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 Methodology & Literacy
- Setting Up Your Accounts
- Software Development
- Mathematics for Machine Learning & Data Scientist
- Data Related Skills
- Foundational Machine Learning
- Deep Learning Foundations
- Machine Learning Operations & Practical Data Science
- Prepare For Interviews
- Closing Remarks
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.
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.
- What is Data Science? (1 day)
- Data Science Methodology (2 days )
- Data Science for Business Innovation (2 days)
- Write an article based on your learning
Before getting deep into practical and theoretical topics it is important to have your accounts ready. This includes your LinkedIn, medium, and GitHub accounts.
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):
- How to Make a Great Linkedin Profile — TIPS + EXAMPLES
- Create a professional LinkedIn account
- [Next Level GitHub Profile README (NEW 2022)(Markdown template) | Improve Your GitHub profiles (https://www.youtube.com/watch?v=G-EGDH50hGE)
- Create a professional GitHub account
- How To Create a Medium Account [Step by Step]
- Create a professional Medium account
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.
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.
- 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
- Python for Everyone Specialization (1 month )
- Software engineering for data scientists (1 week )
- Software Engineering Project With Python (1 week)
- Upload your project on GitHub
- Publish it on your social media channels
- Python for Data Science Roadmap
- Software Engineering for Data Scientist Roadmap
- Efficient Python for Data Scientists
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.
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.
- Mathematics for Machine Learning and Data Science Specialization Additional Resources:
- 6 Top University Courses to Learn Mathematics for Machine Learning & Data Science
- 6 Best Books to Learn Mathematics for Data Science & Machine Learning