In this article you can find online Data Science courses to become a master.
Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from noisy, structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains. Data science is related to data mining, machine learning and big data.
Data science is a “concept to unify statistics, data analysis, informatics, and their related methods” in order to “understand and analyze actual phenomena” with data. It uses techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, information science, and domain knowledge.However, data science is different from computer science and information science. Turing Award winner Jim Gray imagined data science as a “fourth paradigm” of science (empirical, theoretical, computational, and now data-driven) and asserted that “everything about science is changing because of the impact of information technology” and the data deluge.
Complete Python Data Science, Deep Learning, R Programming
Oak Academy via Udemy
Welcome to Complete Python Data Science, Deep Learning, R Programming course.Python Data Science A–Z, Data Science with Machine Learning A–Z, Deep Learning A–Z, Pandas, Numpy and R Statistics. Data science, python data science, r statistics, machine learning, deep learning, data visualization, NumPy, pandas, data science with r, r, complete data science, maths for data science, data science a–z. Data Science A–Z, Python Data Science with Machine Learning, Deep Learning, Pandas, Numpy, Data visualization, and R. Ready for the Data Science career? Are you curious about Data Science and looking to start your self–learning journey into the world of data? Are you an experienced developer looking for a landing in Data Science! In both cases, you are at the right place!
Data Science:Hands-on Diabetes Prediction with Pyspark MLlib
School of Disruptive Innovation via Udemy
Would you like to build, train, test and evaluate a machine learning model that is able to detect diabetes using logistic regression? This is a Hands–on Machine Learning Course where you will practice alongside the classes. The dataset will be provided to you during the lectures. We highly recommend that for the best learning experience, you practice alongside the lectures. You will learn more in this one hour of Practice than hundreds of hours of unnecessary theoretical lectures. The entire course has been divided into tasks. Each task has been very carefully created and designed to give you the best learning experience. In this hands–on project, we will complete the following tasks.
Create & Deploy Data Science,Deep Learning Web Apps 2021
Pianalytix via Udemy
Deployment of machine learning models means operationalizing your trained model to fulfill its intended business use case. If your model detects spam emails, operationalizing this model means integrating it into your company’s email workflow—seamlessly. So, the next time you receive spam emails, it’ll be automatically categorized as such. This step is also known as putting models into production. Machine learning models are deployed when they have been successful in the development stage—where the accuracy is considered acceptable on a dataset not used for development (also known as validation data). Also, the known faults of the model should be clearly documented before deployment.
2021 R 4.0 Programming for Data Science || Beginners to Pro
Laxmi Kant via Udemy
Are you ready to accept the R Programming Challenge? Want to analyze and get insights from your datasets? This Course is for You!!! You will learn R programming in a very interactive way. I will be explaining to you each line of code. You do not need any prior experience in coding. Anyone can start learning. We will start with R Programming and R–Studio set up on the computer thereafter I will be teaching you fundamentals of R Programming.This course is in development. 20+ hours of lectures will be added to the course. Kindly, keep checking regularly.
Data Science & Machine Learning(Theory+Projects)A-Z 90 HOURS
AI Sciences via Udemy
Data science is a large field of study that covers data systems and processes. These systems and processes are aimed at maintaining data sets as well as getting meaning out of them. Machine Learning (ML), a branch of AI, is the concept that systems can automatically learn and adapt from experience without human intervention. ML, essentially, aims to equip machines with independent learning techniques. Data Science & Machine Learning Full Course in 90 Hours is exhaustive and covers various topics in both these fields in great detail. Data science specialists use a combination of algorithms, applications, principles, and tools to gain a real sense of random data clusters. You are probably aware that organizations worldwide are generating exponential amounts of data. So, monitoring and storing all this data becomes very difficult.
Data Science Project Planning
Gopinath Ramakrishnan via Udemy
Success of any project depends highly on how well it has been planned. Data science projects are no exception. Large number of data science projects in industrial settings fail to meet the expectations due to lack of proper planning at their inception stage. This course will provide a overview of core planning activities that are critical to the success of any data science project. We will discuss the concepts underlying – Business Problem Definition; Data Science Problem Definition; Situation Assessment; Scheduling Tasks and Deliveries.
Complete Guide to Data Science Applications with Streamlit
Derrick Mwiti & Namespace Labs via Udemy
Analyzing data and building machine learning models is one thing. Packaging these analyses and models such that they are sharable is a different ball game altogether. This course aims at teaching you the fastest and easiest way to build and share data applications using Streamlit. You don’t need any experience in building front–end applications for this. Here are some of the things you can expect to cover in this course.At the end of the course, you will have built several applications that you can include in your data science portfolio. You will also have a new skill to add to your resume. The course also comes with a 30–day money–back guarantee. Enroll now and if you don’t like it you will get your money back no questions asked.
Data science, machine learning, and analytics without coding
Eric Hulbert via Udemy
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.
Data Science 2022: Data Science & Machine Learning in Python
Ankit Mistry via Udemy
According to an IBM report, Data Science jobs would likely grow by 30 percent. The estimated figure of job listing is 2,720,000 for Data Science in 2020. And according to the US Bureau of Labor Statistics, about 11 million jobs will be created by 2026. Data Science, Machine Learning and Artificial Intelligence are hottest and trending technologies across the globe, almost every multinational organization is working on it and they need a huge number people who can work on these technologies. By keeping all the industry requirements in mind we have designed this course, with this single course you can start your journey in the field of Data Science. In this course we tried to cover almost everything that is comes under the umbrella of Data Science.
R Programming: R for Data Science and Data Analytics A-Z™
Teach Premium via Udemy
In this course first, you will learn how to install R and start programming on it. It will also help you to know the programming structures and functions. This R programming in Data Science and Data Analytics covers all the steps of Exploratory data analysis, Data pre–processing, and Modelling process. In EDA sections you will learn how to import data sets and create data frames from it. Then it will help you to visualize the variables using different plots. It will give you an initial structure of your data points. In Data pre–processing sections you will get the full idea of Missing value & outliers treatment and data split methods. Finally, you will be able to generate machine learning models using Linear and Logistic Regression.