Data Science Prerequisites [2025] - Things to Learn Before Data Science

Data Science is one of the fastest-growing sectors in the technological field and it offers unique opportunities in different areas like media, marketing, healthcare, finance, etc. Starting as a Data Scientist can be challenging and exciting at the same time, however before you jump into advanced analysis or model creation, it is important to build and solidify your fundamentals. Without knowing these basics, it would be near impossible for you to become a full-fledged data scientist.

In this guide, you will discover the essential prerequisites of data science that you need to master to build a thriving career in this dynamic and rapidly growing field.

What is Data Science?

Data science is a method of extracting useful information from data. It uses tools and techniques to analyze, interpret, and visualize data to support decision-making. The techniques for combining programming, mathematics statistics, and domain knowledge are applied to convert raw data into problem solutions.

This guide introduces you to a comprehensive tutorial that provides detailed insights into the field of data science: Data Science Tutorial

Close your eyes and imagine yourself as a detective looking for the cause of a mystery. Data scientists are detectives, and their clues are data. They identify trends and patterns using their instruments and methodologies to answer questions or solve problems. Data Science is used for various tasks such as predicting customer behavior, improving supply chain processing, detecting fraud, recommending products, diagnosing diseases and even making fully autonomous cars.

You must learn the basics to take advantage of the opportunities in data science. When you are familiar with basic concepts, the possibilities are endless!

1. Understanding Data: The Core of Data Science

Before diving into technical tools, it’s essential to understand data - its nature, structure, and how it’s collected.

What is Data?

Data refers to raw facts or figures in different formats like text, visuals, or sound, used to derive insights. In Data Science, this information is categorized as:

• Structured Data:
  • Organized into rows and columns (example: spreadsheets, SQL tables).
  • Example: A sales database containing product names, prices, and quantities.
• Unstructured Data:
  • Does not have a predefined structure.
  • Example: Social media posts, customer reviews, or video content.

Types of Data

Data consists of various types. Some different types of data include:

1. Quantitative Data (Numerical):
   • Discrete: Countable values (example: number of clicks on a website).
   • Continuous: Measurable values (example: temperature, age, income).
2. Qualitative Data (Categorical):
   • Nominal: Categories without order (example: colors, countries).
   • Ordinal: Categories with a meaningful order (example: ratings, educational levels).

Data Sources

Understanding where data comes from is critical for every data scientist. Common data sources include:

• Surveys and Questionnaires: Structured methods to collect targeted data.
• Web Scraping: Extracting information from websites using tools like Beautiful Soup or Scrapy.
• APIs (Application Programming Interfaces): Accessing data from platforms like Twitter, Google Maps, or OpenWeather.
• Sensors and IoT Devices: Collecting data in real-time from devices like fitness trackers or industrial sensors.
• Logs and Records: Tracking user interactions or operational metrics.

2. Mathematics and Statistics: The Foundation of Data Science

Mathematics and statistics are the backbone of data science. They help you understand data, build models, and interpret results.

Key Mathematics Topics:

1. Linear Algebra:
   • Focuses on vectors, matrices, and their operations.
   • Essential for machine learning techniques like Principal Component Analysis (PCA), recommendation systems, and neural networks.
2. Calculus:
   • Helps in understanding optimization and gradient descent.
   • Critical for training machine learning models where minimizing error is the goal.
3. Probability:
   • Provides a framework for handling uncertainty in data.
   • Concepts like probability distributions, conditional probability, and Bayes’ Theorem are vital.
4. Statistics:
   • Descriptive Statistics: Summarize data using metrics like mean, median, mode, variance, and standard deviation.
   • Inferential Statistics: Draw conclusions about a population based on sample data (example: confidence intervals, hypothesis testing).

3. Programming: Your Toolkit for Data Science

Programming is essential for every data scientist. While you don’t need to be a master programmer to get started, a solid grasp of key languages and tools will make your journey smoother.

Key Programming Languages:

1. Python:
   • Widely used in data science due to its simplicity and versatility.
   • Libraries:
     • NumPy: For numerical computations.
     • pandas: For data manipulation and analysis.
     • matplotlib/seaborn/Plotly: For data visualization.
     • scikit-learn: For machine learning.
     • TensorFlow/PyTorch: For deep learning.
2. R:
   • Known for statistical computing and data visualization.
   • Libraries:
     • ggplot2: For visualizations.
     • dplyr: For data manipulation.
     • caret: For machine learning.
3. SQL:
   • Essential for querying and retrieving structured data from databases.

4. Data Manipulation and Cleaning: Preparing Data for Analysis

Cleaning and transforming data for analysis is one of the most time-consuming aspects of a data scientist's work. Raw data is frequently unorganized, lacking, or inconsistent.

Key Steps in Data Cleaning:

1. Handling Missing Data: Fill missing values using techniques like mean imputation or predictive modeling, or remove incomplete rows.
2. Removing Duplicates and Outliers: Clean up anomalies that could skew results.
3. Standardizing Formats: Ensure consistency in text (example: lowercase, removing punctuation) and dates.

Data Transformation:

• Scaling and Normalization: Adjust numerical data for compatibility with machine learning algorithms.
• Encoding Categorical Variables: Convert non-numerical categories into numerical formats (example: one-hot encoding).
• Feature Engineering: Create new features that enhance the predictive power of models.

5. Data Visualization: Communicating Insights

The process of displaying data in a graphical or visual format, like dashboards, graphs, and charts, is known as data visualization. It assists in identifying trends, patterns, and insights that may be hard to find in unprocessed data. It helps stakeholders make well-informed decisions by converting complicated datasets into an understandable visual language.

Tools and Libraries:

1. Python: matplotlib, seaborn, Plotly
2. R: ggplot2, shiny
3. Tableau and Power BI: Popular drag-and-drop tools for creating interactive dashboards.

Common Visualization Types:

• Basic Charts: Line charts, bar charts, pie charts.
• Advanced Charts: Histograms, heatmaps, scatter plots, box plots.
• Interactive Dashboards: Combining multiple visualizations in a single interface.

6. Machine Learning: The Core of Data Science

Machine learning is one of the most exciting aspects of data science. It enables systems to learn from data and make predictions or decisions without being explicitly programmed.

This Machine Learning Tutorial will provide you with everything you need to know about Machine Learning: Machine Learning Tutorial

Key Concepts:

1. Supervised Learning:
   • Algorithms learn from labeled data.
   • Examples: Linear regression, logistic regression, decision trees, random forests.
2. Unsupervised Learning:
   • Algorithms identify patterns in unlabeled data.
   • Examples: K-means clustering, hierarchical clustering, PCA.
3. Reinforcement Learning:
   • Algorithms learn by trial and error, optimizing for rewards.

7. Domain Knowledge: Adding Context to Data Science

Being a great data scientist requires more than just knowing how to code or analyze data. Additionally, you must comprehend the industry in which you operate. This enables you to use the appropriate data, find the right solutions, and interpret the outcomes.

Examples from Various Fields:

• Healthcare: Developing models to forecast patient health or suggest treatments requires an understanding of medical terminology and the course of diseases.
• Finance: Recognizing market dynamics and risk factors aids in fraud detection and credit score prediction.
• Retail: Forecasting demand and creating tailored recommendations are made easier with an understanding of consumer preferences and product sales methods.

This article better explains the importance of domain knowledge in Data Science: Role of Domain Knowledge in Data Science

8. Essential Tools for Data Science

Data science involves a variety of tools that help with coding, version control, big data processing, and more. Below are some of the most commonly used tools:

Development Environments

• Jupyter Notebook: An interactive environment for Python that allows you to mix code, text, and visualizations in a single document. It’s great for data exploration and quick prototyping.
• Google Colab: A cloud-based version of Jupyter with free access to GPUs and TPUs, which is especially helpful for machine learning tasks that require heavy computation.
• VS Code: A lightweight, highly customizable code editor with great support for Python, R, and other languages, along with extensions for Git integration and Jupyter Notebooks.
  • How to Install VS Code on Windows?
  • How to run Python file on VS Code?
• RStudio: The go-to IDE for R programming, widely used in statistical analysis and visualization. It’s perfect for users who prefer R for data manipulation and modeling.

Version Control

• Git: A distributed version control system to track code changes, manage branches, and collaborate effectively with others.
• GitHub: A cloud-based platform that hosts Git repositories and provides collaboration tools, such as pull requests and project boards.
• GitLab/Bitbucket: Alternatives to GitHub with additional features for private repositories and integrations with other development tools.

Big Data Tools

Before getting to Big Data Tools, it is necessary to understand What is Big Data? and What is Big Data Analytics?. Now, lets take a look at the top Big Data tools we have available to us:

• Hadoop: A framework for distributed storage and processing of large datasets. It allows you to scale up and work with data across multiple machines.
• Apache Spark: A fast, in-memory big data processing engine that works on top of Hadoop. It’s widely used for real-time analytics and machine learning tasks.
• Apache Kafka: A distributed event streaming platform for building real-time data pipelines. It’s used for processing and integrating large streams of data in real-time.

9. How Data Science Works (An Example)

To see how data science works in practice, let’s explore a scenario where a retail company wants to predict customer churn—when customers stop purchasing or using services. The process involves several steps:

1. Data Collection

The first step is gathering customer data from a SQL database. This data might include demographic information, purchase history, feedback, complaints, and subscription details. A data scientist extracts the relevant data using SQL queries, ensuring it’s structured and ready for analysis.

2. Data Cleaning

Raw data often contains issues like missing values, outliers, and inconsistent formats. Missing values might be filled or removed, outliers capped, and text or date formats standardized. Cleaning ensures the data is accurate and reliable, setting the stage for meaningful analysis.

3. Exploratory Data Analysis (EDA)

EDA involves analyzing and visualizing data to uncover patterns and trends. Using tools like Python’s seaborn, the data scientist may plot customer purchase frequency, average spending, or the relationship between complaints and churn. This step identifies key factors driving churn.

4. Feature Engineering

New features are created to enhance the model’s predictive power. For instance, the data scientist might calculate average order value, time since the last purchase, or the ratio of complaints to interactions. These engineered features provide additional insights for the model.

5. Model Training

The prepared data is split into training and testing sets. A machine learning model, like a random forest, is trained on the data to recognize patterns associated with churn. The model’s accuracy is tested on unseen data to ensure reliability.

6. Insight Sharing

The final step is communicating findings through dashboards or reports using tools like Tableau. For example, the company might see a list of customers at high risk of churning, along with actionable insights like offering discounts or personalized engagement to retain them.

7. Final Outcome

By following this process, the company gains valuable insights into customer behavior, enabling them to reduce churn and improve customer retention through data-driven strategies. This highlights how data science transforms raw data into actionable solutions.

Consider this practical example: "Python | Customer Churn Analysis Prediction". This article comprehensively walks through the end-to-end process of building a churn prediction model, covering crucial steps like data collection, cleaning, exploratory analysis, feature engineering, model training, and presenting actionable insights.

Conclusion

Data science is a journey of exploration, problem-solving, and continuous learning. While the prerequisites may seem overwhelming, take it step by step. Start small, build projects, and expand your knowledge over time.

The world of data science is vast, challenging, and incredibly rewarding. Your curiosity, creativity, and technical skills can make a real difference - whether it’s helping businesses grow, improving healthcare outcomes, or addressing global challenges. So, roll up your sleeves, dive into data, and get ready to contribute to this transformative field!