and regression in R. You will be able to mine insights from text data and Twitter to give yourself & your company a competitive edge. My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals.

Learn to carry out pre-processing, visualization and machine learning tasks such as: clustering, classification and regression in R. You will be able to mine insights from text data and Twitter to give yourself & your company a competitive edge. My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals.