Simple But Effective Free Roadmap to Start A Career in Data Science & AI In 2023



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Simple But Effective Free Roadmap to Start A Career in Data Science & AI In 2023

Whether you’re a recent graduate or a professional looking to make a career change, the field of Data Science and AI offers a wide range of exciting and lucrative opportunities. In this article, I provide you with a free guide that will provide you with a clear and actionable plan for building the skills and knowledge you need to succeed in this rapidly growing field. By following the steps outlined in this roadmap, you’ll be well on your way to a successful and rewarding career in Data Science & AI.

This roadmap will take you to an intermediate level, and I truly believe you can land a job and start your career after finishing it. However, to go to an advanced level, you will need to take more in-depth courses, books, and research papers. As you will see, most of the courses will be from Coursera. The reason for this is that I believe they have one of the best quality practical and in-depth courses, and at the same time, you can apply for financial aid if you can not pay for the courses, so you can take them for free.

I have written an estimated time for each of the learning paths. This time is in days and weeks, and I assumed that you would learn 4 days for five days a week. So in total of 20 hours per week. Depending on this, you can calculate the time needed to finish this roadmap depending on your pace.

Data Science Roadmap by Youssef Hosni

Table of Contents:

  1. Data Science Methodology & Literacy
  2. Setting Up Your Accounts
  3. Software Development
  4. Mathematics for Machine Learning & Data Scientist
  5. Data Related Skills
  6. Foundational Machine Learning
  7. Deep Learning Foundations
  8. Machine Learning Operations & Practical Data Science
  9. Prepare For Interviews
  10. Closing Remarks
    10.1. From Where To Learn?
    10.2. Developing Learning Habits
    10.3. Avoid Imposter Syndrome

If you would like to start a career in data science & AI and you do not know how. I offer data science mentoring sessions and long-term career mentoring:

Join the Medium membership program to continue learning without limits. I’ll receive a small portion of your membership fee if you use the following link, at no extra cost to you.

1. Data Science Methodology & Literacy

The first step in this roadmap is understanding the data science methodology and data literacy. In this step, you will understand what data science is and how to structure a data science project and what skills are required to succeed in this field.

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Data science methodology refers to the process or steps followed by data scientists to analyze and draw insights from data. This typically includes stages such as data exploration and cleaning, feature selection, model building and evaluation, and model deployment. Having a solid understanding of the data science process and methodology and what to expect in each step will help you structure your project properly and walk through it in a perfect way.

Data science literacy refers to the ability of an individual to understand and work with data science concepts and tools. These skills include knowledge of statistics, programming, and machine learning, as well as the ability to communicate and present data-driven insights to non-technical stakeholders. It is important to have a wide look at the important skills you need to gain to be successful in this field.

Learning Path (1 Week):

Additional Resources:

2. Setting up Your Accounts

Before getting deep into practical and theoretical topics it is important to have your accounts ready. This includes your LinkedIn, medium, and GitHub accounts.

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As you will see in the next steps, you will have to use the mentioned social and professional channels above. They are very important to have a professional account on these social media as they will present your work and will help you build a self-brand and credibility in this field.

In addition to that, you will be able to build very good connections, and you will also be able to follow the news and trends in this field from people in this field. Finally, you will have a professional two-way communication channel with others, whether recruiters will contact you or you can contact people in this field to ask for guidance or advice.

Learning Path (2 Days):

3. Software Development

Data scientists are software engineers, first and foremost. They may not be coding machine learning models or natural language processing algorithms on a day-to-day basis, but the work they do as data scientists requires software engineering and programming skills to be able to apply all the data science project life cycles on the data.

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Data scientists should also be able to understand users’ needs and develop solutions for those needs, which is essential for any data scientist working in an organization.

While you can get a job and make tremendous contributions with only machine learning modeling skills, your job opportunities will increase if you can write good software to implement complex AI systems.

These skills include:

  • Programming fundamentals
  • Data structures (especially those that relate to machine learning, such as data frames)
  • Algorithms (including those related to databases and data manipulation),
  • Software design
  • Python essential libraries include TensorFlow or Pytorch, Scikit-learn, Numpy, and Pandas.
  • Version Control

Learning Path (1.5 Month):

Additional Resources:

4. Mathematics for Machine Learning & Data Scientist

In the field of machine learning and data science, a strong foundation in mathematics is essential for understanding and implementing advanced algorithms. From linear algebra and multivariate calculus to probability and statistics, there are many different mathematical concepts that are important for success in these fields.

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Mathematics is the foundation of machine learning and deep learning algorithms, so it is important to have a strong mathematical background in these areas:

  • Linear algebra (vectors, matrices, and various manipulations of them)
  • Probability and statistics (including discrete and continuous probability, standard probability distributions, basic rules such as independence and Bayes’ rule, and hypothesis testing).
  • Basic intuitive understanding of calculus
  • In addition, exploratory data analysis (EDA) — using visualizations and other methods to systematically explore a dataset — is an underrated skill.

The math needed to do machine learning well has been changing. For instance, although some tasks require calculus, improved automatic differentiation software makes it possible to invent and implement new neural network architectures without doing any calculus. This was almost impossible a decade ago.

Learning Path: (1 Month )

Additional Resources:

5. Data-Related Skills

As a data scientist, you will spend most of your time using data. Therefore it is important to know how to use it effectively, starting from how to collect and store it, how to clean it, and explore it. Therefore having strong data skills will not also make you a better data scientist, but it will also make your life way much easier.

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Here are the basic data skills you should master as a data scientist:

  • Data collection
  • Data cleaning
  • Data exploration
  • Feature engineering
  • Data Visualization

Learning Path: (2 months )

Additional Resources:

6. Machine Learning

Machine learning is a subset of artificial intelligence that involves training models to make predictions or decisions without being explicitly programmed to do so. Data scientists often use machine learning techniques, such as supervised and unsupervised learning, to analyze and make predictions from data. This can include tasks such as classification, regression, clustering, and natural language processing.

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It’s important to have a very good understanding of basic models and machine learning models such as linear regression, logistic regression, neural networks, decision trees, clustering, and anomaly detection.

Beyond specific models, it’s even more important to understand the core concepts behind how and why machine learning works, such as bias/variance, cost functions, regularization, optimization algorithms, and error analysis.

Learning Path (3.5 Months ):

  • Machine Learning Specialization (2 Months )
  • Regression Project (2 Weeks)
  • Classification Project (2 Weeks)
  • Clustering Project (2 Weeks)
  • Upload the projects on GitHub
  • Publish the projects on your social media channels

You can read more about how to build a project that can land you a job in this article:

7. Deep Learning Foundations

Deep learning is a subfield of machine learning that involves training artificial neural networks with multiple layers to perform tasks such as image and speech recognition, natural language processing, and decision-making.

Data scientists can use deep learning techniques to improve the performance of their models and make predictions with a higher level of accuracy. This can be particularly useful when working with large and complex datasets that may be difficult to analyze using traditional machine-learning methods.

Photo by Pietro Jeng on Unsplash

Deep learning became such a large fraction of machine learning that it’s hard to excel in the field without some understanding of it! It’s valuable to know the basics of neural networks, practical skills for making them work (such as hyperparameter tuning), convolutional networks, sequence models, and transformers.

Learning Path (5 Months ):

8. Machine Learning Operations & Practical Data Science

In today’s hype of Machine learning where many organizations have integrated or are trying to integrate ML systems into their products and platforms. There are many challenges in bringing your machine learning systems into production, which include construction, integrating, testing, releasing, deployment, and infrastructure management.

Therefore it is important to follow good practices and know how to overcome these challenges. MLOps technologies are tools and platforms that help organizations manage and optimize machine learning models’ development, deployment, and maintenance.

MLOps, also known as Machine Learning Operations, is the practice of applying DevOps principles to machine learning. It is a set of practices that enables data scientists and machine learning engineers to collaborate, automate, and manage the end-to-end machine learning lifecycle. Some key MLOps practices for data scientists include:

  1. Version control: Using version control systems such as Git to track changes to the code, data, and models and collaborate with other team members.
  2. Automated testing: Using automated testing to ensure that the models and code are functioning correctly and to detect and fix bugs early in the development process.
  3. Continuous integration and deployment: Automating the build, test, and deployment of models so that they can be easily deployed to production.
  4. Monitoring and management: Using monitoring and management tools to keep track of the performance of the models in production and to identify issues that may need to be addressed.
  5. Experimentation management: Tracking and managing machine learning experiments and being able to reproduce the results for a better understanding of the models and their performance.

By implementing these practices, data scientists can ensure that their models are reliable, robust, and can be deployed to production quickly and efficiently.

Learning Path (3 Months) :

Additional Resources:

9. Prepare For Interviews

Now, after finishing your learning path, it is time to start applying for data science positions. To be able to succeed in this step, you need to focus on the basics, which are having a standing-out resume, portfolio, good connections, and a solid basic understanding of the important concepts that you are expected to be asked in a typical data science interview.

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Here are a few resources to help you to prepare your resume and portfolio and to know what to expect in a data science interview process and how to prepare for each of them.

  1. To understand what to expect in the data science hiring process, you can read this article:

2. To be able to write a Standing out Resume

3. How to build a standing-out portfolio

4. How to get an internal referral:

5. Prepare for the data science behavioral interview:

6. Prepare for the technical interview:

10. Closing Remarks

10.1. From Where To Learn?

There’s a lot of good content on the internet, and in theory, they could work. But when the goal is deep understanding, they will be inefficient because they tend to repeat each other, use inconsistent terminology (which slows you down), vary in quality, and leave gaps.

That’s why a good course in which a body of material has been organized into a coherent and logical form is often the most time-efficient way to master a meaningful body of knowledge. My recommendation is:

  • Solid textbooks
  • Coursera & Udacity
  • University open courses

When you’ve absorbed the knowledge available in courses, you can switch over to research papers and more advanced resources.

10.2. Developing Learning Habits

Given how quickly our field is changing, there’s little choice but to keep learning if you want to keep up. Developing a daily learning habit is a must to be able to keep up with the advances in the field. These three rules can be helpful:

  • Two-Day Rule: Whatever task you would like to work on in your daily system never allow yourself to skip more than one day in a row. Skipping one day will not hurt your progress as long as you do not skip the next day.
  • Minimum Viable Progress: Never skips a day, but any progress will count. Having a plan of working on a certain goal, for example, 3 hours per day; doing less than that will count. So the golden rule is 𝐀𝐧𝐲𝐭𝐡𝐢𝐧𝐠 𝐚𝐛𝐨𝐯𝐞 𝐳𝐞𝐫𝐨 𝐜𝐨𝐮𝐧𝐭𝐬.
  • 30-For-30 Approach: If you would like to work on a certain skill to improve, a very simple but effective approach is to do it for 30 minutes for 3o straight days. 30 days of 30 minutes per day is a total of 900 minutes of accumulated effort, which will yield very significant results.

More Information read:

10.3. Avoid Imposter Syndrome

Imposter syndrome can be a common experience for those working in the fields of artificial intelligence (AI) and data science, especially for those who are new to these fields or feel like they don’t have as much experience or knowledge as their peers.

This can lead to feelings of self-doubt and inadequacy, even when there is no evidence to support these feelings which might hurt your career progress, and affect your mental health in a serious way.

These tips include

  • Seeking out mentorship and guidance,
  • Building a strong foundation of knowledge
  • Seeking out opportunities to apply your skills
  • Remembering that everyone makes mistakes
  • Seeking out support from your peers and colleagues.

By following these tips, you can increase your confidence and feel more secure in your abilities as you pursue a career in AI and data science.

You can find more details about avoiding imposter syndrome in this article:

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