Data science: Your 2025 guide from the basics to Saudi Arabia's labor market

Thinking of entering the field of data science? Start your journey here

You may hear the term "Data Science" all around you, and be curious about this field that is being touted as the "oil of the future." You may wonder: What is data science really? How does it differ from data analytics or big data? Most importantly, you might be thinking about your future career and asking: Is this field right for me and is it really in demand in the Saudi labor market?

If these questions are on your mind, you're in the right place. We understand that the mystery around this field can be overwhelming, and that scattered information can make it difficult to get started.

For this reason, we did not prepare this article to be a superficial definition, but rather A comprehensive and practical guide tailor-made for you in Saudi Arabia.

As you read through this guide, you'll get clear answers to:

• Understand the core concept of data science and how it differs from other fields.
• Recognize the strategic importance of data science in achieving Saudi Vision 2030 and its applications in projects such as NEOM and Aramco.
• Discover the technical and soft skills required to assess your suitability for the field.
• Get Practical Roadmap Step by step, it starts from scratch and gets you to the level of readiness for the labor market.
• Know the reality of the Saudi labor market: What jobs are available, what is the demand and the expected salary level.

By the end of this article, you will not only have a clear idea of what data science is, but you will have the knowledge and confidence to decide your next step in this promising field within the Kingdom.

What is data science? A beginner's guide to understanding Saudi Arabia's "Future Oil"

The concept of data science: Simplifying the world's most in-demand field

Very simply. Data science is an interdisciplinary field It uses scientific methods, algorithms, and systems to extract knowledge and insights from data, whether structured (such as Excel spreadsheets) or unstructured (such as text, images, and video).

It is not one thing, but a magical combination of several fields of knowledge:

1. Statistics and Math: This is the theoretical foundation that allows us to understand the data and build logical models.
2. Computer Science & Programming: These are the tools (such as Python) that give us the ability to handle massive amounts of data and apply complex algorithms.
3. Domain Expertise: That's the context. A data scientist working at a bank needs to understand the financial sector, and a data scientist working at an energy company needs to understand petroleum engineering. It is this expertise that helps them ask the right questions.

The ultimate goal of data science is not just to "know" what happened in the past, but to "Predict what will happen in the future, and "Prescribe with the best course of action to take based on those predictions.

How does data science support the pillars of Saudi Vision 2030?

You can't talk about data science in Saudi Arabia without directly linking it to Saudi Vision 2030. This ambitious vision, which aims to diversify the economy and build a vibrant society, relies at its core on digital transformation, and data is the fuel for this transformation.

Data science plays a pivotal role in every pillar of the vision:

• Prosperous economy: Instead of relying entirely on oil, the kingdom is using data science to develop new sectors. In Tourismis used to analyze tourist behavior and personalize offers. And in Financial sectoris used to create secure digital banking services.
• Vital community: To improve "quality of life," data science is used in Digital Healthcare to predict disease outbreaks and improve hospital management. And in Educationto analyze student performance and develop customized curricula.
• Ambitious homeland: Megaprojects such as "NEOM and "The Line They are not just construction projects, they are primarily "data" projects. These smart cities will rely entirely on data science and the Internet of Things (IoT) to manage everything: From autonomous transportation, to clean energy grids, to ultra-efficient public services.

Saudi Data and Artificial Intelligence Authority (SDAIA) The National Strategy for Data and Artificial Intelligence (NSDAI) is leading the way, making the Kingdom a global center in this field.

untangling: Data Science, Data Analytics, and Big Data

It's very easy to confuse these terms. Let's clarify the essential differences:

• Big Data: That's it "Raw Resource" or "the Problem". It refers to the massive amounts of data that are flowing so fast (Velocity), in such huge volumes (Volume), and in such a variety of formats (Variety) that traditional programs cannot process them. Big data is the challenge and resource that data scientists are working on.
• Data Analysis: That's it "Part" of data science that focuses on the past and present. A data analyst uses tools like SQL, Excel, and Power BI to answer questions like: "What happened?" (e.g. How much did we sell last month?) and "Why did it happen?" (Example: Why did sales drop in the Eastern region?). His work is centered on Descriptive and Diagnostic analysis.
• Data Science: That's it "The Wider Field" It encompasses everything a data analyst does, but adds the future to it. A data scientist builds statistical models and Machine Learning algorithms to answer questions like: "What's going to happen?" (Predictive Analysis) and "What should we do about it?" (Prescriptive analysis).

For a clearer understanding, a data analyst gives you a report showing yesterday's sales patterns, while a data scientist builds a system that predicts tomorrow's sales and automatically suggests the best price to maximize profits.

[comparison table] Data Scientist or Data Analyst? Explain the differences and career path

To help you decide which path is right for you, here's a table that summarizes the key differences between the two most in-demand jobs in the industry:

The lifecycle of a data science project: 5 stages of turning raw data into profits

Data science projects are not random, but follow a systematic, structured process known as the Data Science Lifecycle. This cycle ensures that chaos (raw data) is transformed into tangible value (profits and decisions). It can be summarized in 5 main phases:

Stage 1: Identify the issue (the right question is half the answer)

This is the most important and difficult stage, which is not technical but "Business" phase With excellence. Failure here means you're building a great model that answers a question no one cares about.

The story begins when a department (such as marketing) comes up with a vague goal, such as: "We want to increase customer loyalty." The data scientist's role here is to Collaboration with Stakeholders (Stakeholders) and translate this vague goal into a specific, precise, and measurable data science question, such as:

"Can we build a model that predicts which customers have a 90% probability of leaving our service (Churn) in the next 30 days, and what are the top 3 factors that drive them?"

At this stage, the Define success metrics (Success Metrics.) How will we know the project is successful? Is it achieving 95% forecasting accuracy or reducing the dropout rate by 10%? Defining this goal from the beginning ensures that everyone is on the same page.

Stage 2: Data Processing (the art of cleaning up crude "oil")

This is where the real hard work begins. Industry estimates indicate This stage can take up to 60-80% from project time. Real-world data is always messy, incomplete, and unclean. This stage includes several sub-steps:

1. Data Collection: Pulling data from various sources, which could be databases (via SQL), APIs, text files, or server logs.
2. Data Cleaning: This is the most painful part. It involves dealing with Missing Valuesand delete Duplicatescorrecting spelling mistakes (such as "Riyadh" and "Riyadh"), dealing with Outliers that might spoil the model.
3. Data Transformation & Feature Engineering: This is the touch of art. "Feature engineering" means using your expertise and creativity to create new "features" (columns) from existing data to make the model smarter. For example, instead of just using a customer's "date of birth", you can engineer a new feature called "age group" or "Is it their birthday this month?" (to send a promotional offer).

Phase 3: Modeling and Analysis (the beating heart of data science)

Now that the data is clean and ready, we move on to the fun part.

This phase begins with Exploratory Data Analysis (EDA). Here, the data scientist uses visualization tools to understand patterns and relationships within the data. For example, drawing a relationship between a customer's age and how much they buy.

Next comes the step of Model Selection. Based on the question we identified in the first stage (Is it predicting a number? Or categorizing something?), the scientist chooses the appropriate algorithm. Is it a Linear Regression algorithm, a Decision Tree, or a Neural Network?

Finally, the Model Training By "feeding" the model with clean data (training data) and allowing it to "learn" the patterns in it.

Stage 4: Evaluation and verification (checking the accuracy of the results)

You can't trust the model once it's built. It needs to be tested to make sure it will work well with new data it hasn't seen before.

This is done by splitting the original data into two parts: Training Data (which the model sees and learns from, about 70-80%), and Test Data (which remains hidden from the model, 20-30%).

After training the model on the "training data", we ask it to predict the results of the "test data", and then compare its predictions to the actual correct results. Here we use Evaluation Metrics such as Accuracy, Precision, and Recall.

If the results are poor, the data scientist goes back to Stage 2 (maybe he needs better data or different feature engineering) or Stage 3 (maybe he needs to try a different algorithm). This is an iterative process.

Stage 5: Storytelling (turning numbers into actionable decisions)

A great 99% model is worthless if it remains locked in the laptop of the data scientist, and is not understood by the marketing manager or CEO.

This stage is about Effective Communication and Data Storytelling.

The data scientist uses tools to Data Visualization such as Power BI or Tableau to create clear and compelling graphs that explain the results in simple "business" language.

The goal is not to explain "how" the model works (complex technical aspects), but to explain "What?" This model means "actionable insights" for the company.

Example: "Our model found that the top reason customers leave our service is poor customer service over the phone. If we improve our response time by 20%, we expect a decrease in customer churn by 15%, saving 3 million Riyals per year."

The final step is Deployment (Deployment)This model is integrated into the company's live systems to make predictions automatically and continuously.

How data science is changing our lives in Saudi Arabia (real-life examples)

Data science may seem like a theoretical field, but you're actually using its applications every day, perhaps without even realizing it. The Saudi market, thanks to massive government and private investment, is full of them.

From recommendations to predictions: Applications of data science in your day

1. Recommendation Engines: When you open the YouTube or Netflix app, the videos and movies suggested to you are not random. They are the result of complex data science algorithms that analyze your viewing history and those of millions of similar users to suggest what you might like.
2. E-commerce: When apps like Noon or Amazon suggest products you "might also be interested in," that's data science.
3. Google Maps: When the app tells you that it will take 30 minutes to get to work due to "unexpected congestion," it uses machine learning models that analyze live traffic data and historical data to accurately predict the time.
4. Your voice assistant: Whether you use Siri or Google Assistant, their ability to understand your accent (even Saudi) and turn your speech into commands is a direct application of Natural Language Processing (NLP), a fundamental branch of data science.

Securing the banking sector: The role of data science in fraud detection

Saudi Arabia's banking and financial sector (under the supervision of Saudi Central Bank - SAMA) is one of the biggest adopters of data science technologies, mainly because Security and Risk Management.

• Real-time Fraud Detection: When you use your bank card, a machine learning model analyzes the transaction in milliseconds. This model compares the transaction (amount, location, time, store type) to your normal purchasing behavior. If it suspects anything (e.g. a purchase from another country you haven't traveled to), the transaction is immediately blocked and an alert is sent to you.
• Anti-Money Laundering (AML): Banks use complex models to monitor billions of transactions and identify suspicious patterns that may indicate money laundering.
• Credit Scoring: Data science models are used to assess your eligibility for a loan or credit card, based on your financial history and other factors, speeding up the approval process.

Driving the future: Data science in the NEOM and Aramco projects

This is where the real weight of data science in the Saudi economy comes into play, where the traditional energy giant meets the giant of the future.

• Aramco: Some might think that a traditional energy company doesn't need data science, but the opposite is true. Aramco utilizes data science with tremendous intensity in:
  • Predictive Maintenance: Instead of waiting for a pump or rig to fail, machine learning models analyze IoT sensor data to predict "when" the device will fail Before occurring, saving billions of riyals in maintenance and downtime costs.
  • Reservoir Simulation: Using complex models to simulate underground oil fields, which helps determine the best places to drill and maximize extraction efficiency.
  • Optimize supply chains: Efficiently manage the movement of VLCCs around the world based on demand, weather, and costs.
• NEOM and smart cities: NEOM is not just a city, it's a living data platform. Data science is key that The Line will be based on.
  • Self-transportation: Flying taxis (drones) and high-speed trains will be run entirely by AI systems.
  • Clean energy management: NEOM will rely on the world's largest green hydrogen plant. Data science will balance energy production (from the sun and wind) with the city's consumption in real time.
  • Personalized healthcare: Your health data will be analyzed (with your consent) to provide you with personalized preventive and predictive healthcare.

Do you have the "mindset" of a data scientist? (Test your suitability for the field)

Entering the field of data science requires a unique combination of hard technical skills and soft soft soft skills. Before you start the journey, it helps to know if this field suits your way of thinking.

Hard Skills: The indispensable tools

These are skills you can learn through courses, books, and practice.

1. Programming: Python is currently the dominant language in data science, thanks to its powerful libraries such as Pandas (to process tables). NumPy (for arithmetic operations), and Scikit-learn (for machine learning). language R It is also powerful, especially in purely academic and statistical circles.
2. Databases: SQL language It is an indispensable skill. You can't analyze data if you can't extract it. You must master commands such as SELECT, FROM, WHERE, GROUP BYand JOIN.
3. Statistics & Math: You don't need to be a math professor, but you do need a strong understanding of basic concepts such as probability, linear algebra, and descriptive and inferential statistics. This understanding is what makes you know "why" to choose a particular model and what its results mean.
4. Machine Learning: Understand key algorithms, and the difference between supervised learning (e.g. predicting the price of a house) and unsupervised learning (e.g. customer segmentation).
5. Data Visualization: Ability to use tools such as Power BI (highly sought after in the Saudi market) or Tableauor Python libraries like Matplotlib and Seaborn to turn numbers into understandable graphs.

Soft Skills: Just as important as code

These are the skills that distinguish a successful data scientist from a mere programmer.

1. Curiosity: A true data scientist doesn't stop at the first answer. He's always asking: "Why?". "Why are sales down?", "What if we change this factor?". Curiosity is what leads you to discover deep insights.
2. Problem-Solving skills: Your job is to solve complex puzzles. You must possess an organized mindset capable of breaking down a large business issue into small pieces that can be solved with data.
3. Communication & Storytelling: As mentioned, this is a critical skill. The ability to explain complex technical results to a CFO or marketing team in a simple, compelling and actionable way.
4. Business Acumen: You need to understand "how" a company makes money. What are the company's strategic goals? How will your model help the company make more money or save costs? Connecting your analytics to business goals is what makes you invaluable.

[self-test] 10 signs you're a successful data scientist project

Is this field for you? Answer yes or no to these questions to assess your suitability:

1. Do you enjoy solving logic puzzles, sudoku games or chess?
2. When you see a graph or statistic in the news, are you curious about "how" they arrived at that result and what it really means?
3. Are you comfortable working with numbers and tables, and not intimidated by the idea of spending hours in Excel or similar?
4. Do you have the patience and persistence to clean up a messy and disorganized data?
5. Do you like to constantly learn new techniques and tools (the field changes every 6 months)?
6. Can you explain a complex idea (such as a technical or scientific concept) to a non-specialized friend or family member?
7. Are you "skeptical" by nature? (meaning you don't take data or results for granted but question their source and accuracy).
8. Do you tend to think systematically and step-by-step when faced with a big issue?
9. Do you enjoy looking for patterns in everything (in people's behavior, in traffic, in markets)?
10. Are you ready to commit to lifelong learning?

If most of your answers are "yes," you have the right mindset to succeed in data science.

What discipline do you come from? The best academic backgrounds to enter data science

The good news is that data scientists come from very diverse backgrounds. There is no one "right" specialty.

• Traditional specialties:
  • Computer Science: It gives you a very strong foundation in programming and algorithms.
  • Statistics: It gives you a deeper theoretical foundation in modeling and mathematics.
  • Engineering: It gives you an organized problem-solving mindset.
  • Mathematics: closest to the theoretical core of the field.
• Non-traditional (and very successful) specializations:
  • Physics: Physics graduates are some of the best data scientists, because they are trained in complex mathematical modeling and problem solving.
  • Economics: Especially econometrics, which is basically statistical data analysis.
  • Business Administration (Business): Especially from finance or MIS majors, they have the "business acumen" that many others lack.

Conclusion: It's not the degree that counts, it's the quantitative and programming skills that you acquire. If you are able to demonstrate your ability to code (Python), understand statistics, and solve problems (via projects), you can enter the field regardless of your major.

The practical roadmap to becoming a data scientist (from zero to employment in Saudi Arabia)

If you decide this field is for you, here's a practical and realistic roadmap, tailored to the Saudi labor market.

Step 0: Build a solid foundation (statistics and math)

Don't try to jump straight into "machine learning" without this foundation, otherwise you'll just be a "tool user" rather than a "data scientist".

• What do you learn? Focus on basic concepts: Descriptive statistics (mean, median, standard deviation), probability, inferential statistics (hypothesis testing), and the principles of linear algebra (Matrices).
• Suggested sources: Platforms like Khan Academy or Introduction to Statistics courses on platforms like Edraak or Coursera.

Step 1: Speak the data language (master Python and SQL)

These are your indispensable daily tools.

• Start with SQL: It's easier and you'll use it every day to extract data. Learn SELECT, FROM, WHERE, GROUP BY, JOIN. You should be able to pull the data you need from multiple databases.
• Switch to Python: Learn the basics of the language first (variables, loops, functions). Then, immediately specialize in the three most important libraries:
  1. Pandas: This is your essential library for processing, cleaning, and analyzing data in tables.
  2. NumPy: for math and science.
  3. Matplotlib / Seaborn: to create graphs and visualize data.

Step 2: From analysis to visualization (tools like Power BI and Tableau)

Being able to present your results professionally doubles your value.

• The importance of Power BI: In the Saudi market specifically, the Microsoft Power BI Highly sought after and prevalent in most major corporations and government agencies.
• What do you learn? Learn how to connect data sources, create Measures using DAX, and build an interactive and easy-to-read Dashboard.

Step 3: Data Science Core (Machine Learning Fundamentals)

Now you're ready for the fun part.

• Start with the Scikit-learn library: is the standard library in Python for machine learning.
• Understand the differences:
  • Supervised Learning: When you have a "correct answer" (Label) in your data. It is divided into:
    • Classification: Predicting a category (e.g., will this customer leave the service "yes" or "no").
    • Regression: Predicting a continuous number (e.g.: How much is this house worth?).
  • Unsupervised Learning: When you don't have an answer, and you want the algorithm to discover patterns on its own.
    • Clustering: Grouping similar items (e.g., dividing your customers into 5 different segments).

Step 4: Build your portfolio (your first project is your key to the job)

These are The most important step of all To get a job. Theories and certifications alone are not enough; employers want Evidence that you "can" apply what you've learned.

• What is a Portfolio? It is 2-3 projects that you have done yourself from A to Z.
• How?
  1. Find data that interests you. You can use sites like Kaggle, or better yet, search for open data from "General Authority for Statistics (GASTAT) In Saudi Arabia.
  2. Ask an interesting question (e.g., can real estate prices in Riyadh be predicted based on neighborhood and area?)
  3. Full Life Cycle Dish: clean Data (Phase 2). Analyze it (EDA). Build a model (Phase 3). Rate it (Stage 4).
  4. Document everything: Write what you did and "why" you did it in a file README on a platform GitHubOr, better yet, write a blog post explaining your results (Stage 5).

In a job interview, when they ask about your experience, you will open this project and explain it. This is a million times more powerful than any certificate.

Saudi Arabia's labor market: What is the future of data science jobs?

Is data science in demand? A look at demand and salaries in Saudi Arabia

The short answer: Yes. More specialists are needed than there are available.

Demand for data scientists and data engineers in Saudi Arabia is exploding. The reason?

1. Vision 2030: All government projects and initiatives are now focused on "Data-driven decisions".
2. Digital transformation: Private companies in all sectors (banking, telecom, retail) realize that if they don't use their data, they will be left behind.
3. Government support (SDAIA): The Saudi Data and Artificial Intelligence Authority (SDAIA) is not only strategizing, but also pouring billions into capacity building and training thousands of specialists.

salaries: Data science job salaries are considered Highly rewarding and among the highest in the tech industry In the Kingdom. Salary varies based on experience (entry-level, mid-level, expert) and company (startup or large company). But in general, it's a very lucrative career path with a guaranteed future for at least the next decade.

Top 3 most in-demand data science jobs (data scientist, data engineer, machine learning engineer)

When we say "data science," we don't mean just one job. The market is maturing and demanding finer specializations:

1. Data Scientist: This is the "classic" role we talked about. This is the person who asks questions, builds statistical models, and communicates with management. It's a combination of statistics, programming, and business skills.
2. Data Engineer: This is the unsung hero. He "The Builder" who designs and implements the infrastructure and pipelines that bring in data from 100 different sources, clean it, prepare it, and put it into a Data Warehouse ready for the data scientist to use. Demand for data engineers may now outstrip demand for data scientists Because companies realize that there is no data science without clean and reliable data.
3. Machine Learning Engineer: This is the "specialist" who takes the prototype built by the data scientist (who may only work on his machine) and turns it into a real product that works at scale in a production environment. He focuses on MLOps - the processes of deploying, automating, and monitoring models.

Where do I start? The best Arabic learning resources and Saudi universities

Fortunately, learning resources are plentiful and available, especially in Saudi Arabia:

• Bootcamps: This is the fastest and most focused option to enter the market.
  • Tuwaiq Academy: is a pioneering initiative from Saudi Arabian Federation for Cybersecurity, Programming and Drones (SAFCSP)and works in close partnership with SDAIA to offer intensive, high-quality (free and subsidized) camps in data science and AI. Focusing on it is your first choice.
  • Misk Academy: It also offers pioneering programs in collaboration with international organizations.
• Universities: If you're looking for an academic track (master's degree), major universities offer excellent programs:
  • King Abdullah University of Science and Technology (KAUST)
  • King Fahd University of Petroleum and Minerals (KFUPM)
  • King Saud University (KSU)
  • Princess Nourah bint Abdulrahman University (PNU) (offers strong programs for women in the field)
• Arabic online platforms:
  • Edraak and Rwaq: It offers excellent foundation courses in Arabic.
  • Satr platform: Specialized courses are sometimes offered.
• Global Platforms (English):
  • Coursera, edX, DataCamp: It remains the best for specialized courses and professional certifications from major universities and companies (such as Google and IBM).

Frequently asked questions: Quick answers to your top data science questions

[Questions and Answers (FAQ) section on career paths and technologies]

Q1: Do I need a master's or PhD to become a data scientist?

c: Not necessarily, but it helps. In the past, most jobs required a graduate degree. Today, the market is more open. Strong Portfolio Proving your practical skills is more important than a degree. However, if you aspire to work in advanced research and development (R&D) roles or in highly complex fields, a master's or PhD will give you a significant advantage.

Q2: Which is better for learning data science: Python or R?

c: Both are great. R Very strong language in statistics and academic analysis. But Python It has become the dominant language in the industry. The reason is that it's a "General Purpose" language, which means you don't just use it for analysis, you can use it to build web applications and integrate models into production systems. For the Saudi labor market, the demand for Python is clearly higher.

Q3: How much math and statistics do I really need? Do I have to be a genius?

c: You don't need to be a genius Or memorize complex theoretical equations. But you need a strong "conceptual" understanding. You need to understand "why" the model works, what its "assumptions" are, and how to "evaluate" its results. You need to understand concepts like Correlation and Causation, and what a "mean" or "standard deviation" means. Tools (like Python) will do the complex calculations, but your job is to guide the tool and interpret its results.

Q4: How long does it take to get your first job in data science?

c: This depends entirely on How committed and serious you are. If you study intensively (Full-time) and focus on building projects, you can be ready for the job market in 6 to 12 months. If you're studying part-time alongside your job, it may take longer. The key is Continuity and build a business gallery.

Q5: Can I enter the field of data science from a non-technical background (such as marketing or HR)?

c: Yes, of course! This may be Your Big Competitive Advantage. Companies are not just looking for programmers, they are looking for people who understand "business". If you are an expert in marketing, for example, and learn the technical skills (SQL, Python, Power BI), you will become a "marketing data scientist". Your value will be much higher than a purely technical data scientist who understands nothing about customer behavior. Domain Expertise It's your secret weapon.

The essence of your journey: The future is in your hands

We've come to the end of this comprehensive guide. Before we wrap up, let's summarize the main points we've covered:

• Strategic importance: Data science is not just a trendy technical term, it is a key pillar and a vital driver for achieving the goals of Saudi Vision 2030, and its applications in NEOM and Aramco are proof of that.
• Versatility: Excelling in this field requires a unique combination of hard technical skills (e.g. Python, SQL, Power BI) and soft soft soft skills (e.g. curiosity, communication, and the ability to tell stories with data).
• Clarity of the path: Entering the world of data is open to everyone, regardless of their academic background, as long as you follow a practical roadmap that starts with the basics and centers around building a strong portfolio that proves your capabilities.
• High demand in the Saudi market: The Kingdom's labor market is experiencing a huge and growing demand for data professionals, offering a promising career path with high salaries and significant growth opportunities.

Thank you very much for investing your valuable time in reading this guide to the end. We hope it has demystified, provided clarity and usefulness, and given you the confidence to take your next step.

The journey of learning data science is a marathon that requires patience and persistence, not a sprint. The future of Saudi Arabia is being built on data, and those who master its language will be at the forefront of this transformation.

We wish you all the best on your next journey.