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Top 10 Data Science Jobs This Week

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Top 10 Data Science Jobs This Week

This list of the top 10 data science jobs will help you start your career in the emerging field.

Data science is a rapidly expanding profession with numerous intriguing job prospects for anyone with the necessary skills and expertise. There are numerous possibilities available to you whether you are just starting out in your profession or seeking a change. In this article, we’ll highlight the top 10 data science jobs of the week, so you can see what’s available and find the perfect opportunity for you.

Data Science Manager at Deloitte (Hyderabad)

The candidate should assist in the management of large-scale data science initiatives that make use of data transformation and machine learning models. To design first-rate tools and insights for leadership, and balance the complexity of data, coding/visualization platforms, and client demands. Increase efficiencies and scalability by automating and streamlining tasks.

Data Scientist, Business and Marketing at Google (Gurgaon)

Responsibilities -To ensure that efforts are focused on providing significant and practical outcomes, apply critical thinking and issue statement definition, deconstruction, and problem-solving. Adopt and create data engineering approaches such as data source and feature identification and integration, data pipelining, feature engineering, data munging, and analysis using script/code-driven methods that can be translated from research to production.

Data Analyst at Capgemini (Bengaluru)

The Customer Remediation Analytics team is searching for new team members to help with analytics tasks such as customer remediation population identification, remediation execution, exploratory analytics, and transactional testing. As a team member, you will be responsible for supporting customer remediation analytics execution activities and pre-execution transactional testing.

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Data Scientist, Business and Marketing at Google (Bengaluru)

Roles and responsibilities – Adopt and create data engineering approaches such as data source and feature identification and integration, data pipelining, feature engineering, data munging, and analysis using script/code-driven methods that can be translated from research to production. Using proper mathematical methods, natural language processing (NLP), and semantic analysis, analyse and create explanatory, predictive, and prescriptive models.

Machine Learning Engineer at Tech Mahindra (Pune)

The skill set of an ML Engineer: A candidate with Python/PySpark experience is required, as is experienced in constructing and maintaining data/ML pipelines using AWS Sagemaker. Experience in building ML models using Sagemaker is required, as is experienced with AWS Personalize and AWS EventBridge.

Data Scientist at Razorpay (Bengaluru)

They are searching for a motivated Data Scientist to join their expanding team. The new hire will be responsible for collaborating with other data scientists and engineers across the firm to develop production-quality models for a wide range of Razorpay problems.

Data Analyst, Trust and Safety at Google (Hyderabad)

The candidate’s role and responsibilities include performing complex data analysis to gain insight from massive data stores, collaborating with cross-functional groups such as engineering, sales, and policy to increase automation, updating policies, fixing product vulnerabilities, and providing a secure online experience for users.

Data Scientist at Infosys (Bengaluru)

The following abilities are required of the candidate: Programming languages include Python, R, SQL, ML methods, ML for Responsible AI, AI domains such as NLP, voice, computer vision, structured data, and learning patterns such as supervised, unsupervised, and reinforcement learning. Tools for data analysis, machine learning, model deployment and scalability Dataset knowledge, pre-built models available in the open community, and third-party providers.

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Data Scientist at Fractal (Bengaluru)

Responsibilities – Ability to understand a problem description and independently execute analytical solutions and methodologies with independent/proactively/thought leadership. Collaborate with stakeholders across the organisation to find opportunities for using company/client data to create business solutions. Create high-quality solutions and insightful analyses by conceptualising, designing, and delivering them.

Data Analyst at IndusInd Bank (Gurugram)

The individual would work on cutting-edge problems for the bank as part of the asset analytics and data science team. The individual will collaborate closely with stakeholders from risk, business, partnerships, and digital strategies to develop and refine campaign tactics to boost the bank’s profitability and growth.

Overall, these are just some of the many data science job openings available this week.

The post Top 10 Data Science Jobs This Week 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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