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If you're aiming to become a data expert, it's essential to maintain a curious mindset and always explore, learn, and ask questions. While online tutorials and video resources can help you take your first steps, the most effective path is to become a true data professional by mastering the tools you already use in your production environment.
We consulted real data experts and identified 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, giving them hands-on experience. When you're applying for your first job, the deep understanding you gain from investing time in these tools 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 feature. IPython provides:
- A powerful Qt-based terminal for interactive computing
- A browser-based notebook that supports code, text, math formulas, and visualizations
- Support for interactive data visualization and GUI tools
- A flexible and embeddable interpreter that can be integrated into any project
- An efficient 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 allows for fast development of large-scale, high-performance data products. It enables users to analyze data in the terabyte range on their own machines and supports various data types like tables, images, and text. Key features include:
- Interactive analysis of large datasets
- Support for machine learning algorithms such as deep learning and factorization machines
- Compatibility with Hadoop YARN or EC2 clusters
- Flexible APIs for task automation and machine learning
- Easy deployment of predictive services on the cloud
Provided by Benjamin Skrainka, a data scientist at Galvanize.
**Pandas**
Pandas is an open-source Python library licensed under BSD, offering high-performance data structures and analysis tools. While Python has long been used for data manipulation, it lacked robust data analysis capabilities until the introduction of Pandas. This tool fills that gap, allowing users to handle all their data within Python without switching to other languages like R.
It integrates well with IPython and other libraries, providing a powerful environment for data analysis. However, it doesn't cover advanced modeling techniques, which are better handled by tools like Statsmodels or Scikit-learn.
Provided by Nir Kaldero, a data expert at Galvanize.
**PuLP**
PuLP is a linear programming model written in Python. It allows users to create linear problems and solve them using optimized solvers such as GLPK, COIN CLP/CBC, CPLEX, and GUROBI.
Provided by Isaac Laughlin, a data scientist at Galvanize.
**Matplotlib**
Matplotlib is a 2D plotting library for Python, capable of generating publication-quality figures. It works across multiple platforms and can be used in scripts, interactive shells, web applications, and GUI toolkits.
With just a few lines of code, you can generate charts, histograms, scatter plots, and more. For advanced customization, Matplotlib offers both object-oriented and MATLAB-like interfaces.
Contributed by Mike Tamir, Chief Scientific Officer at Galvanize.
**Scikit-Learn**
Scikit-Learn is a simple yet powerful library for data mining and data analysis. Built on NumPy, SciPy, and Matplotlib, it provides tools for classification, regression, clustering, dimensionality reduction, model selection, and preprocessing.
It is available under an open-source BSD license and is also commercially usable.
Provided by Isaac Laughlin, a data science lecturer at Galvanize.
**Spark**
Apache Spark is a distributed computing framework that processes data across clusters. Its core component, RDDs (Resilient Distributed Datasets), allows for efficient parallel processing. Spark also supports shared variables, such as broadcast variables and accumulators, to enhance performance in distributed tasks.
Provided by Benjamin Skrainka, a data scientist at Galvanize.
If you're interested in diving deeper into data science, check out our data science giveaway to get tickets for events like PyData Seattle and the Data Science Summit, or take advantage of discounts on Python resources such as *Effective Python* and *Data Science from Scratch*.