With a little experience, you can land a job in data science

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Although the job market in data science requires skill and experience, lack of experience or even a lack of it does not prevent you from getting a data science job. How is that done? This is what we will discuss in this article

It is noticeable in recent years the great interest in the development of data science of all kinds, such as big data generated by smart devices and the diversity of computer resources such as cloud computing. On the other hand, the development of algorithms has received a great deal of attention.

In addition, the diversity of the fields of the labor market in data science, which includes health, transportation, and industry sectors, in addition to academic, environmental, security, and other activities.

And with the diversity of areas that branch out from data science, such as data analysis, predictive analysis, machine learning, deep learning, data visualization, and other branches.

All these factors have led to an increased demand for data scientists, who have a variety of fields of employment, with a variety of available opportunities, including:

Data Scientist, Data Analyst, Predictive Analyst, Business Analyst, AI Writer, Data Visualizer, Data Engineer

So we are going to give you a set of tips that will help you get a job in data science

1. Learn key skills:

It is necessary to learn the basic principles of data science by following good-level online training courses, and it is preferable to obtain a degree in a university, and these skills include:

Problem Solving, Decision Making, Programming (Python or R), Statistics, Mathematics (Linear Algebra and Calculus), Machine Learning, Deep Learning, Data Visualization, Report Writing

Mastering these skills will increase your chance of getting a job in data science

2. Learn about data science libraries:

The most famous of these libraries:

NumPy, Pandas, Matplotlib, Seaborn, Scikit-Learn, Tensorflow, Keras

And other libraries that must be recognized

3. Stay up-to-date with developments and developments:

One may think that once he gets the job, he no longer needs to keep up with new developments and technologies in this field, but this view is wrong par excellence. Staying abreast of developments in data science increases the skills and experience of the learner because forgetting or interrupting learning is the first enemy of progress and distinction.

4. Specialization in a specific field:

Especially for those who do not have the comprehensive experience that qualifies them to get a job in data science. Therefore, expanding the mastery of a specific field in one of the sub-fields is considered an effective weapon in the hands of its bearer, as is the case in mastering machine learning or deep learning.

5. Self-training on practical experiences:

This advice is specifically directed at learning and developing machine learning algorithms. After the learning stage comes the stage of being able to write code that leads to algorithmic outputs that produce real data, and this will pave the way for you to be able to modify codes, produce new outputs, and make comparisons and analyzes.

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6. Take notes

Recording notes and all the experiences you have learned will help you to retrieve information when you need to refer to it, and with the passage of time it will form a blog that you can benefit from in the future so that you can build your own brand.

7. Follow online training courses

It is widely available on the Internet, but be sure to follow the reliable courses in terms of information led by trainers with scientific weight in this field

Start by learning the principles of data science, machine learning, deep learning, and other technologies

And I recommend courses offered by famous platforms such as Coursera, as they offer scientific degrees in cooperation with the best universities in the world, and it is not necessary to apply for paid courses in order for the novice learner to start developing his skills, as the free courses are sufficient for such cases

8. Support your CV with a professional certificate

In continuation of what was stated in the previous paragraph, you can obtain a certificate after you have followed a paid course. This certificate is considered an official document indicating your level of experience and skill.

9. Create a community of data scientists

It is one of the things that increase your chances of being accepted into a job in data science

The following platforms are fertile environments for building a community of data scientists

LinkedIn: A scientific community is built by creating and sharing data science posts on the platform

Medium: Through it, you can create a blog related to data science and build an information network

Kaggle: Through it, you can participate in data science competitions and build a network

10. Completion of projects in accordance with the requirements of the potential job

You must complete projects related to the field of work that you prefer to apply for in the potential job, for example, if you prefer to apply for a job in the field of data visualization, you must implement projects related to data visualization

11. Start your career at a low job level

As working at low job levels does not require you to have a lot of sufficient experience as a beginner in the job, and with the acquisition of more experience, you can search for a higher-level job, but the right start for the inexperienced starts from a mini-work environment

12. Build a distinguished resume

Building a distinguished CV reflects a positive impression on decision makers in employment matters, and thus will support your chances of getting a job.

And we can call the characteristic of excellence on the resume if it has the factors we mentioned in a previous article, you can view them by reading the article in detail from here How to write a killer resume and ace the interview

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ุจู‚ู„ูŠู„ ู…ู† ุงู„ุฎุจุฑุฉ ูŠู…ูƒู†ูƒ ุฃู† ุชุญุตู„ ุนู„ู‰ ูˆุธูŠูุฉ ููŠ ุนู„ูˆู… ุงู„ุจูŠุงู†ุงุช

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ุฑุบู… ุฃู† ุณูˆู‚ ุงู„ุนู…ู„ ููŠ ุนู„ู… ุงู„ุจูŠุงู†ุงุช ูŠุชุทู„ุจ ุงู„ู…ู‡ุงุฑุฉ ูˆุงู„ุฎุจุฑุฉ ุฅู„ุง ุฃู† ู‚ู„ุฉ ุงู„ุฎุจุฑุฉ ุฃูˆ ุญุชู‰ ุงู†ุนุฏุงู…ู‡ุง ู„ุง ูŠู…ู†ุน ู…ู† ุฃู† ุชุญุตู„ ุนู„ู‰ ูˆุธูŠูุฉ ุนู„ู… ุงู„ุจูŠุงู†ุงุช ูƒูŠู ูŠุชู… ุฐู„ูƒุŸ ู‡ุฐุง ู…ุง ุณู†ู†ุงู‚ุดู‡ ููŠ ู‡ุฐุง ุงู„ู…ู‚ุงู„

ู…ู† ุงู„ู…ู„ุงุญุธ ููŠ ุงู„ุณู†ูˆุงุช ุงู„ุฃุฎูŠุฑุฉ ุงู„ุงู‡ุชู…ุงู… ุงู„ูƒุจูŠุฑ ุจุชุทูˆูŠุฑ ุนู„ูˆู… ุงู„ุจูŠุงู†ุงุช ุจุฃู†ูˆุงุนู‡ุง ูƒุงู„ุจูŠุงู†ุงุช ุงู„ุถุฎู…ุฉ ุงู„ู…ุชูˆู„ุฏุฉ ุนู† ุทุฑูŠู‚ ุงู„ุฃุฌู‡ุฒุฉ ุงู„ุฐูƒูŠุฉ ูˆุชู†ูˆุน ุงู„ู…ูˆุงุฑุฏ ุงู„ุญุงุณูˆุจูŠุฉ ูƒุงู„ุญูˆุณุจุฉ ุงู„ุณุญุงุจูŠุฉุŒ ูˆู…ู† ุฌุงู†ุจ ุขุฎุฑ ู†ุงู„ ุชุทูˆูŠุฑ ุงู„ุฎูˆุงุฑุฒู…ูŠุงุช ุญูŠุฒุงู‹ ูƒุจูŠุฑุงู‹ ู…ู† ุงู„ุงู‡ุชู…ุงู…ุŒ ูˆุงู„ุฌุงู†ุจ ุงู„ุฃูƒุซุฑ ุฃู‡ู…ูŠุฉ ู…ู† ุฐู„ูƒ ุฃู† ู…ุฌุงู„ุงุช ุงู„ุนู…ู„ ููŠ ุนู„ู… ุงู„ุจูŠุงู†ุงุช ุฐุงุช ู…ุตุฏุฑ ู…ูุชูˆุญ   

ุนู„ุงูˆุฉ ุนู„ู‰ ุฐู„ูƒ ุชู†ูˆุน ู…ุฌุงู„ุงุช ุณูˆู‚ ุงู„ุนู…ู„ ููŠ ุนู„ู… ุงู„ุจูŠุงู†ุงุช ูˆุงู„ุชูŠ ุชุดู…ู„ ู‚ุทุงุนุงุช ุงู„ุตุญุฉ ูˆุงู„ู†ู‚ู„ ูˆุงู„ุตู†ุงุนุฉ ุฅุถุงูุฉ ุฅู„ู‰ ุงู„ู†ุดุงุทุงุช ุงู„ุฃูƒุงุฏูŠู…ูŠุฉ ูˆุงู„ุจูŠุฆูŠุฉ ูˆุงู„ุฃู…ู†ูŠุฉ ูˆุบูŠุฑู‡ุง ู…ู† ุงู„ูุนุงู„ูŠุงุช ุงู„ุฃุฎุฑู‰  

ูˆู…ุน ุชู†ูˆุน ุงู„ู…ุฌุงู„ุงุช ุงู„ุชูŠ ุชุชูุฑุน ุนู† ุนู„ู… ุงู„ุจูŠุงู†ุงุช ูƒุชุญู„ูŠู„ ุงู„ุจูŠุงู†ุงุช ูˆุงู„ุชุญู„ูŠู„ ุงู„ุชู†ุจุคูŠ ูˆุงู„ุชุนู„ู… ุงู„ุขู„ูŠ ูˆุงู„ุชุนู„ู… ุงู„ุนู…ูŠู‚ ูˆุชุตูˆุฑ ุงู„ุจูŠุงู†ุงุช ูˆุบูŠุฑู‡ุง ู…ู† ุงู„ูุฑูˆุน ุงู„ุฃุฎุฑู‰  

ูƒู„ ู‡ุฐู‡ ุงู„ุนูˆุงู…ู„ ุฃุฏุช ุฅู„ู‰ ุชุฒุงูŠุฏ ุงู„ุทู„ุจ ุนู„ู‰ ุนู„ู…ุงุก ุงู„ุจูŠุงู†ุงุช ุงู„ุฐูŠู† ุชู†ูˆุนุช ุฃู…ุงู…ู‡ู… ู…ุฌุงู„ุงุช ุงู„ุชูˆุธูŠู ู…ุน ุชู†ูˆุน ุงู„ูุฑุต ุงู„ู…ุชุงุญุฉ ูˆุงู„ุชูŠ ู†ุนุฏุฏ ู…ู†ู‡ุง

ุนุงู„ู… ุจูŠุงู†ุงุชุŒ ู…ุญู„ู„ ุจูŠุงู†ุงุชุŒ ู…ุญู„ู„ ุชู†ุจุคูŠุŒ ู…ุญู„ู„ ุงู„ุฃุนู…ุงู„ุŒ ูƒุงุชุจ ุงู„ุฐูƒุงุก ุงู„ุงุตุทู†ุงุนูŠุŒ ู…ุตูˆุฑ ุงู„ุจูŠุงู†ุงุชุŒ ู…ู‡ู†ุฏุณ ุจูŠุงู†ุงุช

ู„ุฐุง ุณู†ุชู‚ุฏู… ู„ูƒ ู…ุฌู…ูˆุนุฉ ู†ุตุงุฆุญ ุชุณุงุนุฏูƒ ุนู„ู‰ ุงู„ุญุตูˆู„ ุนู„ู‰ ูˆุธูŠูุฉ ููŠ ุนู„ู… ุงู„ุจูŠุงู†ุงุช

1- : ุชุนู„ู… ุงู„ู…ู‡ุงุฑุงุช ุงู„ุฑุฆูŠุณูŠุฉ

ู…ู† ุงู„ุถุฑูˆุฑูŠ ุชุนู„ู… ุงู„ู…ุจุงุฏุฆ ุงู„ุฃุณุงุณูŠุฉ ู„ุนู„ู… ุงู„ุจูŠุงู†ุงุช ูˆุฐู„ูƒ ุนู† ุทุฑูŠู‚ ู…ุชุงุจุนุฉ ุฏูˆุฑุงุช ุชุฏุฑูŠุจูŠุฉ ุฐุงุช ู…ุณุชูˆู‰ ุฌูŠุฏ ุนุจุฑ ุงู„ุฅู†ุชุฑู†ุช ูƒู…ุง ูˆูŠูุถู„ ุงู„ุญุตูˆู„ ุนู„ู‰ ุดู‡ุงุฏุฉ ููŠ ุฅุญุฏู‰ ุงู„ุฌุงู…ุนุงุช ูˆุชุดู…ู„ ู‡ุฐู‡ ุงู„ู…ู‡ุงุฑุงุช

(R ุญู„ ุงู„ู…ุดูƒู„ุงุชุŒ ุตู†ุน ุงู„ู‚ุฑุงุฑุŒ ุงู„ุจุฑู…ุฌุฉ ( ุจุงูŠุซูˆู† ุฃูˆ

ุงู„ุฅุญุตุงุกุŒ ุงู„ุฑูŠุงุถูŠุงุช (ุงู„ุฌุจุฑ ุงู„ุฎุทูŠ ูˆุญุณุงุจ ุงู„ุชูุงุถู„ ูˆุงู„ุชูƒุงู…ู„)ุŒ ุงู„ุชุนู„ู… ุงู„ุขู„ูŠุŒ ุงู„ุชุนู„ู… ุงู„ุนู…ูŠู‚ุŒ ุชุตูˆุฑ ุงู„ุจูŠุงู†ุงุชุŒ ูƒุชุงุจุฉ ุงู„ุชู‚ุงุฑูŠุฑ

ุฅุชู‚ุงู† ู‡ุฐู‡ ุงู„ู…ู‡ุงุฑุงุช ุณูŠุฒูŠุฏ ูุฑุตุชูƒ ููŠ ุงู„ุญุตูˆู„ ุนู„ู‰ ูˆุธูŠูุฉ ููŠ ุนู„ู… ุงู„ุจูŠุงู†ุงุช

2- :ุงู„ุชุนุฑู ุนู„ู‰ ู…ูƒุชุจุงุช ุนู„ูˆู… ุงู„ุจูŠุงู†ุงุช

:ูˆุฃุดู‡ุฑ ู‡ุฐู‡ ุงู„ู…ูƒุชุจุงุช

NumPy, Pandas, Matplotlib, Seaborn, Scikit-Learn, Tensorflow, Keras

ูˆุบูŠุฑู‡ุง ู…ู† ุงู„ู…ูƒุชุจุงุช ุงู„ุชูŠ ู„ุงุจุฏ ู…ู† ุงู„ุชุนุฑู ุนู„ูŠู‡ุง

3- :ุงู„ุงุทู„ุงุน ุนู„ู‰ ุงู„ู…ุณุชุฌุฏุงุช ูˆุงู„ุชุทูˆุฑุงุช ุจุดูƒู„ ู…ุณุชู…ุฑ

ู‚ุฏ ูŠุนุชู‚ุฏ ุงู„ู…ุฑุก ุฃู†ู‡ ุจู…ุฌุฑุฏ ุญุตูˆู„ู‡ ุนู„ู‰ ุงู„ูˆุธูŠูุฉ ูุฅู†ู‡ ู„ู… ูŠุนุฏ ุจุญุงุฌุฉ ุฅู„ู‰ ู…ูˆุงูƒุจุฉ ุงู„ุชุทูˆุฑุงุช ูˆุงู„ุชู‚ู†ูŠุงุช ุงู„ุฌุฏูŠุฏุฉ ููŠ ู‡ุฐุง ุงู„ู…ุฌุงู„ ุฅู„ุง ุฃู† ู‡ุฐู‡ ุงู„ู†ุธุฑุฉ ุฎุงุทุฆุฉ ุจุงู…ุชูŠุงุฒ ูุงู„ุจู‚ุงุก ุนู„ู‰ ุงุทู„ุงุน ุฏุงุฆู… ุนู„ู‰ ุชุทูˆุฑุงุช ุนู„ู… ุงู„ุจูŠุงู†ุงุช ูŠุฒูŠุฏ ุงู„ู…ู‡ุงุฑุงุช ูˆุงู„ุฎุจุฑุงุช ุนู†ุฏ ุงู„ู…ุชุนู„ู… ู„ุฃู† ุงู„ู†ุณูŠุงู† ุฃูˆ ุงู„ุงู†ู‚ุทุงุน ุนู† ุงู„ุชุนู„ู… ู‡ูˆ ุงู„ุนุฏูˆ ุงู„ุฃูˆู„ ู„ู„ุชู‚ุฏู… ูˆุงู„ุชู…ูŠุฒ

4- :ุงู„ุชุฎุตุต ููŠ ู…ุฌุงู„ ู…ุนูŠู†

ูˆุฎุตูˆุตุงู‹ ู„ู„ุฐูŠู† ู„ุง ูŠู…ุชู„ูƒูˆู† ุงู„ุฎุจุฑุฉ ุงู„ุดุงู…ู„ุฉ ุงู„ุชูŠ ุชุคู‡ู„ู‡ู… ู„ู„ุญุตูˆู„ ุนู„ู‰ ุงู„ูˆุธูŠูุฉ ููŠ ุนู„ู… ุงู„ุจูŠุงู†ุงุช ู„ุฐุง ูุงู„ุชูˆุณุน ููŠ ุฅุชู‚ุงู† ู…ุฌุงู„ ู…ุนูŠู† ููŠ ุฅุญุฏู‰ ุงู„ู…ุฌุงู„ุงุช ุงู„ูุฑุนูŠุฉ ูŠุนุชุจุฑ ุณู„ุงุญ ูุนุงู„ ุจูŠุฏ ุญุงู…ู„ู‡ ูƒู…ุง ู‡ูˆ ุงู„ุญุงู„ ููŠ ุฅุชู‚ุงู† ุงู„ุชุนู„ู… ุงู„ุขู„ูŠ ุฃูˆ ุงู„ุชุนู„ู… ุงู„ุนู…ูŠู‚

5-:ุงู„ุชุฏุฑุจ ุงู„ุฐุงุชูŠ ุนู„ู‰ ุงู„ุฎุจุฑุงุช ุงู„ุนู…ู„ูŠุฉ

ูˆู‡ุฐู‡ ุงู„ู†ุตูŠุญุฉ ู…ูˆุฌู‡ุฉ ุฎุตูŠุตุงู‹ ู„ุชุนู„ู… ูˆุชุทูˆูŠุฑ ุฎูˆุงุฑุฒู…ูŠุงุช ุงู„ุชุนู„ู… ุงู„ุขู„ูŠ ูุจุนุฏ ู…ุฑุญู„ุฉ ุงู„ุชุนู„ู… ุชุฃุชูŠ ู…ุฑุญู„ุฉ ุงู„ู‚ุฏุฑุฉ ุนู„ู‰ ูƒุชุงุจุฉ ุงู„ูƒูˆุฏุงุช ุงู„ุจุฑู…ุฌูŠุฉ ุงู„ู…ุคุฏูŠุฉ ุฅู„ู‰ ู…ุฎุฑุฌุงุช ุฎูˆุงุฑุฒู…ูŠุงุช ุชู†ุชุฌ ุจูŠุงู†ุงุช ุญู‚ูŠู‚ูŠุฉุŒ ูˆู‡ุฐุง ุณูŠู…ู‡ุฏ ุฃู…ุงู…ูƒ ุงู„ุทุฑูŠู‚ ู„ุชุตุจุญ ู‚ุงุฏุฑุงู‹ ุนู„ู‰ ุชุนุฏูŠู„ ุงู„ูƒูˆุฏุงุช ูˆุฅู†ุชุงุฌ ู…ุฎุฑุฌุงุช ุฌุฏูŠุฏุฉ ูˆุฅุฌุฑุงุก ุงู„ู…ู‚ุงุฑู†ุงุช ูˆุงู„ุชุญู„ูŠู„ุงุช

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6- :ุชุฏูˆูŠู† ุงู„ู…ู„ุงุญุธุงุช

ุชุณุฌูŠู„ ุงู„ู…ู„ุงุญุธุงุช ูˆูƒู„ ู…ุง ุชุนู„ู…ุชู‡ ู…ู† ุฎุจุฑุงุช ุณูŠุนูŠู†ูƒ ุนู„ู‰ ุงุณุชุนุงุฏุฉ ุงู„ู…ุนู„ูˆู…ุงุช ุนู†ุฏ ุงู„ุญุงุฌุฉ ุฅู„ู‰ ุงู„ุฑุฌูˆุน ุฅู„ูŠู‡ุง ูˆุณูŠุดูƒู„ ู…ุน ู…ุฑูˆุฑ ุงู„ุฒู…ู† ู…ุฏูˆู†ุฉ ูŠู…ูƒู†ูƒ ุงู„ุงุณุชูุงุฏุฉ ู…ู†ู‡ุง ู…ุณุชู‚ุจู„ุงู‹ ุจุญูŠุซ ุชุจู†ูŠ ู„ูƒ ุนู„ุงู…ุฉ ุชุฌุงุฑูŠุฉ ุฎุงุตุฉ ุจูƒ

7-:ู…ุชุงุจุนุฉ ุฏูˆุฑุงุช ุชุฏุฑูŠุจูŠุฉ ุนุจุฑ ุงู„ุฅู†ุชุฑู†ุช

ูˆู‡ูŠ ู…ุชูˆูุฑุฉ ุจุดูƒู„ ูƒุจูŠุฑ ุนู„ู‰ ุดุจูƒุฉ ุงู„ุฅู†ุชุฑู†ุช ูˆู„ูƒู† ุงุญุฑุต ุนู„ู‰ ุงุชุจุงุน ุงู„ุฏูˆุฑุงุช ุงู„ู…ูˆุซูˆู‚ุฉ ู…ู† ุญูŠุซ ุงู„ู…ุนู„ูˆู…ุงุช ูŠู‚ูˆุฏู‡ุง ู…ุฏุฑุจูˆู† ูŠุชู…ุชุนูˆู† ุจุซู‚ู„ ุนู„ู…ูŠ ููŠ ู‡ุฐุง ุงู„ู…ุฌุงู„

ุงุจุฏุฃ ู…ู† ุชุนู„ู… ู…ุจุงุฏุฆ ุนู„ูˆู… ุงู„ุจูŠุงู†ุงุช ูˆุงู„ุชุนู„ู… ุงู„ุขู„ูŠ ูˆุงู„ุชุนู„ู… ุงู„ุนู…ูŠู‚ ูˆุบูŠุฑู‡ุง ู…ู† ุงู„ุชู‚ู†ูŠุงุช

Coursera ูˆุฃูˆุตูŠ ุจุฏูˆุฑุงุช ุชู‚ุฏู…ู‡ุง ู…ู†ุตุงุช ุดู‡ูŠุฑุฉ ู…ุซู„

ูู‡ูŠ ุชู‚ุฏู… ุดู‡ุงุฏุงุช ุนู„ู…ูŠุฉ ุจุงู„ุชุนุงูˆู† ู…ุน ุฃูุถู„ ุงู„ุฌุงู…ุนุงุช ููŠ ุงู„ุนุงู„ู…ุŒ ูˆู„ุง ูŠุดุชุฑุท ุงู„ุชู‚ุฏู… ุฅู„ู‰ ุงู„ุฏูˆุฑุงุช ุงู„ู…ุฏููˆุนุฉ ู„ูƒูŠ ูŠุจุฏุฃ ุงู„ู…ุชุนู„ู… ุงู„ู…ุจุชุฏุฆ ุจุชุทูˆูŠุฑ ู…ู‡ุงุฑุงุชู‡ ูุงู„ุฏูˆุฑุงุช ุงู„ู…ุฌุงู†ูŠุฉ ุชููŠ ุจุงู„ุบุฑุถ ู„ู…ุซู„ ู‡ุฐู‡ ุงู„ุญุงู„ุงุช

8- :ุงุฏุนู… ุณูŠุฑุชูƒ ุงู„ุฐุงุชูŠุฉ ุจุดู‡ุงุฏุฉ ู…ู‡ู†ูŠุฉ

ูˆุงุณุชุทุฑุงุฏุงู‹ ู„ู…ุง ูˆุฑุฏ ููŠ ุงู„ูู‚ุฑุฉ ุงู„ุณุงุจู‚ุฉ ูŠู…ูƒู†ูƒ ุงู„ุญุตูˆู„ ุนู„ู‰ ุดู‡ุงุฏุฉ ุจุนุฏ ุงุชุจุงุนูƒ ู„ุฏูˆุฑุฉ ู…ุฏููˆุนุฉ ูˆุชุนุชุจุฑ ู‡ุฐู‡ ุงู„ุดู‡ุงุฏุฉ ูˆุซูŠู‚ุฉ ุฑุณู…ูŠุฉ ุชุฏู„ ุนู„ู‰ ู…ุณุชูˆู‰ ุฎุจุฑุชูƒ ูˆู…ู‡ุงุฑุชูƒ ย 

9- :ุฅู†ุดุงุก ู…ุฌุชู…ุน ุนู„ู…ุงุก ุงู„ุจูŠุงู†ุงุช

ูˆู‡ูŠ ู…ู† ุงู„ุฃู…ูˆุฑ ุงู„ุชูŠ ุชุฑูุน ู…ู† ุญุธูˆุธูƒ ููŠ ุงู„ู‚ุจูˆู„ ููŠ ูˆุธูŠูุฉ ููŠ ุนู„ู… ุงู„ุจูŠุงู†ุงุช

ูˆุชุนุชุจุฑ ุงู„ู…ู†ุตุงุช ุงู„ุฃุณุงุณูŠุฉ ุงู„ุชุงู„ูŠุฉ ุจูŠุฆุงุช ุฎุตุจุฉ ู„ุจู†ุงุก ู…ุฌุชู…ุน ูŠุถู… ุนู„ู…ุงุก ุงู„ุจูŠุงู†ุงุช

: LinkedIn

ูŠุชู… ุจู†ุงุก ู…ุฌุชู…ุน ุนู„ู…ูŠ ุนู† ุทุฑูŠู‚ ุฅู†ุดุงุก ู…ู†ุดูˆุฑุงุช ุนู„ูˆู… ุงู„ุจูŠุงู†ุงุช ูˆู…ุดุงุฑูƒุชู‡ุง ุนู„ู‰ ุงู„ู…ู†ุตุฉ ย 

: Medium

ูˆู…ู† ุฎู„ุงู„ู‡ุง ูŠู…ูƒู†ูƒ ุฅู†ุดุงุก ู…ุฏูˆู†ุฉ ุชุชุนู„ู‚ ุจุนู„ู… ุงู„ุจูŠุงู†ุงุช ูˆุจู†ุงุก ุดุจูƒุฉ ู…ุนู„ูˆู…ุงุช

: Kaggle

ูˆู…ู† ุฎู„ุงู„ู‡ุง ูŠู…ูƒู†ูƒ ุงู„ู…ุดุงุฑูƒุฉ ููŠ ู…ุณุงุจู‚ุงุช ุนู„ูˆู… ุงู„ุจูŠุงู†ุงุช ูˆุจู†ุงุก ุดุจูƒุฉย 

10- :ุฅู†ุฌุงุฒ ู…ุดุงุฑูŠุน ูˆูู‚ ู…ุชุทู„ุจุงุช ุงู„ูˆุธูŠูุฉ ุงู„ู…ุญุชู…ู„ุฉ

ุนู„ูŠูƒ ุฅู†ุฌุงุฒ ู…ุดุงุฑูŠุน ุชุชุนู„ู‚ ุจู…ุฌุงู„ ุงู„ุนู…ู„ ุงู„ุฐูŠ ุชูุถู„ ุงู„ุชู‚ุฏู… ุฅู„ูŠู‡ ููŠ ุงู„ูˆุธูŠูุฉ ุงู„ู…ุญุชู…ู„ุฉุŒ ู…ุซู„ุงู‹ ุฅู† ูƒู†ุช ุชูุถู„ ุงู„ุชู‚ุฏู… ุนู„ู‰ ูˆุธูŠูุฉ ููŠ ู…ุฌุงู„ ุชุตูˆุฑ ุงู„ุจูŠุงู†ุงุช ููŠุฌุจ ุนู„ูŠูƒ ุชู†ููŠุฐ ู…ุดุงุฑูŠุน ุชุชุนู„ู‚ ุจุชุตูˆุฑ ุงู„ุจูŠุงู†ุงุช

11- :ุงุจุฏุฃ ู…ุณูŠุฑุชูƒ ุงู„ูˆุธูŠููŠุฉ ุจู…ุณุชูˆู‰ ูˆุธูŠููŠ ู…ู†ุฎูุถ

ุฅุฐ ุฃู† ุงู„ุนู…ู„ ููŠ ู…ุณุชูˆูŠุงุช ูˆุธูŠููŠุฉ ู…ู†ุฎูุถุฉ ู„ุง ูŠุญุชุงุฌ ู…ู†ูƒ ุงู„ูƒุซูŠุฑ ู…ู† ุงู„ุฎุจุฑุฉ ุงู„ูƒุงููŠุฉ ูƒู…ุจุชุฏุฆ ููŠ ุงู„ูˆุธูŠูุฉุŒ ูˆูŠู…ูƒู†ูƒ ู…ุน ุงูƒุชุณุงุจ ู…ุฒูŠุฏ ู…ู† ุงู„ุฎุจุฑุงุช ุฃู† ุชุจุญุซ ุนู† ูˆุธูŠูุฉ ุฐุงุช ู…ุณุชูˆู‰ ุฃุนู„ู‰ ู„ูƒู† ุงู„ุจุฏุงูŠุฉ ุงู„ุตุญูŠุญุฉ ุจุงู„ู†ุณุจุฉ ู„ู‚ู„ูŠู„ูŠ ุงู„ุฎุจุฑุฉ ุชู†ุทู„ู‚ ู…ู† ุจูŠุฆุฉ ุนู…ู„ ู…ุตุบุฑุฉ

12- :ุจู†ุงุก ุณูŠุฑุฉ ุฐุงุชูŠุฉ ู…ุชู…ูŠุฒุฉ

ุจู†ุงุคูƒ ู„ุณูŠุฑุฉ ุฐุงุชูŠุฉ ู…ุชู…ูŠุฒุฉ ูŠุนูƒุณ ุงู†ุทุจุงุนุงู‹ ุฅูŠุฌุงุจูŠุงู‹ ู„ุฏู‰ ุฃุตุญุงุจ ุงู„ู‚ุฑุงุฑ ููŠ ุฃู…ูˆุฑ ุงู„ุชูˆุธูŠู ูˆุจุงู„ุชุงู„ูŠ ุณุชุฏุนู… ุญุธูˆุธูƒ ููŠ ุงู„ุญุตูˆู„ ุงู„ูˆุธูŠูุฉ

ูˆูŠู…ูƒู† ุฃู† ู†ุทู„ู‚ ุตูุฉ ุงู„ุชู…ูŠุฒ ุนู„ู‰ ุงู„ุณูŠุฑุฉ ุงู„ุฐุงุชูŠุฉ ุฅุฐุง ุชูˆูุฑุช ููŠู‡ุง ุนูˆุงู…ู„ ุฐูƒุฑู†ุงู‡ุง ููŠ ู…ู‚ุงู„ ุณุงุจู‚ ูŠู…ูƒู†ูƒ ุงู„ุงุทู„ุงุน ุนู„ูŠู‡ุง ู…ู† ุฎู„ุงู„ ู‚ุฑุงุกุฉ ุงู„ู…ู‚ุงู„ ุจุดูƒู„ ู…ูุตู„ ู…ู† ู‡ู†ุง

How to write a killer resume and ace the interview

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