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10 Highest Paying Data Science Certifications You Should Know In 2023

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10 Highest Paying Data Science Certifications You Should Know In 2023

Top 10 Highest Paying Data Science Certifications You Should Be Aware Of in 2023

With the advancements in the IT sector, technologies like data science and artificial intelligence are growing. And the demand for data science jobs will increase and continue to grow in 2023 in all sectors. This is the right time for aspirants to look for data science certifications in 2023. As the top 3 jobs, with an average pay of $120,000 per year, Data science is unsurprisingly a challenging field. According to the glass door, there are over 14,000 Data science jobs in the US.

In this article, we have explained the 10 highest-paying data science certifications you should know in 2023 which will help you land your dream job in data science.

  1. Certified Analytics Professional (CAP)

The Certified Analytics Professional is the premier global professional certification which is one of the best certifications for those who are looking to work in data science and analytics. The CAP certification focuses on a particular technology and aims to equip individuals with analytical skills. If you meet high standards in CAP and pass the rigorous exam you are in for a treat because you will unlock doors for lucrative career opportunities. This CAP certification is specially designed for analytics professionals in their early- to mid-career.

  1. SAS-certified Data Scientist

The SAS-certified data scientist is a certification designed for individuals who can gain insights and manipulate big data. Now with a variety of open-source and SAS tools, you can make complex machine-learning models and deploy them using the flexible SAS environment.

  1. Proven Professional Certification Program from Dell EMC

The Dell EMC Data Scientist Associate is a foundation certification that exposes you to data analytic basics and big data. As you pass your data scientist certification you are considered a proven professional.

  1. Azure Data Scientist Associate by Microsoft

Next on the list is the Azure Data Scientist Associate certification by Microsoft. Here azure data scientist applies their knowledge of artificial intelligence and data science to run and implement machine learning workloads on the Azure Cloud Platform.

  1. CCA Data Analyst

The data analyst certification shows that you can prepare, structure and analyse data in Cloudera’s CDH environment. You can be able to do things like import data from MySQL into Hadoop, you can create tables and alter them, you can also make reports with select and join queries.

  1. Cloudera Certified Professional (CCP)

The CCP data engineer certificate certifies that you can develop autonomous, reliable, and scalable data pipelines that result in optimised data sets for a variety of workloads. Meaning, as a CCP data engineer you can demonstrate that they can wrangle data into a clean, useful shape that can be used for various kinds of purposes and by different people.

  1. Data Analytics Certification from Amazon AWS

The AWS Certified Data Analytics – Specialty exam is intended for individuals who perform in a focused role in data analytics. The exam validates a comprehensive understanding of the examinee of using AWS services to design, build, maintain and secure analytics solutions that provide data from insight.

  1. Data Engineer Certification from Google

With Cloud Data Engineering certification from google, you can show the world that you are an expert in building data-driven solutions using google cloud. As today’s companies are collecting lots of data the need for skilled professionals who can build solutions in the cloud has increased. And this is the best way you can prove yourself with Cloud Data Engineering certification.

  1. Open Certified Data Scientist

Certification from the open group certified data scientist has been developed to verify people who have data scientist qualities and skills required to produce an effective analysis of data for improving the overall business.

  1. IBM Data Science Professional Certificate

The IBM data science professional certificate is designed to be accessible to everyone who has a passion for data science. This is one of the best data science certificates and a solid choice for beginners to develop essential skills with certification. You will learn Python, data analysis, SQL, visualization, machine learning, statistics, and more in this.

The post 10 Highest Paying Data Science Certifications You Should Know In 2023 appeared first on Analytics Insight.

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Top 10 Data Science Slack Communities To Join In The Year 2023

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Top 10 Data Science Slack Communities To Join In The Year 2023

Take your journey to the next level by joining these top Data Science Slack communities in 2023

Data science Slack communities act as a community that inspires thousands of people and aims to support student growth and entrepreneurial abilities. Taking part in a community is a fantastic way to learn. Particular attention in this article is given to Slack communities. Slack is a team collaboration tool that facilitates communication and teamwork. To stay up with the newest discussions on data science, we have compiled our top data science Slack communities for you to check out.

Let us discuss some of the data science Slack communities to join in the year 2023.

  1. Datatalks.Club

It is everything data, as the name implies. This may come from machine learning, data science, or data analytics. There are several Slack channels, including #ai-memes-for-ai-peeps, #book-of-the-week, #career, #datascience, #events, and more. There are free weekly events you can attend as well as a podcast with up to 12 seasons.

  1. Data Reliability Engineering Community

This Slack channel is more narrowly focused on a particular Data Science issue. Many different data engineers and scientist network and discuss in-depth issues with data dependability and the best methods for solving them. This will be a helpful slack channel if you wish to focus on this area of data science or need further guidance.

  1. DataScientists

A group that lectures about data science, data warehousing, business intelligence-related subjects, and other things. By networking with others in the industry, you may both learn from each other’s and your failures.

  1. AI-ML-Data Science Lovers

The AI-ML-Data Science Lovers slack group is for you if you’re searching for something a little more relaxed and peaceful. There are many people in this group talking informally about artificial intelligence, machine learning, and data science.

It is a great method to stay informed about other people’s viewpoints and broaden your knowledge.

  1. Papers with Code

Papers with Code is a free and open-source website that offers papers, code, datasets, algorithms, and assessment charts related to machine learning. You will have access to excellent materials through the community that will aid your study. You will progress from studying Data Science theory to using and refining your abilities.

  1. KaggleNoobs

You must develop your coding abilities if you want to succeed as a data scientist. You can only evaluate your talents through tasks. Kaggle will become your closest buddy in the beginning. It will be wise to join the Kaggle community to get assistance with unresolved issues and advice on specific topics.

  1. Data Science Salon

A team of senior data scientists, machine learning engineers, and other professionals make up the eclectic community that is the Data Science Salon, a unique gathering. They want to connect IT experts so they may network, develop, and learn from one another about potential new approaches.

  1. Open Data Science Community

a group that concentrates on all things Data Science. The top Data Science publications, tutorials that will accelerate your learning, code sharing, and general guidance will all be made available to you. aimed at bringing together data science experts from across the globe.

  1. Data with Danny

Here, you may complete difficult tasks as part of a unique data apprenticeship while learning data analytics, data science, and machine learning. Danny Ma, a well-known data science specialist, started this group. On this channel, you may discuss any data-related subject and, more importantly, you can ask Danny any questions.

  1. Riga DS Club

Riga Data Science Club is what Its stands for. It is a non-profit group that brings people together to construct machine-learning projects by exchanging ideas and experiences. Its objective is to establish a thriving data science community in Latvia that may have a beneficial influence on the future.

Disclaimer: The information provided in this article is solely the author’s opinion and not investment advice – it is provided for educational purposes only. By using this, you agree that the information does not constitute any investment or financial instructions. Do conduct your own research and reach out to financial advisors before making any investment decisions.

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Top 10 Data Science Programming Languages You Should Know In 2023

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Top 10 Data Science Programming Languages You Should Know In 2023

The Top 10 data science programming languages you should know in 2023

Data science has become an increasingly popular field in recent years, and as a result, there has been a growing demand for skilled data scientists. To be a successful data scientist, you need to have a solid understanding of the various programming languages used in the field. In this article, we will be discussing the top 10 programming languages that you should know if you are interested in pursuing a career in data science in 2023.

Python

Python is the most popular programming language used in data science, and it’s not hard to see why Python is easy to learn and use, making it a great choice for beginners. It also has a large and active community, which means that there are many resources available for those who want to learn more about the language. Additionally, Python has a vast array of libraries and frameworks that make it easy to perform complex data analysis tasks.

R

R is another programming language that is commonly used in data science. Like Python, R is open-source, which means that it is free to use and has a large community of developers. R is particularly useful for data visualization, and it has a number of powerful libraries for visualizing and analyzing data. R is also highly extensible, which makes it possible to add new functionalities to the language as needed.

SQL

SQL is a relational database management system that is widely used in data science. It is used to manage and analyze large amounts of data, and it is an essential tool for data scientists who work with structured data. SQL is also used to extract and manipulate data from databases, making it an important tool for data analysis.

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Julia

Julia is a newer programming language that is quickly gaining popularity in the data science community. Julia is designed to be fast and efficient, which makes it a great choice for data science tasks that require high performance. Additionally, Julia has a number of libraries and tools that make it easy to perform complex data analysis tasks.

Scala

Scala is a functional programming language that is used in data science. Scala is particularly useful for big data processing, and it has a number of libraries and tools that make it easy to perform complex data analysis tasks. Scala is also known for its high performance, making it a great choice for data science tasks that require fast processing times.

MATLAB

MATLAB is a numerical computing environment that is widely used in data science. MATLAB is used for data analysis and visualization, and it is particularly useful for tasks that require complex mathematical calculations. MATLAB also has a large and active community, which means that there are many resources available for those who want to learn more about the language.

SAS

SAS is a proprietary software suite that is widely used in data science. SAS is used for data analysis and visualization, and it is particularly useful for tasks that require complex statistical analysis. SAS is also widely used in the business world, making it an important tool for data scientists who work in the business sector.

Java

Java is a widely used programming language that is used in data science. Java is particularly useful for data science tasks that require large-scale data processing, and it has a number of libraries and tools that make it easy to perform complex data analysis tasks. Java is also widely used in the business world, making it an important tool for data scientists who work in the business sector.

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Kotlin

Kotlin is a programming language that is used in data science. Kotlin is particularly useful for data science tasks that require fast and efficient data processing,

JavaScript

JavaScript is a widely used programming language in the field of data science. It offers powerful libraries for data visualization, web scraping, data processing, machine learning, data analytics, real-time data processing, and data integration. JavaScript’s versatility and ease of use make it a valuable tool for data scientists.

The post Top 10 Data Science Programming Languages You Should Know in 2023 appeared first on Analytics Insight.

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Top 10 Data Science Prerequisites You Should Know In 2023

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Top 10 Data Science Prerequisites You Should Know In 2023

Data science paves an enticing career path for students and existing professionals. Be it product development, improving customer retention, or mining through data to find new business opportunities, organizations are extensively relying on data scientists to sustain, grow, and stay one step ahead of the competition. This throws light on the growing demand for data scientists. If you, too, are aspiring to become a successful data scientist, you have landed at the right place for we will talk about the top 10 data science prerequisites you should know in 2023. Have a look!

Statistics

As a matter of fact, data science has a lot to do with data. In such a case, statistics turn out to be a blessing. This is for the sole reason that statistics help to dig deeper into data and gain valuable insights from them. The reality is – the more statistics you know, the more you will be able to analyze and quantify the uncertainty in a dataset.

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Understanding analytical tools

Yet another important prerequisite for data science is to have a fair understanding of analytical tools. This is because a data scientist can extract valuable information from an organized data set via analytical tools. Some popular data analytical tools that you can get your hands on are – SAS, Hadoop, Spark, Hive, Pig, and R.

Programming

Data scientists are involved in procuring, cleaning, munging, and organizing data. For all of these tasks, programming comes in handy.  Statistical programming languages such as R and Python serve the purpose here. If you want to excel as a data scientist, make sure that you are well-versed in Python and R.

Machine learning (ML)

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Data scientists are entrusted with yet another important business task – identifying business problems and turning them into Machine Learning tasks. When you receive datasets, you are required to use your Machine Learning skills to feed the algorithms with data. ML will process these data in real time via data-driven models and efficient algorithms.

Apache Spark

Apache Spark is just the right computation framework you need when it comes to running complicated algorithms faster. With this framework, you can save time a lot of time while processing a big sea of data. In addition to that, it also helps Data Scientists handle large, unstructured, and complex data sets in the best possible manner.

Data Visualization

Yet another important prerequisite for data science that cannot go unnoticed is data visualization, a representation of data visually, through graphs and charts. As a data scientist, you should be able to represent data graphically, using charts, graphs, maps, etc. The extensive amount of data generated each day is the very reason why we require data visualization.

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Communication skills

The fact that communication skill is one of the most important non-technical skill that one should possess, no matter what the job role is, goes without saying. Even in the case of data science, communication turns out to be an important prerequisite. This is because data scientists are required to clearly translate technical findings to the other non-technical teams like Sales, Operations or Marketing Departments. They should also be able to provide meaningful insights, hence enabling the business to make wiser decisions.

Excel

Excel is one tool that is extremely important to understand, manipulate, analyze and visualize data, hence a prerequisite for data science. With Excel, it is quite easy to proceed with manipulations and computations that have to be done on the data. Having sound Excel knowledge will definitely help you become a successful data scientist.

Teamwork

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No matter how critical or simple the task is, one should always be good at teamwork. In the case of data science too, teamwork would take you to heights.

The post Top 10 Data Science Prerequisites You Should Know in 2023 appeared first on Analytics Insight.

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