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Latest 20 Best Data Science on Udemy to Consider

Latest 20 Best Data Science on Udemy to Consider 

New Student Deal

Description

Web scraping refers to systematically extracting data ('scraping') from websites ('web').

In business intelligence, web scraping is a cost-effective way for companies to collect market information such as product description, seller information, product price, customer review and so. Web scraping also provides companies information in a timely and unbiased manner when compared to conventional sources, such as market research reports.

New Student Deal

Description
In this course I demonstrate open source python packages for the analysis of vector-based geospatial data.  I use Jupyter Notebooks as an interactive Python environment.  GeoPandas is used for reading and storing geospatial data, exploratory data analysis, preparing data for use in statistical models (feature engineering, dealing with outlier and missing data, etc.), and simple plotting.  Statsmodels is used for statistical inference as it provides more detail on the explanatory power of individual explanatory variables and a framework for model selection.  Scikit-learn is used for machine learning applications as it includes many advanced machine learning algorithms, as well as tools for cross-validation, regularization, assessing model performance, and more.

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Description
Before applying any data science model its always a good practice to understand the true nature of your data. In this Course we will cover fundamentals and applications of statistical modelling. We will use R Programming Language to run this analysis. We will start with Math, Data Distribution and statistical concepts then by using plots and charts we will interpret our data. We will use statistical modelling to prove our claims and use hypothesis testing to confidently make inferences.

New Student Deal

Description
Data Science grew through our experiences with Business Intelligence or BI, a field that became popular in 1990s. However, the last 20 years have seen unprecedented improvement in our ability to take actions using Artificial Intelligence. As we adopt the BI methodologies to AI deployments, how will these methodologies morph to add considerations needed for model deployment, and machine learning.

New Student Deal

Description
Being a data scientist is one of the most lucrative and future proof careers with Glassdoor naming it the best job in America for the third consecutive year in a row with great future growth prospects and a median base salary of $110,000. I have recently made the transition from being a PhD student in Computer Science to a Senior Data Scientist at a large tech company. In this course I give you all the questions and answers that I used to prepare for my data science interviews as well as the questions and answers that I now expect when I am giving interviews to potential data science candidates. The course provides a complete list of 150+ questions and answers that you can expects in a typical data science interview including questions on machine learning, neural networks and deep learning, statistics, practical experience, big data technologies, SQL, computer science, culture fit, questions for the interviewer and brainteasers.

New Student Deal

Description
According to StackOverFow report 2020, Python is the most preferred programming language by industry professionals. The report also emphasis that employers prefer Python programmers and are willing to pay them more than any other programming language professionals.

The Harvard Business Review also labelled Data Science and Data Analysis as the Sexiest job of the 21st Century. So imagine if you combine the two(i.e. Python + Data). In my experience at Microsoft working as a Data Scientist, I can testify how software developers, data scientist or data analyst who use Python, become more efficient than others who use other programming languages.

New Student Deal

Description
Welcome to Complete Practical Time Series Analysis and Forecasting  in Python

Time series analysis and forecasting is one of the areas of Data Science and has a wide variety of applications in the industries in the current world. Many industries looking for a Data Scientist with these skills. This course covers all type of modeling techniques for forecasting and analysis.

We start with programming in Python which is the essential skill required and then we will exploring the fundamental time series theory to help you understand the modeling that comes afterward.

Then throughout the course, we will work with a number of Python libraries, providing you with complete training. We will use the powerful time-series functionality built into pandas, as well as other fundamental libraries such as NumPy, matplotlib, statsmodels, Sklearn, and ARCH.

New Student Deal

Description
Go, also know as Golang, is a modern programming language created  by 3 Great individuals and backed by Google to address

certain types of issues.

Being one of the fastest growing languages used in the DevOps and Microservices world, can it also be used for Data Science?

Data Science is being used everywhere and has several applications in the real world such as Machine Learning Applications to Natural Language processing and Deep Learning.

As everyone knows and would agree with me,Python is known to be the best language for doing Data Science in this present era, but can we also use Golang for doing Data Science?

With all the benefits and features of Golang - is it easy for beginners to pick up Golang and use it for Data Science?

Go is fast, easy to learn and cross compatible with a great concurrency feature as well as several web frameworks,etc but is it Data Science friendly?

Introducing Go4DataScience & Go4NLP.

New Student Deal

Description
This is a course mainly about Machine Learning. If you are new to python we cover the basics of python before moving on to other topics.

We start off by analysing data using pandas, and implementing some algorithms from scratch using Numpy. These algorithms include linear regression, Classification and Regression Trees (CART), Random Forest and Gradient Boosted Trees.

The focus of this course is on programming, however, we do cover the maths when it is important to do so. This is to ensure that you are ready for those theoretical questions at interviews, while being able to put Machine Learning into solid practice.

Some of the other key areas that we discuss include, unsupervised learning, time series analysis and Natural Language Processing. Scikit-learn is an essential tool that we use throughout the entire course.

We spend quite a bit of time on feature engineering and making sure our models don't overfit. Diagnosing models by splitting into training and testing as well as looking at the correct metric can make a world of difference.

I would like to highlight that we talk about Machine Learning Deployment, since this is a topic that is rarely talked about. The key to being a good data scientist is having a model that doesn't decay in production.

I hope you enjoy this course and please don't hesitate to contact me for further information.

New Student Deal

Description
Do you want to super charge your career by learning the most in demand skills? Are you interested in data science but intimidated from learning by the need to learn a programming language?

I can teach you how to solve real data science business problems that clients have paid hundreds of thousands of dollars to solve. I'm not going to turn you into a data scientist; no 2 hour, or even 40 hour online course is able to do that. But this course can teach you skills that you can use to add value and solve business problems from day 1.


New Student Deal

Description
Learn Python for Data Science & Machine Learning from A-Z

In this practical, hands-on course you’ll learn how to program using Python for Data Science and Machine Learning. This includes data analysis, visualization, and how to make use of that data in a practical manner.

Our main objective is to give you the education not just to understand the ins and outs of the Python programming language for Data Science and Machine Learning, but also to learn exactly how to become a professional Data Scientist with Python and land your first job.

New Student Deal

Description
This Course will design to understand Data Science using Machine Learning Algorithms with big data concept. Big data Analysis covered with machine learning algorithms. This Course divide in three part. Part 1 focus on Data Science with all important concept, Part 2 focus on Machine Learning with all necessary algorithms, Part 3 focus on Big Data with basic fundamental. The Machine Learning Algorithms  such as Linear Regression, Logistic Regression, SVM, K Mean, KNN, Na├»ve Bayes, Decision Tree and Random Forest are covered with case studies. The course provides path to start career in Data Science,  Machine Learning and big data . Machine Learning Types such as Supervise Learning, Unsupervised Learning, Reinforcement Learning are also covered. Machine Learning concept such as Train Test Split, Machine Learning Models, Model Evaluation are also covered.

New Student Deal

Description
Welcome! If you see Data Science as a potential career in your future, this is the perfect course to get started with.

Our course does not require any previous Data Science experience. The goal of 'Data Science for Beginners' is to get you acquainted with Data Science methodology, data science concepts, programming languages, give you a peak into how machine learning works, and finally show you a data science tool like GitHub, which lets you collaborate with your colleagues.

Now, while this is a beginner course,  it does not mean that it is an easy course. For example in the Data Science methodology section, many different concepts are introduced. But please keep in mind that a. you will get concrete examples of what each concept means when it is brought up b. you can ask questions in the Q and A and c. most importantly, you are not meant to understand all the concepts .  Going through the methodology is meant to introduce you to concepts, not prepare you to fully apply them. You will get a chance to do this in other courses (ours or other providers).

New Student Deal

Description
Perform simple or complex statistical calculations using Python! - You don't need to be a programmer for this :)

You are not expected to have any prior knowledge of Python. I will start with the basics. Coding exercises are provided to test your learnings.

The course not only explains, how to conduct statistical tests using Python but also explains in detail, how to perform these using a calculator (as if, it was the 1960s). This will help you in gaining the real intuition behind these tests.

Learn statistics, and apply these concepts in your workplace using Python.

The course will teach you the basic concepts related to Statistics and Data Analysis, and help you in applying these concepts. Various examples and data-sets are used to explain the application.

I will explain the basic theory first, and then I will show you how to use Python to perform these calculations.

New Student Deal

Description
Are you interested in data science and machine learning, but you don't have any background, and you find the concepts confusing?

Are you interested in programming in Python, but you always afraid of coding?

I think this course is for you!

Even if you are familiar with machine learning, this course can help you to review all the techniques and understand the concept behind each term.

This course is completely categorized, and we don't start from the middle! We actually start from the concept of every term, and then we try to implement it in Python step by step. The structure of the course is as follows:

New Student Deal

Description
This is the tutorial you've been looking for to become a modern JavaScript machine learning master in 2020. It doesn’t just cover the basics, by the end of the course you will have advanced machine learning knowledge you can use on your resume. From absolute zero knowledge to master - join the TensorFlow.js revolution.

Machine Learning in TensorFlow.js provides you with all the benefits of TensorFlow, but without the need for Python. This is demonstrated using web-based examples, stunning visualizations, and custom website components.

This course is project-based so you will not be learning a bunch of useless coding practices. At the end of this course, you will have real-world apps to use in your portfolio. We feel that project-based training content is the best way to get from A to B. Taking this course means that you learn practical, employable skills immediately.

You can use the projects you build in this course to add to your LinkedIn profile. Give your portfolio fuel to take your career to the next level.

New Student Deal

Description
The most commonly available and widely used type of data in healthcare is claims data. Claims data is sometimes also called ‘billing data’ or administrative data. The reason why claims data is the most large scale, reliable and complete type of big data in healthcare is rather straightforward. It has to do with reimbursement, that is, the payment of health care goods and services depends on claims data. Healthcare providers may not always find the time to fill in all required paperwork in healthcare, but they will always do that part of their administration on which their income depends. Thus, in many cases, analyzing healthcare claims data is a much more pragmatic alternative for extracting valuable insights.

Claims data allows for the analysis of many non-biological elements pertaining to the organization of health care, such as patient referral patterns, patient registration, waiting times, therapy adherence, health care financing, patient pathways, fraud detection and budget monitoring. Claims data also allows for some inferences about biological facts, but these are limited when compared to medical records.

New Student Deal

Description
Recently in last couple of years we have seen and listened the buzzwords such as Data Science, Artificial Intelligence, Machine

Learning and Deep Learning. Most of us if we are not very close to this world of technology take all these buzz words with same

context and consider them as same. But in reality they are quite different and covers different parts of the Data Science and Analytics

world.

This course has been designed for beginners who are new to it and wants to enter into this field. Obviously it has a lot of scope in the

upcoming years and there is no stopping, this will grow even further where the entire world will be digitized and automated.

New Student Deal

Description
In this course, our main focus will be on learning the advance level stuff in Python Programming Language. My main focus is that you not only learn these advance level concepts but you can use them to make advance level applications in Python. I will assume that you know the basics of Python Programming Language and you are here to learn only the Advance Level Programming in Python. As grabbing the main concept behind Advance Topics is not simple therefor, special attention is given to the intuition part of each concept where we gonna understand these concepts with proper animated slides. The implementation is done by real time examples so that you can best use the concept.

Also not only understanding these advance concepts are important but to make something real out of it is very important or else there is no reason to learn Advance Programming therefor we will also make real time Advance level Applications in Python using Advance level concepts that we have covered in this course. We have made couple of GUI Based Advanced Level Applications in Python.

New Student Deal

Description
Regression Analysis for Machine Learning & Data Science in R

My course will be your hands-on guide to the theory and applications of supervised machine learning with the focus on regression analysis using the R-programming language.

Unlike other courses, it offers NOT ONLY the guided demonstrations of the R-scripts but also covers theoretical background that will allow you to FULLY UNDERSTAND & APPLY REGRESSION ANALYSIS (Linear Regression, Random Forest, KNN, etc) in R (many R packages incl. caret package will be covered) for supervised machine learning and prediction tasks.

This course also covers all the main aspects of practical and highly applied data science related to Machine Learning (i.e. regression analysis). Thus, if you take this course, you will save lots of time & money on other expensive materials in the R based Data Science and Machine Learning domain.

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