Data Science Engineer Resume | Data Scientist Resume | Data Science Resume Tips | Intellipaat

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In this Data Science Engineer Resume video, you will learn what makes a good resume, career in data science, building a data science resume, spell check and proof reading customizing and updating a resume, data science sample resumes, Do's and Don't's of data science resume. This session is a must-watch for everyone who wishes to learn data science and make a career in it.

1. 01 What makes a Good Resume? 04 02 Career in Data Science ? 03 05 6 Building a Data Science Resume Customizing and Updating your Resume Spell Check and Proofreading 07 Data Science Resume Samples Do’s and Don’ts of a Data Science Resume

2. What makes a Good Resume?

3. What makes a good resume? To make a good resume: Pick the right format and template Proofread and Spellcheck to avoid grammatical and spelling errors . Keep it up-to-date and relevant to the job

4. What makes a good resume? To make a good resume: List your educational qualifications Briefly describe previous work experiences Mention your skills relevant to the job

5. Career in Data Science

6. What is Data Science? The simplest answer: Making data of value and of use to us, and applying it to the real world and practical scenarios is called data science. It is the process of obtaining information and insight from data. It is one of the most promising careers in today’s world!

7. Job Roles in Data Science Data Analysts They analyze large amounts of data, to decipher its meaning and understand trends. Skills – Mathematics, Statistics, Computer Science etc. Business Intelligence Analysts BI Analysts use data analysis to transform data into insights that improve business value . Skills – Statistics, SQL, C#, Python etc.

8. Job Roles in Data Science Data Mining Engineers They mine hidden information from large amounts of data and suggest how this information can provide value to the organization. Skills – Java, PERL, Hadoop, Python, SQL etc. Data Architects They design secure electronic databases for storing and organizing data, by understand the organization’s existing data infrastructure. Skills – ML, NLP, Applied Math, Statistics, etc.

9. B uilding a Data Science Resume

10. Keeping it short L ist down everything that you want to mention. The key is to make everything concise and to-the-point. C hoose the projects, certifications that are most relevant to data science. It is very important to prioritize .

11. Selecting a Resume Template Working on the visual appeal of the resume makes it unique and your resume stands out from the crowd. Pick a resume template according to your own aesthetic preference. Pick a design template that is appealing and unique, but yet simple and makes the content easily readable. Using too many colors would do more harm than good .

12. Profile and Contact Information Mention your email address, contact number and links to your LinkedIn and GitHub profiles. Make sure all your data science related projects are present on your GitHub account. Write a brief description about yourself, in 2-3 sentences that tells about your skills, interests and qualification.

13. Writing your skills Skills should always be mentioned in order of relevance to the job role you’re applying for .

14. Be it data mining or running embedded systems, python can do everything. The python library used for data analysis is Pandas. SQL Databases Python Programming R Programming SQL is used to manage and query data that is held in a relational database system. It is used to read, retrieve, update or insert data R Programming implements ML algorithms to give us many statistical techniques. It is used for calculations and data manipulation. Skills for a Data Science Resume

15. Data Visualization Machine Learning Business Strategy Artificial Intelligence Skills for a Data Science Resume They should understand business problems and provide solutions. They use data in a way that is helpful to the company. A data scientist should be able to represent data graphically. Visualization makes sense of the large amount of data. ML analyzes data using algorithms and automates a data scientist’s jobs. Should be familiar with NLP & Recommendation engines

16. Projects, Publications and Accomplishments Since you’re seeking a role in the data science industry, mentioning publications, projects and achievements related to data science is very important .

17. Analytical and Communication Skills I t is very important to justify your analytical and communication capabilities. Leadership and communication skills can be highlighted by mentioning projects and experiences of working in a team setting, on collaborative projects .

18. Work Experience and Education Explain your role and responsibilities in each organization and the period of time you worked there for. After work experience, mention your educational background and names of institutions you studied in.

19. Selecting font style and arrangement Pick a font style and organize every mentioned in a neat manner. It is best to mention projects, accomplishments , employment history and certifications in a reverse chronological manner .

20. Proofreading and Spellcheck

21. Proofreading and Spellcheck It is always very important to check for spelling and grammatical errors. Have someone go through your resume to suggest changes or point out any errors.

22. Customizing and Updating your resume

23. Customizing and Updating Always customize your resume for every job profile and company.

24. Data Science Resume Samples

25. Data Science Resume for a Fresher For a fresher, building a strong resume mentioning all your skills is very important. It is also crucial to make the resume look unique and neat.

26. Data Science Resume for Experienced Professionals For data science professionals with some experience in the field, mentioning relevant work experience and responsibilities at each job role is crucial.

27. Do’s and Don’ts of a Data Science Resume

28. Do’s and Don’ts Include updated contact information. For each work experience, include a brief summary about your role and responsibility in the organization. While writing educational qualification, always mention the name of the institute , and the time period you studied there for . Include relevant project work. Make your resume look unique. Don’ts Don’t include date of birth or religious beliefs. Don’t use vague descriptions for jobs and projects. Mention exactly what you did, and what impact it had. Don’t list technologies and programming languages that you’re not proficient in. Over exaggerated abilities will get you rejected in the interview. Don’t make any grammatical errors. Don’t make your resume too generic . Customize it as per job roles . Do’s Finally, now that you have learnt everything you need to know to build yourself the perfect data scientist resume, one that makes your application stand out, let’s go over a few things you should do, and mistakes you should not make .

29. Final Thoughts

30. Final Thoughts It is very important for everyone to make their resume stand out from the crowd, in order to get a job in the data science industry. Recruiters get thousands of applications and resumes, Don’t give them any reasons to reject your application and remember to pay attention to small details too.

31. Further Learning..

32. Further Learning

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