What skills must you master in order to be a good data scientist?

 Data science - Data - Data scientist - Skills - Cloud - 5G - Technical report

Why the cloud has become an opportunity for a data scientist? What is good practice for writing a relevant technical report?

The goal of data science is to make the most of data. This is when data management enters the picture. Data management is the process of transforming data from one form to another. This is critical since data science entails creating models, testing new features, and performing deep dives.

There's no doubting that data science is all about maximizing the value of raw data. Simply described, it is the process of extracting useful information from large amounts of unstructured data. There is no better way to organize and analyze data than to use statistics. Statistics aid in the identification of correlations between data sets.

Analytical concepts play a big role in data science. The success of a firm is directly linked to the quality with which a data scientist presents analytical insights. Do you want to know how data visualization may benefit you? A data scientist with strong visualization abilities, on the other hand, can display data in a way that everyone can understand.

There will be times in data science when it is required to communicate or display a model or project that does not yet exist. As a result, rather than relying on data analysts and/or data engineers, a good data scientist is one who can develop robust pipelines for your projects. This saves time as well.

Making well-informed and suitable conclusions based on data and facts is what critical thinking is all about. An ambitious data scientist must take this into consideration. While it may appear tough at first, it is certainly possible to master it over time.

Without programming, data science is a complete waste of time. A data scientist who is proficient in programming languages such as R, Python, Java, and others is more likely to succeed. Indeed, programming is the only way to give computer instructions. As a result, developing this skill is a sure bet.

A data scientist should, without a doubt, be able to address challenges. In truth, data science is linked to a slew of issues that require rapid attention.

It's critical to know where to begin in order to arrive at a solution. As a result, a data scientist must be able to solve problems and translate them into long-term, production-ready code.

Model deployment, or the use of new data to apply a prediction model, is closely related to data science. This type of model aids in better understanding customers/target audiences, allowing the company to work toward its objectives.

The term "data science" refers to the process of translating raw data into a format that everyone can understand in order to make better judgments. This emphasizes the importance of having great communication abilities. This ability allows you to communicate technical outcomes to non-technical team members.

It is unrealistic to expect data scientists to work alone. A data scientist's job necessitates tight collaboration with other departments like finance, IT, and operations, among others. This is why collaboration is so important.

Why the cloud has become an opportunity for a data scientist?

Mainly because you can take your data, take your information and put it in the cloud, and put it in a central storage system.
It allows you to bypass the physical limitations of the computers and systems that you and it allows you to deploy the analytics and storage capabilities of advanced machines that don't have to be your machine or your company's machine.

The cloud not only allows you to store large amounts of data on servers somewhere in the world, but it also allows you to deploy advanced computing algorithms and the ability to perform high-performance calculations using machines that are not your own.

As you have information that you can't store, you send it to the storage space or Cloud, and the algorithms that you need to use, you don't have them with you, but on the Cloud, you have those algorithms available.

What you do is you deploy these algorithms on very large datasets and you're able to do that even if your own systems, your own machines, your own computing environments wouldn't allow you to do that.

The other thing that the cloud is beautiful for is that it allows multiple entities to work with the same data at the same time, you can work with the same data as your colleagues.

Using the Cloud gives you instant access to open source technologies, without the need to install and configure them locally.

Using the cloud also gives you access to the latest tools and libraries without worrying about maintaining them and making sure they are up to date.

The cloud is accessible from anywhere and in any time zone.

You can use cloud technologies from your laptop, tablet, and even your phone making collaboration easier than ever.

Multiple collaborators or teams can access data simultaneously, working together to produce a solution.

Some major technology companies offer cloud platforms, allowing you to learn about cloud-based technologies in a predefined environment.

• IBM offers the IBM Cloud.
• Amazon offers Amazon Web Services or AWS.
• And Google offers the Google Cloud Platform.

The cloud significantly improves productivity for data scientists.

Cloud & Edge Computing

Artificial intelligence has the potential to alter our society, and it is currently being used in our daily lives, from Google searches to Amazon purchase recommendations and Netflix tailored recommendations, as well as in the security procedure for fraudulent credit card use. AI and machine learning are also the foundations for modern technological advancements with a rapidly growing worldwide market. AI enables robots to do a wide range of human-like tasks, such as seeing (facial recognition), writing (chatbots), and speaking (speech recognition) ( Alexa).

Cloud computing is the storage and processing of data and programs on a virtual area in a data center across a network, allowing businesses to store and process large volumes of data in real-time. The processing of data on devices such as smartphones is referred to as edge computing.

Cloud service providers like IBM, Amazon, Google, and Microsoft enable businesses to store all critical IT infrastructure in their clouds rather than within their digital walls, lowering the cost of maintaining and administering individual systems, software, and data. Edge computing, on the other hand, happens up close and personal on the front lines of corporate activities, rather than from afar in remote data centers. Rather than sending all data acquired by cameras, scanners, handhelds, or sensors to the cloud for processing, edge devices perform all or part of the processing locally, at the point of collection.

The 5G network

The fifth-generation cellular network technology, often known as 5G, promises faster, more dependable, and robust wireless networking, as well as a transition to constant communication between devices, allowing for richer and more diverse data streams.

Networking technology is one of the pillars of a smarter, more connected world, as improved bandwidth and coverage make more things possible, from email to web browsing to location-based services to video and gaming streaming. Furthermore, 5G promises quick data transfers of massive amounts of data, which will undoubtedly affect a wide number of sectors.

The 5G network will be able to connect devices in a geographic area, in addition to providing multi-gigabit per second peak data rates.

What are the best practices that should adopt by a computer programmer?

What is good practice for writing a relevant technical report?

Before starting the analysis, think about the structure of the report. The structure of the report depends on the length of the document. A short report is more relevant and presents a summary of the main findings. A detailed report builds the argument progressively and contains details of other relevant work, research methodology, data sources, intermediate conclusions, and key findings.

1. Cover page: should include the title of the report, authors' names, affiliations and contacts, institutional publisher, and date of publication.
2. Table of Contents: with main titles and lists of tables and figures, provides an overview of what is to come in the document.
3. The references.
4. The abstract: explains the main points of the argument in three paragraphs or less.
5. Introductory section: defines the issue for the reader who may be new to the topic and may need to be gently introduced to the subject before being immersed in complex details.
6. The methodology section: presents the research methods and data sources used for the analysis, the choices of variables, data, and methods, and how they will help answer the research questions.
7. The results section: is the place to present the findings.
8. The discussion section: is where to count the power of narrative to allow numbers to communicate the thesis to readers.
9. The conclusion section: is where to generalize the specific findings and take a rather marketing approach to promote your findings.
10. The Acknowledgement section: it is always good to acknowledge the support of those who made this work possible.