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A winning data analyst resume must be tailored to a particular job ad, brief, easy to scan, and mistake-free. At the same time, it has to showcase your qualifications and experience in a way that will compel the employer to call you in for the coveted data analyst interview.
So, how can you craft a data analyst resume that hits all of the above requirements?
For starters, and that is especially true for entry-level candidates, don’t fuss over consistency or details from the get-go. Just note down all relevant experiences that run through your mind – education, data analyst internships, job-specific skills you have mastered, data analyst projects and publications, and certificates under your belt. Once you list all the information you need on the page, you can start organizing the parts of your data analyst resume. If you lack plenty of years on the job, start with education. Then continue with relevant employment history, data analyst projects you’ve worked on, data analyst skills and certifications.
However, even if your resume is perfect content-wise, it can still end up in the “maybe later” pile if its formatting is less than impeccable.
The following data analyst resume example won’t let your data analyst resume go unnoticed. It will help you write a resume that not only demonstrates your skill set and expertise but will also make an instant great impression with appealing font, accurate spacing, and elegant look.
You can download this template easily and customize your resume in minutes!
Once you’re ready, all you have to do is pair it with your cover letter and submit your job application with confidence.
Just click on the button below and follow the instructions.
Your Name and Contact Information
Result-oriented individual with strong Business Intelligence and Analytics background. Seeking to utilize hands-on machine learning and data-driven experience as a Data Analyst at [Company Name]. Coming with expert knowledge of SQL, Tableau, Python, R, Probability, Statistics, Mathematics, and ability to work in a cross-functional team.
GISMA Business School (Berlin, Germany)
Master in Business Intelligence & Analytics (Apr 2019)
Completed Coursework: Advanced Data Modeling, Advance Data Discovery, Advanced Data Visualization and Advanced Qualitative & Quantitative Analytics
WHU – Otto Beisheim School of Management (Vallendar, Germany)
BA in International Business Administration (Jul 2017)
Human Resources Analytics/Predicting Employee Churn in Python
House Prices: Advanced Regression Techniques
Lufthansa, Germany (May 2019- July 2020)
Pricing operation assistant
Python | R | SQL | Excel | UML/ER modeling | Talend ETL | Tableau | Power BI | MicroStrategy | SPSS
SQL+Tableau+Python | 365 Data Science
Introduction to R Programming | 365 Data Science
Machine Learning in Python | 365 Data Science
Snooker | table tennis | badminton
English | German
How to Write a Data Science Resume – The Complete Guide (2021)
How to Write a Winning Data Science Cover Letter (2021)
How to Organize a Data Science Cover Letter
How to Format a Data Science Cover Letter
Data Science Cover Letter Dos and Don’ts
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Writing a successful data scientist resume is a skill in its own right. Data science is a highly competitive field and if you want to move your job application to the top of the pile, matching the keywords listed in your target data scientist job ad is just not enough.
A winning data scientist resume conveys that you’re the perfect fit – a data scientist with the exact mindset, education, and skills the employer needs.
To achieve that, you need a brief and straight-to-the-point resume that quotes explicit numbers and details to add credibility to your accomplishments. Moreover, you must also select strong action words that demonstrate you are a self-starter who gets things done.
However, none of that matters, if your resume fails to grab the attention of the employer at first glance. In fact, 15–30 seconds of consideration is all that you may get.
And this is where the power of consistent, elegant format, and clean looks come into play.
The following data scientist resume example will help you write a resume that emphasizes your skills and experience.
You can download this data scientist template easily and customize your resume in minutes!
Once you’re ready, all you have to do is pair it with your cover letter and submit your job application with confidence.
Just click on the button below and follow the instructions.
Your Name and Contact Information
A purpose-driven Data Scientist with deep analytical and quantitative expertise and MSc. (Hons.) in Economics. Seeking to leverage superior strategic and data mining skills to major company challenges at [Company Name]. Coming with strong programming skills and ability to build complex predictive models and machine-learning algorithms.
R | PYTHON | JAVA | SQL | NO SQL | AZURE ANALYTICS | POWER BI | TABLEAU |
PAYPAL
DATA SCIENTIST
MERCEDES BENZ RESEARCH AND DEVELOPMENT
DATA SCIENTIST
Birla Institute of Technology and Science
MSC (HONS.) ECONOMICS
B.E. (HONS.)., COMPUTER SCIENCE
365 DATA SCIENCE PROGRAM
ENGLISH | HINDI
How to Write a Data Science Resume – The Complete Guide (2021)
How to Write a Winning Data Science Cover Letter (2021)
How to Organize a Data Science Cover Letter
How to Format a Data Science Cover Letter
Data Science Cover Letter Dos and Don’ts
The post Data Scientist Resume Sample and Template appeared first on 365 Data Science.
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As per the latest reports, by end of 2020, the population of wild animals living on the Earth is expected to fall by two-thirds. Few folks don’t care about animals and don’t know how these natural habitants affect the life of other livening beings on this planet.
This continuous process will disturb the biodiversity of the Earth, and conserving the natural biodiversity of the planet is vital for the functioning of our natural ecosystems. Every animal, even a single plant or a small fungus is a part of a bigger system, and if they disappear, it will affect the parts of an ecosystem resulting in the instability eventually the collapse of the entire system.
Hence, saving the biodiversity of the earth is important to maintain the balance between the entire Ecosystem. But existing wildlife monitoring system is either incapable to scale globally, or don’t have the right resolutions or you can say the fine-scale data is often not within reach of the authorities.
As a conventional practice, researchers work tediously to complete manual tasks like identifying the specific animals from photoshoots for population studies. Later with more effort and spend time these camera photos are classified manually.
But now, thanks to advanced level technologies like Artificial Intelligence (AI) and Machine Learning (ML) such tasks can be performed more efficiently with better results. Yes, the fully-integration of AI & ML-based solution in wildlife conservation can help us to save the biodiversity of the earth.
How AI can Help in Saving the Biodiversity ?
Animal conservation is becoming one the key issue to save the biodiversity of the earth. And AI can play a vital role in detecting or recognizing and keeping the track of wild animals wandering into their natural environment or conserved into the wildlife sanctuaries.
Most importantly, AI can help in preventing the extinction of endangered plants and animals. And if such animals are kept under observation or tracked by the forest rangers, they can be saved from natural disasters such as fires in the forest, floods, and illegal activities like poaching.
And to conserve wild animals, AI-enabled devices, applications, and analysis or monitoring system is used to keep their track record and understand the behavior of animals for right predictions. Let’s find out how AI-enabled applications can be used for animal conservation.
Endangered species at the brink of extinction are kept in special conservation. AI-enabled machines like Machine Learning in Robotics or Drone Image Dataset can keep an eye on such animals helping the wildlife conservation authorities keeping their population under observation.
Similarly, computer vision technology in AI-enabled drones can detect the types and species of animals inform researchers about their activities. The machine learning algorithms developed with a wide-ranging huge quantity of training datasets equips AI to recognize the different species of animals.
Large animals like elephants and whales can be spotted from the satellites. And using the set of satellite imagery, researchers can gather the data and keep an eye on such animals. Animal detection and their counting are important to make sure if their population is increasing or decreasing.
Intensely killing the animals is another illicit activity, reducing the population of endangered species. Poachers kill animals like elephants for their precious tusks and Rhinoceros for their horns that are sold at very high rates in the international markets. But now AI can help in controlling such unlawful actions through a human-less monitoring system.
AI-enabled drones and night vision cameras can detect such poachers on the ground and report the forest rangers to take the action against them before they kill any animal. Humans with weapons and other unusual activities can be easily spotted by the AI-enabled cameras with quick alert systems.
The combination of the machine with humans working together with forest rangers can accomplish more such actions. And with intelligent visibility from the sky, there is a great opportunity for the wildlife animals. And to make the drones detect varied animals, a relevant amount of high-quality training datasets are required for training the machine learning algorithms.
People enjoying beach sides but litter waste material near the banks of the ocean, which is another risk for various species living or completely dependent on marine life. But now thanks to AI algorithms, models can easily identify and remove plastics from natural environments before they harm wildlife.
Drones are trained to identify the waste materials floating or drowning into the sea and inform the marine wildlife conservation department to collect and remove such waste materials. Marine litter mostly contains the plastic materials that have become a universal practice by the tourist and people enjoying their life around the ocean.
Plastic is harmful for living beings and carries invasive species posing a risk to biodiversity and ecosystems. Hence, an improved understanding of littering sources, the distribution, and the degradation of the plastic in oceans is necessary to reckon the risk related to plastic pollution.
Identifying and classifying the littered plastic waste in the ocean is a challenging task. But improved AI-enabled cameras equipped with drones, gathering marine litter information has become easier. The AI model is well-trained to recognize the varied types of waste materials littered into the ocean.
Reckoning the wild animals is another challenging task, especially when they are living into their natural environment. But thanks to AI, such animals can be easily counted without any human encounter to keep their population under observation.
Cogito provides Image Bounding Box Annotation to make such animals identifiable to machines like drones. All types of animals are annotated here with best level of accuracy for right detection.
Apart from counting, detecting the different types or species of animals is also the part of animal conservation. Here again Annotate Image Online can make such animals recognizable to machines (drones) and provide the information to forest rangers. Cogito use the right image annotation technique for animal detection with best level of accuracy.
Again, semantic segmentation is the image annotation technique helps to recognize the animals in the single class. AI Drones can recognize such animals captured in the single frame helping the forest animal’s conservation department to recognize animals. Cogito can produce the high-quality training data sets for machine learning to train the AI models developed for animal recognition.
Annotation for Species Identification
Identifying the different animal species is another challenging factor for wild animal conversation. But AI can easily detect the different species living on the earth or water. And Cogitotech provides the image annotation to annotate the animals with metadata if animal name or species. And when wide range of animal species can be identified with the AI model is trained with right training data.
The animals in the single class need to more precisely identified. And semantic segmentation image annotation is the best and one of the right technique, helps to identify such animals from distant location with extra precision. Cogito use the best tools and techniques annotate the animals in the images with semantic segmentation for deep learning AI models developed for animal conservation.
Animals at extinction levels can be protected if illegal killings can be spotted by the wildlife conservation department using the AI-enabled security surveillance installed at suspicious locations. Yes, poachers, even in the dark or night can be detected with AI security cameras.
Cogito provides image annotation for AI cameras, night vision view and object detection in the dark and nights. Image Annotation with added metadata is used to train the AI model that can detect the person and helping the animal conservation authorities to save the biodiversity of the earth.
Cogito provides image annotation for AI cameras, night vision view and object detection in the dark and nights. Image Annotation with added metadata is used to train the AI model that can detect the person and helping the animal conservation authorities to save the biodiversity of the earth.
If AI can be fully and efficiently deployed into the animal conservation it can help in saving the biodiversity of the earth. And it is only possible when the AI models are trained with the right machine learning datasets. And to develop such a fully functional model, AI companies need high-quality Animal Detection Dataset for Machine Learning training to identify animals and objects with the right precision.
Image annotation is the right data labeling process to generate the datasets for computer vision-based AI models. As much as such data is feed into the algorithms the model will be able to learn with a varied scenario and detect the different objects for giving the right predictions when used in real life.
Artificial Intelligence in Biodiversity: How AI can help in Animal Conservation? was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.
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