Essential Data science skills for new grads and early-career devs

Approximately 402.74 million terabytes of datahttps://explodingtopics.com/blog/data-generated-per-day# are generated daily. This means that every 24 hours, we could fill millions of modern hard drives with an immense volume of information being produced. Extracting valuable insights from vast amounts of data is a highly sought-after skill in today’s job market. Regardless of specialization—whether in software development, cybersecurity, AI, front-end development, back-end development, or full stack development—data science skills significantly enhance problem-solving abilities and provide a competitive edge.

In a recent project, I enhanced the performance of a customer support chatbot by leveraging data science techniques. I collected and cleaned interaction logs and user feedback, then used exploratory data analysis to identify delays caused by inefficient processing. I performed the correlation analysis, which revealed that certain queries triggered slow responses. I also segmented the data by user type to help pinpoint specific issues. Predictive modeling was employed to forecast peak usage times and optimize server resources. This approach significantly improved response times and user satisfaction.

Don’t know some of the terms or methods used? How can you apply these data science skills to your scenario? Don’t worry, we’ve got you covered in this blog. We will highlight the essential data science skills for new grads and early-career devs in detail.

Why choose data science?#

Data science is a field in computer science that combines mathematics, statistics, machine learning algorithms, and analytical processes to extract meaningful information from data.

Data is essential for solving any problem, whether you’re an individual or a company. Even with advanced large language models (LLMs) like ChatGPT, LLaMA, and Gemini, mastering data science skills is indispensable in today’s data-driven world. These skills will help you in:

1. Knowing your problem: Clearly defining the problem you are trying to solve helps select the appropriate data science techniques and approaches.

2. Knowing your data: Understanding the source, structure, and quality of your data is essential for accurate analysis and meaningful insights.

3. Understanding LLMs and the data they are trained on: Large language models (LLMs) are trained on extensive and diverse datasets, requiring a deep understanding of data preprocessing and model training to leverage their capabilities effectively.

4. Generalizing this knowledge to your problem: Applying your understanding of data science and LLMs to specific problems allows you to develop tailored solutions and innovative applications.

5. Career advancement / additional skill set: Acquiring data science skills can significantly enhance your career prospects, providing opportunities for leadership roles, higher salaries, and cross-functional collaboration.

Data science impacting various roles in the developer landscape#

Data science empowers developers to make informed, data-driven decisions, optimize system performance, and drive innovation across various domains. Here’s how:

• Software developers: Data science skills enable integrating data-driven features into applications, such as predictive analytics and recommendation systems. By understanding data structures, algorithms, and statistical methods, developers can optimize code efficiency and enhance user experience.

• Cybersecurity experts: Data science can help to identify patterns in security threats and implement predictive defenses. By analyzing large datasets, cybersecurity professionals can detect anomalies, predict potential breaches, and respond proactively to emerging threats.

• Front-end developers: Data science can help create user interfaces that intelligently display and react to complex data. Knowledge of data visualization techniques allows developers to present data in a clear and engaging way, improving user interaction and satisfaction.

• Back-end developers: Data science can be used to optimize data processing and storage. By understanding data modeling and database management, developers can design systems that handle large datasets efficiently and support advanced analytics. This ensures that applications run smoothly and can scale effectively as data volumes grow.

• Full stack developers: With their knowledge of both front-end and back-end technologies, they can leverage data science to create cohesive, data-driven applications. They can build systems where data flows seamlessly between the client and server, offering real-time, personalized experiences to users.

• Other roles: Beyond these roles, data science skills are also valuable in other positions such as product management, marketing, data analysis, business analysis, financial analysis, healthcare, supply chain management, human resources, UI/UX design, and operations management. In each of these roles, data science enables professionals to make data-informed decisions, optimize processes, and drive innovation.

Key data science terminology#

Before diving into data science skills, familiarize yourself with these 23 essential terms:

1. Dataset: A collection of data, often organized in tables or spreadsheets, used for analysis.

2. Feature: An individual measurable property or characteristic of a dataset, also known as a variable or attribute.

3. Label: The outcome or target variable in a dataset that the model is trying to predict.

4. Model: A mathematical representation or algorithm trained to make predictions or decisions based on data.

5. Algorithm: A set of rules or procedures a model uses to learn from data and make predictions.

6. Training data: The subset of data used to train a model, allowing it to learn patterns and make predictions.

7. Test data: The subset of data used to evaluate the performance of a model after training.

8. Overfitting: A scenario where a model learns the training data too well, including noise and outliers, leading to poor generalization of new data.

9. Underfitting: A scenario where a model is too simple to capture the underlying patterns in the data, leading to poor performance on both training and test data.

10. Feature engineering: The process of creating new features or modifying existing ones to improve model performance.

11. Normalization: The process of scaling data to a standard range, often [0, 1], to ensure all features contribute equally to the model.

12. Standardization: The process of transforming data to have a mean of 0 and a standard deviation of 1, often used to bring different features onto a common scale.

13. Data cleaning: The process of detecting and correcting errors or inconsistencies in data to improve its quality and usability.

14. Missing data handling: Techniques for addressing gaps in datasets, such as imputation (filling in missing values) or deletion.

15. Outlier detection: Identifying and handling data points that deviate significantly from most data.

16. Data transformation: Modifying data into a suitable format or structure for analysis, including aggregation, merging, or reshaping.

17. Data wrangling: The process of gathering, cleaning, and organizing raw data into a structured format for analysis.

18. Cross-validation: A technique for assessing the performance of a model by splitting the data into multiple training and test sets.

19. Performance metrics:

    1. Confusion matrix: A table used to evaluate the performance of a classification model by comparing predicted and actual values.

    2. Accuracy: It measures the proportion of correctly predicted instances (both true positives and true negatives) out of the total number of instances.

    3. Mean squared error (MSE): It measures the average of the squared differences between the predicted values and the actual values. It is used to evaluate the performance of regression models.

    4. Precision: The ratio of true positive predictions to the total number of positive predictions made by the model.

    5. Recall: The ratio of true positive predictions to the total number of actual positive instances in the data.

    6. F1 score: The harmonic mean of precision and recall, used to measure the overall performance of a classification model.

20. Regression: An algorithm used for predicting continuous outcomes based on input features.

21. Classification: A type of algorithm used for predicting categorical outcomes based on input features.

22. Clustering: An unsupervised learning technique used to group similar data points together.

23. Dimensionality reduction: Techniques such as PCA (Principal Component Analysis) are used to reduce the number of features in a dataset while retaining important information.

Use case#

Imagine you’re working on a new data analysis project where you need to predict customer churn for an e-commerce company. You start by organizing a dataset with various features like purchase history and interactions. You clean the data through data wrangling, addressing errors, missing values, and outliers. After transforming and scaling the data using normalization or standardization, you apply feature engineering to improve predictions. You train your model with this prepared data and evaluate it using test data, cross-validation, and metrics like the confusion matrix to measure precision and recall. Finally, you might use dimensionality reduction to streamline the dataset while retaining key information.

Essential data science skills for everyday professionals #

For everyday professionals, mastering some essential data science skills can lead to more effective analysis and insights. Here’s a look at key skills that can be invaluable:

• Basic statistics: Understanding fundamental statistical concepts such as mean, median, mode, standard deviation, and correlation is crucial. These basics help in interpreting data trends and making informed decisions based on statistical summaries.

• Data analysis tools: Familiarity with tools like Excel, Google Sheets, or basic SQL queries can significantly streamline data analysis tasks. These tools allow professionals to manipulate, sort, and filter data efficiently.

• Data visualization: The ability to create clear and meaningful visual representations of data, such as charts and graphs, is essential. Tools like Tableau, Power BI, or built-in charting features in spreadsheets can help present data easily.

• Data cleaning: Understanding basic techniques for data cleaning, including handling missing values, correcting errors, and removing duplicates, ensures that the data used for analysis is accurate and reliable.

• Introduction to programming: Learning the basics of programming languages like Python or R can enhance data manipulation and analysis capabilities. Even a basic understanding of coding can automate repetitive tasks and handle larger datasets more efficiently.

• Data wrangling: This involves gathering, transforming, and organizing raw data into a structured format for analysis. Skills in data wrangling ensure that data is ready for analysis and interpretation.

• Basic machine learning concepts: Knowing the fundamentals of machine learning, such as classification and regression, can help understand how data-driven models work and how they can be applied to make predictions.

• Understanding data sources: Being aware of various data sources and their reliability is important for ensuring that the data used is accurate and relevant. This includes knowledge of data collection methods and data quality assessment.

To kick things off, check out this excellent interview handbook:

Now that you’re familiar with basic terminology and fundamental data science skills, let’s explore the skills needed for a serious career in data science.

Essential data science skills for aspiring data scientists#

Each step in the data science life cycle is closely connected, with every phase relying on the outcomes of the previous one. Mastering these skills and tools is essential for a successful career in data science. By following this structured approach, you can ensure thorough analysis and actionable insights from your data. Let’s discuss the technical skills required to be a data scientist.

Note: Please be aware that the following skills should be acquired sequentially as listed, as each skill builds upon the previous one. Skipping any step may hinder your understanding and proficiency in the subsequent skills. However, if you’ve already studied any of these skills during your CS degree, you may skip them.

Programming#

To effectively manipulate, analyze, and store data, having essential programming skills is crucial. To be a data scientist, you should know how to program in:

• Python: Widely used for data analysis, machine learning, and general-purpose programming, making it a versatile tool for a broad range of data science tasks.

• R: Specifically designed for statistical analysis and data visualization, it creates complex plots and performs rigorous statistical tests.

• SQL: Essential for querying databases and handling large datasets, it helps you efficiently extract and manipulate data needed for analysis.

Enhance your skills by learning these languages through our expert-designed courses:

Data collection#

Data collection involves gathering data from various sources, including databases, APIs, and third-party providers, therefore, a crucial skill for being a data scientist. Once you have acquired the basic programming knowledge in Python, R, and database querying skills using SQL in the previous step, you should also understand the following skills:

• Web scraping: These are the tools for extracting data from websites. Some tools for scraping include:

  • Beautiful Soup: A Python library that parses HTML and XML documents for web scraping.

  • Scrapy: A Python framework that facilitates web crawling and scraping tasks.

  • Puppeteer: A Node.js library for controlling headless Chrome browsers to scrape and automate web tasks.

Learn about web scraping from our catalog of projects:

1. Scraping Wikipedia Using Selenium in Python

2. Headless Web Scraping Using Puppeteer

• APIs: These are the interfaces for accessing and getting data from services and online platforms. The list includes the following:

  • Requests: A Python library for making HTTP requests to interact with APIs.

  • Postman: A tool used for testing and interacting with APIs to retrieve data.

  • API Clients: Language-specific libraries (e.g., httr in R) used to access and interact with various APIs.

• Social media tools: These are the tools and libraries for collecting data from social media platforms. The list includes:

  • Tweepy: A Python library for accessing and interacting with Twitter data.

  • Facebook Graph API: Provides access to data from Facebook for analysis.

Enhance your knowledge by taking up this skill path especially tailored for you:

Data cleaning and manipulation #

This step is crucial for removing inaccuracies, dealing with missing values, and transforming data into a usable format. This will improve the quality and reliability of the data. Your skill set would be incomplete without this step. Once you have acquired your dataset, you will need the following tools/skills to clean it:

• Data wrangling techniques: Methods used to clean, transform, and prepare raw data for analysis, ensuring it is accurate, consistent, and usable.

• pandas (Python): A powerful Python library for data manipulation and analysis, offering data structures like DataFrames for efficient data wrangling.

Pick your course for mastering these techniques:

Exploratory data analysis (EDA)#

This stage involves exploring the data to uncover patterns, anomalies, and relationships. It also includes using visual tools such as histograms, scatter plots, and box plots to gain insights into the data’s structure, distribution, and correlations. The skills/tools you need to know are:

• Matplotlib: Python library for creating static, animated, and interactive visualizations with fine control over plot elements.

• seaborn: Python visualization library based on Matplotlib that provides a high-level interface for creating attractive and informative statistical graphics.

• ggplot2:  A library for creating complex plots based on the grammar of graphics, enabling intuitive and flexible data visualization.

• Statistical analysis: The process of collecting, summarizing, and interpreting data to uncover patterns, test hypotheses, and make informed decisions.

Take a look at these interesting courses:

Feature engineering#

This is a skill that involves creating new features from raw data that better represent the underlying problem to predictive models. Feature extraction involves selecting and transforming variables (features) from raw data to enhance the effectiveness of machine learning algorithms. This skill is essential for data scientists as it directly influences model accuracy and interpretability. To read more about these techniques, check out our interesting list of courses:

Machine learning#

This is a branch of artificial intelligence that enables systems to learn and improve from experience without being explicitly programmed. In this stage, we select appropriate algorithms, and training models, and optimize them to make predictions or classify data. It involves iterative experimentation to improve model accuracy. You will need to take an introductory course on ML and learn these supporting libraries that support numerous algorithms:

• Scikit-learn: A Python library for classical machine learning algorithms, providing tools for data preprocessing, model training, and evaluation.

• TensorFlow: An open-source platform for machine learning and deep learning, offering a comprehensive ecosystem for developing and deploying models.

• Keras: A high-level neural networks API, running on top of TensorFlow, designed for fast experimentation with deep learning models.

• PyTorch: An open-source deep learning framework known for its dynamic computational graph and ease of use in research and development.

How to learn machine learning as a CS graduate:

Model evaluation#

Model evaluation involves assessing the performance of the machine learning models using metrics like accuracy, precision, recall, F1 score, confusion matrix, and ROC curve analysis. This step ensures the model meets the project objectives. You must understand the theory behind these metrics and how to calculate them. Scikit-learn can be used to efficiently compute these metrics.

Data visualization#

Data visualization is the process of representing data and model results graphically to communicate findings effectively to stakeholders. It helps in understanding complex data insights quickly. This is another crucial skill that will eventually make you a data scientist.

Tools like Matplotlib and seaborn were introduced during the exploratory data analysis (EDA) stage. However, in this step, we focus on visualizing the model’s performance. While these tools can be used in both exploration and visualization, the key distinction here is that this step applies after the model has been trained and tested, focusing on presenting the results effectively.

The best data visualization tools for beginners are:

• Plotly: An interactive graphing library that supports various chart types and interactive features.

• Tableau: A powerful data visualization tool that allows for the creation of interactive and shareable dashboards.

• Power BI: A Microsoft tool for visualizing data and creating business intelligence reports and dashboards.

• D3.js: A JavaScript library for producing dynamic, interactive data visualizations on the web.

Explore our highly interactive courses on visualization tools:

In summary, the data science life cycle can be visualized as a sequence of stages, each representing a skill that involves mastering various tools and techniques. Understanding this life cycle will help you build a data science portfolio as a recent graduate.

Human skills vs. LLMs#

You might be wondering why companies would hire you when powerful tools like GPT or other advanced LLMs can automate many tasks in the data science life cycle. Take a step back and relax, LLMs are not perfect and have their limitations. They still need human expertise and creativity to truly excel. They also limit usage in multimodal support whereas data visualization is a very important stage, so it is better to rely on old-school programming and generate as many graphs as you want!