Data Science Roadmap for Beginners Complete Guide in 2025

Hello everyone, welcome to the Data Science Roadmap for beginners of Blogs with Abdi. You have decided to become a data scientist, you have come to the right place, we give you all the advice. This guide is written in easy-to-understand alphanumeric format to help you do complete research and prepare various documents. In it, we will give you important tips related to data science related to Python, it will also help you in job hunting.

 

First of all, we need to find the important tools of AI, join us on this journey of knowledge and become a member of our WhatsApp channel, where you will be given free courses, tools, and any questions related to this topic, please comment.

 

 

What is Data Science?

• Companies need to better understand their customers and people's interests.

• Hospitals track disease trends to provide better treatment and information.

• If we look at the government sector, we can see that smart government decisions like traffic control or disaster response or palaces in a country or city are easily monitored and the temperature in a city or city is easier to predict.

• If you look at social media, you will see that they connect content or things that you are interested in, such as when you search for clothes, then you will see relevant ads, releases, posts. Even Facebook, Instagram, Netflix or YouTube will recommend content to you that you like or search for!

Data science is a multidisciplinary field that uses a combination of technology, mathematics, and domain knowledge to extract meaningful insights from data. The field analyzes raw data (such as numbers, text, images), businesses, organizations, or industries to provide useful information, which helps in decision-making. If we understand it in simple terms, data science is a mixed field of various fields that is used to derive information and reports from structured and unstructured data.

 

It consists of techniques and processes, such as data collection, cleaning, analysis/reporting, visualization, and modeling. Data scientists use their statistics, mathematics, programming, and their domain knowledge to understand the patterns, trends, and learnings present in the data and strengthen organizations or a company. Nikal keeps your company or organization safe from any map.

 

I will tell you some of the essential fields in data science that are involved in it, such as statistics, computer science and mathematics. Along with this, data cleaning and formatting is also a good thing. It also uses data visualization so that your insights can be easily explained in your reports.

 

Before understanding the entire roadmap of data science, it is important to have a clear goal in your mind as to why you want to pursue data science. Let me explain why data science is essential with examples so that you can understand it from every perspective. Data science is essential because in today’s era, data is everywhere, from mobile applications to websites to businesses to every social interaction. You don’t even need a data tab until we understand it, valuable insights will come out of us.

 

What we can do with the help of log data science and we understand it:

 

That is, it is a skill that is useful in every field and is becoming even more important in the future.

Why do you want to pursue data science? If you are looking for College educational projects, then finding a surf beginner level choice will be enough. But if you want to make a career in it, then you will also have to look for professional or advanced skills. And for this, you will have to understand all the basics in detail and work hard.

This is your goal in your hands and this decision is also yours as to why you want to pursue data science. 

🚀 Data Science Roadmap

1. Programming Fundamentals

Start with Python, R, SQL, and Git to handle data and projects efficiently.

2. Probability

Understand events, random variables, and distributions to model uncertainty.

3. Statistics

Learn hypothesis testing, mean, median, mode, standard deviation, and linear regression.

4. Feature Engineering

Master encoding, binning, handling missing values, and scaling to improve models.

5. Data Visualization

Create clear, meaningful visuals using Excel, Tableau, Power BI, or Python libraries like Seaborn and Matplotlib.

6. Machine Learning

Study supervised and unsupervised learning, model evaluation, and algorithms like Decision Trees, KNN, and SVM.

7. Deep Learning

Dive into neural networks (ANN, CNN, RNN), and explore frameworks like TensorFlow and PyTorch.

8. NLP (Natural Language Processing)

Focus on text processing, sentiment analysis, vectorization, and popular NLP models.

9. Deployment & Practice

Deploy ML models using Flask or Streamlit, and build projects with real-world datasets (Kaggle, GitHub).

Importance of Data Scientist

Data scientist has become a very important role in today’s digital era. The role of a data scientist is no longer that of a treasure hunter, but rather a sea of ​​data that provides valuable insights that help companies become smarter and faster.

Here are some of the key reasons why data scientists are so important:

1. Make decision-making smarter
2. Understand the customer
3. Predict the future
4. Detect fraud and manage risk
5. Innovation or automation
6. Every industry needs one.
7. Understand patterns and trends within datasets
8. Drive evidence-based innovation

A data scientist is someone who has the ability to turn raw data into gold. When you find talent in a design-oriented job like yours, it takes a great fusion of creativity and logic. And according to ISI, it has become an essential resource for every data-driven organization.

Required Skills to Become a Data Scientist

To become a data scientist, you need a good mix of technical and soft skills. The combination can be even more powerful for a creative and detail-oriented designer like you.

Here’s a structured list:

Technical skills

• Programming: preferably in Python or R.
• Math and statistics: understanding data, linear algebra.
• Data manipulation: tools like Pandas, NumPy to clean, transform, and prepare data.
• Data visualization: libraries: Matplotlib, Seaborn, Plotly Tools: Tableau, Power BI — to tell stories with data.
• Machine learning: proficient in machine learning.
• Deep learning (advanced): neural networks, CNNs/RNNs, and frameworks like TensorFlow or PyTorch.
• Big Data Tools (optional for scale): Hadoop, Spark, and cloud platforms like AWS, GCP, or Azure.
• Model Deployment: Use Flask, Fast API, or Streamlit to deploy models as web apps.

Soft Skills

• Problem Solving: Look at problems logically and creatively to find solutions through data.
• Communication: Clearly explain technical concepts to non-technical stakeholders using visuals or simple language.
• Curiosity and Continuous Learning: Data science is always evolving, so staying up-to-date is crucial.
• Domain Knowledge: Understanding the industry you are working in helps make insights more meaningful.

Career Opportunities with Salary in Data Science

 

Role	🇵🇰 PKR (Avg/Year)	💵 USD (Avg/Year)	💶 EUR (Avg/Year)
Data Analyst	PKR 800k – 1.5M	$55,000 – $75,000	€45,000 – €65,000
Junior Data Scientist	PKR 1M – 2M	$70,000 – $90,000	€55,000 – €75,000
Data Scientist	PKR 1.5M – 4M	$95,000 – $120,000	€70,000 – €100,000
Machine Learning Engineer	PKR 2M – 5M	$110,000 – $140,000	€85,000 – €115,000
Data Engineer	PKR 1.8M – 4.5M	$100,000 – $130,000	€75,000 – €105,000
AI Researcher / Specialist	PKR 3M – 6M+	$120,000 – $160,000+	€90,000 – €130,000+

 

💡 Entry-level data scientists in Pakistan start at around PKR 679,000, while experienced professionals are making PKR 4 million+

This data is an average of what is going on right now, this is the data, but the estimate of each country and each company may be different. Salary always depends on what your role is, what is your job? You have a lot of skills and whose skills you need to practice. If you are a fresher, your expiration time can be from 1 to 2 years. If you are a fresher, look for an internship, focus on the internship, you will get to do it on-site in some company.

How did you develop your skills?

Let's see step by step how you can easily and effectively find skills like Math and Python.

1. Math (Stats + Linear Algebra + Probability)

These are the basics for data science. How to find:

• Start with visual math YouTube channels like 3Blue1Brown or StatQuest that explain math with animations - it won't be boring!
• Practice with real-life examples—like Netflix recommendations or Google Maps predictions—and see which math concepts apply where.
• Practice platforms: Khan Academy, Brilliant.org, or the "Essence of Linear Algebra" playlist.

2. Python (Coding for Data Science)

Finding Python is like finding a creative language and you'll even have fun with animation and conversation:

• Install Jupyter Notebook (via Anaconda) - lets you write code in interactive cells
• Basic concepts: variables, loops, conditions, functions → phir Pandas aur NumPy
• Fun projects:

1. Create a data dashboard of your favorite YouTube channels
2. Analyze your phone usage data (screen time, call logs, etc.)

• Platforms: W3Schools - Python, FreeCodeCamp.org, Kaggle Learn - Python course

3. Combine the two with projects

Combine Math + Python by doing projects like:

• Create a simple data visualizer using Python’s Seaborn or Plotly
• Analyze datasets like “Titanic Survivors” or “Pokémon Statistics” (easy + fun)
• Add hover effects or animations to your Python dashboards using Plotly Express or Dash

Conclusion

Data science is a multidisciplinary field that turns numbers into narratives, and raw data into real decisions. People like you who have both creativity, design sense, and technical acumen can not only bring fresh thinking to the field, but also take visual storytelling and user-centric insights to the next level.

You’ve created a roadmap, explored tools, and figured out practical ways to acquire skills like Python, Math, ML, and deployment. Each skill is a building block that builds a strong foundation for your data science career.

🚀 If the goal is clear, the resources are available, and the drive to find — then becoming a data scientist isn’t just possible, it’s your playground!