Is it better to learn R or Python for a career as a data analyst? Learn more about how to choose the best statistical programming language for your career goals.

If you’re getting started in data analysis, you’ll find that one of the most important skills is proficiency in a statistical programming language. Data analysts use SQL (Structured Query Language) to communicate with databases, but when it comes to cleaning, manipulating, analyzing, and visualizing data, you’re looking at either Python or R.

Python vs. R: What’s the difference?

Both Python and R are free, open-source languages that can run on Windows, macOS, and Linux. Both can…

1.TensorFlow

The first in the list of python libraries for data science is TensorFlow. TensorFlow is a library for high-performance numerical computations with around 35,000 comments and a vibrant community of around 1,500 contributors. It’s used across various scientific fields. TensorFlow is basically a framework for defining and running computations that involve tensors, which are partially defined computational objects that eventually produce a value. To get in-depth knowledge on Python Please go through Best Python Programming Books

Features:

• Better computational graph visualizations
• Reduces error by 50 to 60 percent in neural machine learning
• Parallel computing to execute complex models
• Seamless library management…

This post was created in collaboration with Claudio Acquaviva, Solution Engineer, Kong, and Morgan Davies, Kong Alliances.

A service mesh is transparent infrastructure layer that has become a common architectural pattern for intra-service communication. By combining Amazon EKS and AWS App Mesh, you form a powerful platform for your microservices, addressing technical requirements that occur in service-to-service communication, including Best AWS Books load balancing, service discovery, observability, access control, tracing, health checks, and circuit breakers.

A modern enterprise solution requires clear management controls for the following categories:

• API Management covering external traffic ingress to the API endpoints.
• Service management capabilities…

Everything was good until a few aspirants pointed out that there are too many resources and many of them are expensive. Python programming was the only branch that had a number of really good courses, but it ends right there for beginners.

A few important questions on foundational data science struck me:

• What should one do after learning how to code? Are there topics that help you strengthen your foundations for data science?
• I hate math, and there are either very basic tutorials or too deep for me. Can you recommend a compact yet comprehensive course on Math and Statistics?
• …

As Big Data continues to grow in importance at Software as a Service (SaaS) companies, the field of Big Data analytics is a safe bet for any professional looking for a fulfilling, high-paying career.

If you’re considering starting or advancing your career in the field of Big Data and data science, we’ve described popular programming languages you might want to learn to give that career move a boost with Best R Books

Why Learn R?

A good data scientist is a passionate coder-slash-statistician, and there’s no better programming language for a statistician to learn than R. The standard among statistical programming languages, R…

One of my favorite features introduced to Tableau in the past couple of years has the ability to use relationships in data models. If you aren’t familiar with data modeling using relationships. Using this relatively newer technique to Tableau, I am going to go over how to create an extremely common use case for many in industry; forecasting inventory.

In this example, I am going to bring in data from 3 different sources to put together a mock use case for projecting a fictional material’s inventory levels.

Why Are We Doing This?

The basic answer to the question, why are we doing this, is because…

Plenty of packages implement the Scikit-learn estimator API.

If you’re already familiar with Scikit-learn, you’ll find the integration of these libraries pretty straightforward.

With these packages, we can extend the functionality of Scikit-learn estimators, and I’ll show you how to use some of them in this article.

Data formats

In this section, we’ll explore libraries that can be used to process and transform data.

Sklearn-pandas

You can use this package to map ‘DataFrame’ columns to Scikit-learn transformations. Then you can combine these columns into features.

To start using the package, install ‘sklearn-pandas’ via pip. The ‘DataFrameMapper’ can be used to map pandas data…

Introduction

The microservices architectural pattern is an architectural style that is growing in popularity, given its flexibility and resilience. Together with technologies such as Kubernetes, it is getting easier to bootstrap an application using a Microservices architecture as never before.

In short, the microservice architectural style is an approach to developing a single application as a suite of small services, each running in its own process and communicating with lightweight mechanisms, often an HTTP resource API. These services are built around business capabilities and independently deployable by fully automated deployment machinery. …