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If you're passionate about becoming a data expert, it's essential to maintain curiosity and continuously explore, learn, and ask questions. While online tutorials and video courses can help you take your first steps, the most effective way to become a true data professional is by mastering the tools you already use in your production environment.
We spoke with real data experts and compiled a list of seven Python tools that every data professional should be familiar with. The Galvanize Data Science and GalvanizeU courses are designed to immerse students in these technologies, allowing them to gain deep expertise through hands-on experience. When you're applying for your first job, this kind of in-depth knowledge will give you a significant advantage. Let’s take a closer look at these essential tools:
**IPython**
IPython is an interactive command-line shell that supports multiple programming languages. Originally developed in Python, it offers enhanced introspection, rich media support, extended syntax, tab completion, and a powerful history system. IPython provides features such as:
- A powerful Qt-based terminal for interactive computing
- A browser-based notebook that supports code, text, math, and visualizations
- Support for interactive data visualization and GUI tools
- A flexible, embeddable interpreter for integration into projects
- An easy-to-use tool for parallel computing
Provided by Nir Kaldero, Director of Data Analysis at Galvanize.
**GraphLab Create**
GraphLab Create is a Python library powered by a C++ engine that enables fast development of large-scale data products. It allows users to analyze terabytes of data interactively on their computers and supports various data types like tables, images, and text. Key features include:
- Interactive analysis of large datasets
- Unified platform for diverse data types
- Advanced machine learning algorithms, including deep learning and factorization machines
- Compatibility with Hadoop YARN and EC2 clusters
- Flexible API for task and model development
- Cloud-based predictive services for deployment
Provided by Benjamin Skrainka, Data Scientist at Galvanize.
**Pandas**
Pandas is an open-source library licensed under BSD, offering high-performance data structures and analysis tools for Python. It fills a gap in Python’s data handling capabilities, making it easier to process and analyze data without switching to other languages like R. Pandas integrates well with IPython and other libraries, providing a robust environment for data analysis. While it doesn’t handle advanced modeling, it works seamlessly with tools like statsmodels and scikit-learn.
Provided by Nir Kaldero, Data Scientist at Galvanize.
**PuLP**
PuLP is a linear programming model written in Python that allows users to define and solve optimization problems. It can generate linear files and interface with solvers like GLPK, COIN CLP/CBC, CPLEX, and GUROBI to find optimal solutions.
Provided by Isaac Laughlin, Data Scientist at Galvanize.
**Matplotlib**
Matplotlib is a 2D plotting library for Python that produces publication-quality figures. It is widely used in scripts, interactive environments, and web applications. With just a few lines of code, you can create charts, histograms, scatter plots, and more. Its pyplot module provides a MATLAB-like interface, while advanced users can customize every aspect of their plots.
Contributed by Mike Tamir, Chief Scientific Officer at Galvanize.
**Scikit-Learn**
Scikit-Learn is a simple and efficient tool for data mining and data analysis. Built on NumPy, SciPy, and Matplotlib, it offers a wide range of functionalities, including classification, regression, clustering, dimensionality reduction, and model selection. It is open source and commercially available, making it a go-to choice for many data scientists.
Provided by Isaac Laughlin, Data Scientist at Galvanize.
**Spark**
Apache Spark is a distributed computing framework that processes large datasets efficiently. It uses resilient distributed datasets (RDDs) to enable parallel processing across clusters. Spark also supports shared variables like broadcast variables and accumulators, which help optimize performance in distributed tasks.
Provided by Benjamin Skrainka, Data Scientist at Galvanize.
If you want to dive deeper into data science, check out our data science giveaway to get tickets to events like PyData Seattle and the Data Science Summit, or enjoy discounts on Python resources like *Effective Python* and *Data Science from Scratch*.