Top 12 Quantitative Developer Skills to Put on Your Resume

In an ever-evolving financial landscape, quantitative developers stand out by blending advanced mathematics with programming prowess to solve complex problems. Mastering a unique set of skills is crucial for those aspiring to excel in this competitive field, making your resume a critical tool to showcase your expertise and edge out the competition.

Quantitative Developer Skills

1. Python
2. R
3. MATLAB
4. C++
5. SQL
6. TensorFlow
7. PyTorch
8. Machine Learning
9. Statistical Analysis
10. QuantLib
11. Hadoop
12. Spark

1. Python

Python is a high-level, versatile programming language widely used by quantitative developers for data analysis, financial modeling, and algorithmic trading due to its simplicity, extensive libraries (like NumPy, pandas, and SciPy), and strong community support.

Why It's Important

Python is crucial for a Quantitative Developer due to its simplicity, extensive libraries for numerical and statistical analysis (like NumPy, pandas, and SciPy), powerful data visualization tools (such as Matplotlib and Seaborn), and its wide adoption in the finance industry for tasks ranging from data analysis and modeling to back-testing trading strategies and machine learning.

How to Improve Python Skills

Improving your Python skills, especially as a Quantitative Developer, involves continuous learning and practical application. Here are concise pointers with resources:

1. Master Core Concepts: Deepen your understanding of Python's core concepts, such as data structures, OOP, and concurrency. Python Docs is a great starting point.

2. Data Analysis Libraries: Gain proficiency in NumPy, pandas, and matplotlib for data manipulation and analysis. 10 Minutes to pandas is a quick tutorial.

3. Machine Learning: Learn to apply Python libraries like scikit-learn for quantitative analysis and predictive modeling. Check out scikit-learn Tutorials.

4. Quantitative Finance Libraries: Explore libraries such as QuantLib for quantitative finance. The QuantLib Python Cookbook is a useful resource.

5. Algorithmic Trading: Understand how to design and backtest trading strategies with libraries like backtrader. Backtrader's Documentation provides comprehensive guidance.

6. Performance Optimization: Learn to optimize your code for performance using techniques like vectorization and parallel computing. Numba offers easy JIT compilation.

7. Code Quality: Improve code quality and maintainability using tools like flake8 for style guide enforcement. More on flake8.

8. Version Control: Use version control systems like Git to manage code changes and collaborate. Git Basics covers the essentials.

9. Continuous Learning: Stay updated with the latest Python developments and quantitative finance trends through platforms like QuantStart and Towards Data Science on Medium.

10. Practical Projects: Apply your skills to real-world projects or contribute to open-source. Exploring projects on GitHub can provide inspiration.

By following these steps and utilizing the provided resources, you'll be well on your way to enhancing your Python proficiency as a Quantitative Developer.

How to Display Python Skills on Your Resume

2. R

R is a programming language and software environment designed for statistical computing and graphics, widely used by data analysts, quantitative developers, and researchers for data manipulation, calculation, and graphical display.

Why It's Important

R is crucial for a Quantitative Developer because it offers a rich set of statistical and graphical techniques, facilitating data analysis, modeling, and visualization, which are essential for developing quantitative financial models and strategies.

How to Improve R Skills

Improving your R skills, especially as a Quantitative Developer, involves understanding advanced R programming concepts, efficient data manipulation, and statistical modeling techniques. Here are concise steps with relevant resources:

1. Master Advanced R Features: Learn about functional programming, object-oriented programming, and efficient data handling in R. The Advanced R book by Hadley Wickham offers a deep dive.

2. Efficient Data Manipulation: Become proficient with dplyr and data.table for fast data manipulation. The dplyr vignette and data.table wiki are excellent resources.

3. High-Performance R: Learn to write high-performance R code, utilizing techniques for speeding up R, including Rcpp for integrating C++ code. Check The Rcpp book for comprehensive guidance.

4. Statistical Modeling and Machine Learning: Deepen your knowledge in statistical modeling and machine learning within R. The ISLR book provides a solid foundation in statistical learning techniques.

5. Time Series Analysis: For financial quantitative analysis, understanding time series data is crucial. The forecast package and its accompanying book offer extensive insights into forecasting methods.

6. Quantitative Finance Packages: Explore R packages specific to quantitative finance, such as quantmod, PerformanceAnalytics, and TTR for market analysis, performance evaluation, and technical trading rules, respectively. The CRAN Task View: Finance lists relevant packages and resources.

7. Contribute to Open Source: Engage with the R community by contributing to open-source projects. This provides practical experience and feedback. Explore GitHub for R projects in need of contributors.

8. Networking and Continual Learning: Join R user groups, forums like Stack Overflow, and attend workshops or conferences like useR! to stay updated on the latest trends and techniques.

By following these steps and utilizing the linked resources, you can significantly improve your R skills in the context of quantitative development.

How to Display R Skills on Your Resume

3. MATLAB

MATLAB is a high-level programming and numerical computing environment used for algorithm development, data analysis, visualization, and numerical computation, highly valued by quantitative developers for its powerful mathematical and financial modeling capabilities.

Why It's Important

MATLAB is important for a Quantitative Developer due to its powerful mathematical and statistical toolset, high-level programming environment, and capabilities for data analysis and visualization, allowing for efficient development and implementation of complex quantitative financial models and algorithms.

How to Improve MATLAB Skills

Improving MATLAB skills, especially for a Quantitative Developer, involves a focused approach on mastering advanced mathematical modeling, algorithm development, and data analysis techniques. Here are concise steps with resources to enhance your MATLAB proficiency:

1. Master Core MATLAB Syntax and Functions: Start with the basics but quickly move on to more complex concepts. MATLAB Onramp is a free tutorial that covers fundamental programming concepts.

2. Learn Advanced Data Analysis Techniques: Dive into statistical modeling, machine learning, and time series analysis using MATLAB. The Statistics and Machine Learning Toolbox provides functions and apps to describe, analyze, and model data.

3. Enhance Your Algorithm Development Skills: Focus on creating efficient, scalable algorithms for quantitative analysis and financial modeling. Algorithm Development with MATLAB offers guidance on developing algorithms.

4. Familiarize with Quantitative Finance Tools: Explore the Financial Toolbox and Risk Management Toolbox for functions that support quantitative finance and risk management tasks.

5. Practice with Real-World Projects: Apply your skills to solve practical problems in quantitative finance. MATLAB Central provides a community and a repository of code, questions, and files that can be a rich resource for real-world examples.

6. Stay Updated and Connect with Community: Join MATLAB and Simulink Maker Community to stay updated on the latest trends, ask questions, and connect with other professionals.

By focusing on these areas and utilizing the resources provided, you can significantly improve your MATLAB skills relevant to quantitative development.

How to Display MATLAB Skills on Your Resume

4. C++

C++ is a high-performance programming language that offers a blend of procedural, object-oriented, and generic programming features, making it a preferred choice for developing complex quantitative financial models and algorithms due to its ability to handle large datasets and execute calculations rapidly.

Why It's Important

C++ is crucial for a Quantitative Developer because it offers high performance and fine-grained control over system resources, essential for developing complex numerical algorithms and real-time financial models where speed and efficiency are paramount.

How to Improve C++ Skills

Improving your C++ skills, especially as a Quantitative Developer, involves focusing on performance optimization, understanding financial algorithms, and mastering advanced C++ features. Here's a concise guide:

1. Master the Basics: Solidify your understanding of C++ fundamentals, including syntax, data types, pointers, and memory management. C++ Documentation is an excellent resource.

2. Learn Advanced Features: Dive into advanced topics like templates, the Standard Template Library (STL), and C++11/14/17/20 features. LearnCpp is a great starting point.

3. Understand Concurrency and Multithreading: Essential for performance optimization in quantitative finance. C++ Concurrency in Action provides in-depth knowledge.

4. Optimize Performance: Focus on writing efficient code. Learn about optimization techniques and tools. The book Optimized C++ offers practical advice.

5. Practice with Quantitative Problems: Apply your C++ skills to solve quantitative finance problems. Websites like Project Euler and LeetCode have relevant challenges.

6. Contribute to Open Source Projects: Engage with the community and contribute to open-source projects related to finance. GitHub is a great place to find projects.

7. Stay Updated: The C++ landscape is continually evolving. Follow ISO C++ for the latest standards and proposals.

8. Networking and Community: Join forums and groups like Stack Overflow and Reddit’s r/cpp. Sharing knowledge and solving problems together can accelerate learning.

By focusing on these areas and consistently practicing, you can significantly improve your C++ skills, making you a more effective and efficient Quantitative Developer.

How to Display C++ Skills on Your Resume

5. SQL

SQL (Structured Query Language) is a standardized programming language used for managing and manipulating relational databases, enabling data retrieval, insertion, updating, and deletion, essential for quantitative developers for data analysis and financial modeling.

Why It's Important

SQL is crucial for a Quantitative Developer because it enables efficient data retrieval, manipulation, and analysis from databases, which are fundamental tasks for developing quantitative models, conducting financial analyses, and driving data-driven decision-making.

How to Improve SQL Skills

Improving your SQL skills as a Quantitative Developer involves enhancing both the efficiency of your queries and your ability to handle complex data analysis. Here's a concise guide:

1. Understand SQL Execution Plans: Get familiar with how your database executes queries. This knowledge can help you write more efficient SQL code. SQL Execution Plans

2. Indexing: Learn about indexing strategies to speed up data retrieval. Proper indexing can dramatically improve query performance. SQL Indexing and Tuning

3. Analytical Functions: Master SQL analytical functions (e.g., ROW_NUMBER, RANK, LEAD, LAG, SUM() OVER()) for advanced data analysis. Analytical Functions

4. Complex Joins: Understand how to efficiently join multiple tables and how to utilize different types of joins for complex data analysis. SQL Joins

5. Optimization Techniques: Learn various SQL optimization techniques such as query refactoring, avoiding subqueries, and using temp tables effectively. SQL Optimization

6. Practice with Real Datasets: Apply your skills on real or simulated datasets to solve practical problems. Websites like Kaggle offer datasets and challenges. Kaggle Datasets

7. Continuous Learning and Community Engagement: Stay updated with the latest trends and best practices in SQL by following blogs, forums, and participating in discussions. Stack Overflow and Database Administrators Stack Exchange are great places to start.

By focusing on these areas, you can significantly enhance your SQL capabilities, making your data analysis more efficient and insightful.

How to Display SQL Skills on Your Resume

6. TensorFlow

TensorFlow is an open-source machine learning library developed by Google, designed for high-performance numerical computation. It facilitates building and training neural networks for tasks such as deep learning and complex algorithms, crucial for quantitative analysis and financial modeling.

Why It's Important

TensorFlow is important for a Quantitative Developer because it provides a flexible and powerful platform for building and deploying complex mathematical models, including deep learning algorithms, essential for analyzing large datasets and making predictive financial decisions.

How to Improve TensorFlow Skills

To enhance TensorFlow for a Quantitative Developer, consider the following strategies:

1. Stay Updated: Regularly check TensorFlow's official website for updates, as newer versions often come with performance improvements and additional features.

2. Optimize Computation: Utilize TensorFlow's performance guide to optimize computation by leveraging graph execution, which can lead to significant speedups.

3. Use Accelerators: Employ hardware accelerators like GPUs or TPUs. TensorFlow has extensive support for GPUs and TPUs, which can drastically reduce training and inference times.

4. Parallelize Data Loading: Improve data input pipeline efficiency by using the tf.data API for parallel data loading. See Better performance with the tf.data API for techniques to optimize data loading and preprocessing.

5. Apply Quantization: Implement model quantization to reduce model size and improve latency without significantly sacrificing accuracy. TensorFlow provides guides on post-training quantization and quantization aware training.

6. Utilize TensorFlow Serving: For deploying models in production, use TensorFlow Serving, which is designed for high-performance machine learning model serving.

7. Profile Your Model: Use TensorFlow's Profiler to identify bottlenecks in your models and data pipelines, allowing for targeted optimizations.

8. Custom Operations: For specific tasks not covered by existing TensorFlow operations, consider developing custom ops. This can help in tailoring computations to your specific needs, potentially improving performance.

Each of these strategies can contribute to a more efficient and effective use of TensorFlow in quantitative development projects.

How to Display TensorFlow Skills on Your Resume

7. PyTorch

PyTorch is an open-source machine learning library widely used for deep learning applications, providing powerful tools for tensor computation with strong GPU acceleration, and a dynamic computational graph that supports flexible model development and training.

Why It's Important

PyTorch is important for a Quantitative Developer because it offers a flexible and intuitive framework for building and deploying complex machine learning models efficiently, enabling advanced quantitative analysis, financial modeling, and algorithmic trading strategies.

How to Improve PyTorch Skills

Improving your PyTorch skills as a Quantitative Developer involves a focus on optimization techniques, understanding advanced model architectures, and leveraging the extensive PyTorch ecosystem for efficient numerical computations and deep learning applications. Here are concise steps with relevant resources:

1. Master PyTorch Basics: Ensure a solid grasp of PyTorch fundamentals. PyTorch's official tutorials are an excellent starting point.

2. Understand Computational Graphs: Deepen your understanding of how PyTorch handles computations under the hood. This guide provides a clear explanation.

3. Optimize Model Performance: Learn optimization techniques specific to PyTorch to enhance model efficiency and performance. PyTorch's documentation on optimization offers insights into various optimizers and strategies.

4. Parallel Computing and Distributed Training: Leverage PyTorch's capabilities for parallel computing and distributed training to handle large datasets and complex models. This tutorial covers the basics.

5. Stay Updated with PyTorch Ecosystem: Explore and integrate tools and libraries from the PyTorch ecosystem like Torchvision, TorchText, and PyTorch Lightning for advanced functionalities. Regularly visiting PyTorch's ecosystem page can keep you updated.

6. Participate in PyTorch Community: Engage with the PyTorch community through forums, GitHub discussions, or PyTorch conferences. Sharing knowledge and challenges can offer new insights and solutions. The PyTorch Discussion Forum is a great place to start.

7. Practice on Real-World Projects: Apply your skills on quantitative finance projects or Kaggle competitions. Hands-on experience will significantly improve your proficiency. Check out Kaggle for datasets and challenges.

8. Advanced Model Architectures: Familiarize yourself with the latest model architectures relevant to your field (e.g., GANs for synthetic data generation in finance). The Papers With Code website is an invaluable resource for staying on top of recent advancements and their implementations.

By systematically following these steps and utilizing the provided resources, you can significantly enhance your PyTorch skills and apply them effectively in quantitative development projects.

How to Display PyTorch Skills on Your Resume

8. Machine Learning

Machine Learning is a subset of artificial intelligence that enables systems to learn and improve from experience without being explicitly programmed, primarily through the use of algorithms and statistical models to analyze and draw inferences from patterns in data. For a Quantitative Developer, it's a tool for developing predictive models and algorithms that can process large datasets to inform financial strategies, optimize portfolios, and manage risks.

Why It's Important

Machine Learning is important for a Quantitative Developer as it enables the development of sophisticated models for predicting market behaviors, optimizing trading strategies, and managing risk more effectively, leading to improved decision-making and profitability.

How to Improve Machine Learning Skills

Improving machine learning models as a Quantitative Developer involves several key strategies aimed at enhancing performance, accuracy, and efficiency. Here's a concise guide:

1. Data Quality and Preparation: Begin with high-quality, relevant data. Clean and preprocess your data to remove noise and handle missing values. Feature engineering is crucial for creating meaningful variables. Data Preprocessing Techniques

2. Feature Selection and Engineering: Use techniques like Principal Component Analysis (PCA) to reduce dimensionality and to identify the most relevant features. This can significantly improve model performance. Feature Selection Techniques

3. Model Selection: Experiment with different algorithms to find the one that best suits your data's characteristics. Remember, there's no one-size-fits-all in machine learning models. Choosing the Right Machine Learning Algorithm

4. Cross-Validation and Hyperparameter Tuning: Use cross-validation to assess how your model will generalize to an independent dataset. Tune your model's hyperparameters to optimize performance. Tools like GridSearchCV and RandomizedSearchCV can automate this process. Hyperparameter Tuning

5. Ensemble Methods: Improve model robustness and accuracy by combining the predictions of several models. Techniques like Bagging, Boosting, and Stacking are popular ensemble methods. Ensemble Learning

6. Regularization: To prevent overfitting, especially in models with high complexity, use regularization techniques like L1 (Lasso) and L2 (Ridge) regularization. Understanding Regularization

7. Evaluation Metrics: Use appropriate metrics to evaluate your model's performance, such as accuracy, precision, recall, F1 score for classification problems, and MSE, RMSE, MAE for regression problems. Adjust your model based on these metrics. Evaluation Metrics

8. Continuous Learning: Implement a system where your model can learn continuously from new data, adapting to changes and improving over time. Continuous Learning

9. Stay Updated and Collaborate: Machine learning is a rapidly evolving field. Keep learning through online courses, reading recent research papers, and collaborating with the community. Platforms like GitHub and Kaggle provide opportunities to work on real-world problems and learn from others' solutions.

By focusing on these areas, you can significantly improve the performance and accuracy of your machine learning models.

How to Display Machine Learning Skills on Your Resume

9. Statistical Analysis

Statistical analysis involves collecting, examining, summarizing, and interpreting data to discover patterns and underlying relationships, thereby enabling informed decision-making and predictions. For a Quantitative Developer, it's a fundamental tool for designing algorithms, financial models, and strategies based on quantitative data.

Why It's Important

Statistical analysis is crucial for a Quantitative Developer as it enables the identification of patterns, trends, and relationships within data, facilitating the development of models for prediction and optimization of financial strategies, ultimately leading to informed decision-making and enhanced investment performance.

How to Improve Statistical Analysis Skills

Improving statistical analysis, particularly for a Quantitative Developer, involves a multifaceted approach focusing on theoretical understanding, practical application, and continuous learning. Here’s a concise guide:

1. Strengthen Mathematical Foundations: Enhance your grasp of probability, statistics, and linear algebra. MIT's OpenCourseWare (Linear Algebra) and Stanford's Probability for Computer Scientists (Probability) are excellent resources.

2. Master Statistical Software: Become proficient in tools like R (R Project) and Python, particularly libraries such as NumPy, pandas, and SciPy. The Python Data Science Handbook (Python Data Science Handbook) is a valuable resource.

3. Learn Through Projects: Apply your knowledge to real-world datasets. Kaggle (Kaggle) provides datasets and competitions to hone your skills.

4. Stay Updated: Follow recent advancements and research in statistics and quantitative analysis. Journals like The American Statistician (The American Statistician) and preprint servers like arXiv (arXiv) are useful.

5. Optimize and Validate Models: Learn techniques for model validation and optimization, including cross-validation and bootstrap methods. The book "The Elements of Statistical Learning" (The Elements of Statistical Learning) covers these topics comprehensively.

6. Incorporate Machine Learning: Understand machine learning algorithms and their statistical foundations. "Machine Learning Yearning" by Andrew Ng (Machine Learning Yearning) offers insights into applying ML effectively.

7. Engage with the Community: Participate in forums such as Stack Overflow (Stack Overflow) and Cross Validated (Cross Validated) to discuss problems and solutions with peers.

Improving statistical analysis skills is an ongoing process that combines theoretical study, practical application, and engagement with the wider community.

How to Display Statistical Analysis Skills on Your Resume

10. QuantLib

QuantLib is an open-source software library that provides tools for quantitative finance, focusing on the modeling, trading, and risk management of financial instruments. It is written in C++ and offers extensive functionality for derivatives pricing and analysis, making it a valuable resource for quantitative developers in the finance industry.

Why It's Important

QuantLib is crucial for a Quantitative Developer as it provides a comprehensive, open-source library for quantitative finance, offering tools for modeling, trading, and risk management, thus enabling efficient development and implementation of complex financial algorithms and strategies.

How to Improve QuantLib Skills

Improving QuantLib, a free/open-source library for quantitative finance, requires a blend of programming skills, financial knowledge, and contributions to its ecosystem. Here's a concise guide for a Quantitative Developer:

1. Understand QuantLib's Architecture and Coding Standards: Familiarize yourself with QuantLib's design, coding practices, and contribution guidelines. Start with the QuantLib GitHub repository and the Developer's Guide.

2. Identify Areas for Improvement: Look for areas such as performance optimization, feature enhancements, bug fixes, or documentation updates. Engage with the community via the QuantLib mailing list to understand current needs and priorities.

3. Enhance Algorithms and Models: Optimize existing algorithms for better performance or introduce new models to extend the library's capabilities. Consider modern C++ features for efficiency and maintainability.

4. Contribute Code: Clone the repository, make your changes, and submit a pull request. Ensure your code adheres to the project's standards and has accompanying tests. The GitHub contribution guide outlines the process.

5. Improve Documentation: Good documentation helps users and developers alike. Whether it's clarifying existing features, adding examples, or documenting new contributions, your input is valuable. Follow the documentation guidelines when contributing.

6. Engage with the Community: Participate in discussions, provide support to others, and share your expertise. This collaborative effort not only helps improve QuantLib but also fosters a vibrant community.

By focusing on these areas, you can significantly contribute to the enhancement of QuantLib, making it more efficient, robust, and user-friendly for the quantitative finance community.

How to Display QuantLib Skills on Your Resume

11. Hadoop

Hadoop is an open-source framework designed for distributed storage and processing of large datasets across clusters of computers using simple programming models. It is particularly useful for handling big data analytics.

Why It's Important

Hadoop is crucial for a Quantitative Developer as it enables the efficient processing and analysis of large datasets across distributed computing environments, facilitating complex quantitative analyses and data-driven decision-making.

How to Improve Hadoop Skills

Improving Hadoop for a Quantitative Developer involves enhancing performance, ensuring data reliability, and optimizing resource management. Here's how:

1. Performance Tuning: Adjust Hadoop's configuration settings based on your workload. Utilize the Apache Hadoop Best Practices and Tuning Guide for optimizing memory, CPU, and disk usage.

2. High Availability (HA): Implement HA for critical components like HDFS and YARN to minimize downtime. Follow the Apache guide for setting up Hadoop High Availability.

3. Data Compression: Use data compression to increase storage efficiency and reduce I/O operations. Explore different codecs as discussed in Hadoop Compression.

4. Cost-Effective Scaling: Scale your cluster horizontally by adding more nodes or vertically by upgrading existing ones based on the workload analysis. Cloudera's guide offers insights on scaling.

5. Use YARN Tuning: Optimize YARN by tuning memory and CPU allocations for better resource management. The YARN Tuning Guide provides detailed configurations.

6. Advanced Analytics Tools: Integrate with tools like Apache Spark for faster in-memory processing and advanced analytics capabilities. The official Apache Spark website has resources and documentation.

7. Security Enhancement: Implement robust security measures, including Kerberos authentication and encryption. The Apache Hadoop Security guide covers the essentials.

8. Routine Maintenance and Monitoring: Use tools like Apache Ambari for cluster management and monitoring. Visit the Apache Ambari project page for more info.

Each of these steps can significantly improve the performance and reliability of Hadoop from the perspective of a Quantitative Developer, enhancing the overall efficiency of data processing and analysis workflows.

How to Display Hadoop Skills on Your Resume

12. Spark

Spark is a high-performance, distributed computing framework designed for large-scale data processing and analytics, offering powerful APIs in Python, Java, Scala, and R. It enables efficient data querying, analysis, and machine learning at scale.

Why It's Important

Spark is important for a Quantitative Developer because it provides a powerful platform for large-scale data processing and analytics, enabling efficient manipulation, analysis, and modeling of big data sets to derive insights, optimize strategies, and improve decision-making processes in a scalable and fast manner.

How to Improve Spark Skills

Improve Apache Spark performance by:

1. Optimizing Data Storage: Use Parquet for efficient storage and compression. Store data in a columnar format to enable faster querying and filtering.

2. Partitioning: Properly partition your data to ensure parallelism. Use repartition() or coalesce() based on your data shuffling needs to optimize resource usage.

3. Caching: Use persist() or cache() strategically for RDDs/DataFrames that are accessed frequently. Choose the right storage level based on your use case.

4. Broadcast Variables: Utilize broadcast variables to share large, read-only data efficiently across nodes instead of sending this data along with every task.

5. Tuning Spark Configuration: Adjust Spark configuration settings such as spark.executor.memory, spark.driver.memory, and spark.sql.shuffle.partitions based on your workload and cluster capacity.

6. Avoiding Operations that Cause Data Shuffling: Operations like join, groupBy, and repartition cause data to be shuffled across the nodes. When possible, minimize these operations or optimize their usage.

7. Using DataFrames and Datasets API: Leverage the Catalyst optimizer by using DataFrames and Datasets for structured data processing, which provides more efficient execution plans compared to RDDs.

8. Garbage Collection Tuning: Monitor and tune the JVM garbage collection processes to reduce pause times and improve efficiency.

For deeper insights and advanced techniques, refer to the following resources:

• Apache Spark Documentation
• Databricks Best Practices
• Apache Spark Performance Tuning – A Complete Guide

How to Display Spark Skills on Your Resume