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Top 6 Data Science Programming Languages for 2022 - DataFlair

#artificialintelligence

Data Science has become one of the most popular technologies of the 21st Century. With a high demand for Data Scientists in industries, there is a need for people who possess the required skills in order to become proficient in this field. Besides mathematical skills, there is a requirement for programming expertise. But before gaining expertise, an aspiring Data Scientist must be able to make the right decision about the type of programming language required for the job. In this article, we will go through some of the required data science programming languages in order to become a proficient Data Scientist.


Could Big Data Apps Prevent the Next Pandemic?

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For programmers, algorithms and data structures are their most essential subjects--a programmer's bread and butter if you will. If you want to enter the field of programming and hit the ground running, you'll need to master the most common data structures and boost your resume with in-demand skills. Here, we'll explore the eight most important data structures every programmer should know, including what they do and where to use them. To start, let's gain a fundamental understanding of what a data structure is. Data structures are methods of storing and organizing data in a computer system so that operations can be performed upon them more efficiently.


Learn Data Science & Machine Learning with R from A-Z

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Welcome to the Learn Data Science and Machine Learning with R from A-Z Course! In this practical, hands-on course you'll learn Welcome to the Learn Data Science and Machine Learning with R from A-Z Course! In this practical, hands-on course you'll learn how to program in R and how to use R for effective data analysis, visualization and how to make use of that data in a practical manner. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. Our main objective is to give you the education not just to understand the ins and outs of the R programming language, but also to learn exactly how to become a professional Data Scientist with R and land your first job.


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

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In this practical, hands-on course you'll learn how to program using Python for Data Science and Machine Learning. 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. We'll go over some of the best and most important Python libraries for data science such as NumPy, Pandas, and Matplotlib NumPy -- A library that makes a variety of mathematical and statistical operations easier; it is also the basis for many features of the pandas library.


Forecasting: theory and practice

arXiv.org Machine Learning

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.


Big Data Industry Predictions for 2022 - insideBIGDATA

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As a result, all major cloud providers are either offering or promising to offer Kubernetes options that run on-premises and in multiple clouds. While Kubernetes is making the cloud more open, cloud providers are trying to become "stickier" with more vertical integration. From database-as-a-service (DBaaS) to AI/ML services, the cloud providers are offering options that make it easier and faster to code. Organizations should not take a "one size fits all" approach to the cloud. For applications and environments that can scale quickly, Kubernetes may be the right option. For stable applications, leveraging DBaaS and built-in AI/ML could be the perfect solution. For infrastructure services, SaaS offerings may be the optimal approach. The number of options will increase, so create basic business guidelines for your teams.


Data Visualization with Python Masterclass

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Anyone who has programming experience and wants to learn data visualization and improve the skills. Statisticians and mathematicians who want to data visualization. Data analysts who want to learn data visualization. If you are one of these, you are in the right place.


A Brief Introduction to Python

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Now, let's get to our main topic. What can you do with Python? In this part, I try to prepare roughly some examples that you can do with python. Developing web applications is one of the most in-demand skills. In here, Python present some toolkits and package.


Data Science & Machine Learning with R from A-Z Masterclass

#artificialintelligence

Welcome to the Learn Data Science and Machine Learning with R from A-Z Course! In this practical, hands-on course you'll learn how to program in R and how ... Welcome to the Learn Data Science and Machine Learning with R from A-Z Course! In this practical, hands-on course you'll learn how to program in R and how to use R for effective data analysis, visualization and how to make use of that data in a practical manner. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. Our main objective is to give you the education not just to understand the ins and outs of the R programming language, but also to learn exactly how to become a professional Data Scientist with R and land your first job.


Python for Data Science & Machine Learning from A-Z

#artificialintelligence

Become a professional Data Scientist, Data Engineer, Data Analyst or Consultant Learn data cleaning, processing, wrangling and manipulation How to create resume and land your first job as a Data Scientist How to use Python for Data Science How to write complex Python programs for practical industry scenarios Learn Plotting in Python (graphs, charts, plots, histograms etc) Learn to use NumPy for Numerical Data Machine Learning and it's various practical applications Supervised vs Unsupervised Machine Learning Learn Regression, Classification, Clustering and Sci-kit learn Machine Learning Concepts and Algorithms Use Python to clean, analyze, and visualize data Building Custom Data Solutions Statistics for Data Science Probability and Hypothesis Testing 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. We'll go over some of the best and most important Python libraries for data science such as NumPy, Pandas, and Matplotlib NumPy -- A library that makes a variety of mathematical and statistical operations easier; it is also the basis for many features of the pandas library. Pandas -- A Python library created specifically to facilitate working with data, this is the bread and butter of a lot of Python data science work.