If you are interested in being a data expert, you should keep a curiosity and always explore, learn, and ask questions. Online tutorials and video tutorials can help you get out of the first step, but the best way to do this is to be a true data expert by becoming familiar with the tools you already use in your production environment. I consulted our real data experts and collected seven Python tools that they thought all data experts should have. The Galvanize Data Science and GalvanizeU courses focus on allowing students to spend a lot of time immersing themselves in these technologies. When you are looking for your first job, the in-depth understanding of the tools you have gained in the time you invest will give you a bigger advantage. Let's get to know them: IPython IPython is a command-line shell for interactive computing between multiple programming languages. It was originally developed in Python and provides enhanced introspection, rich media, extended shell syntax, tab completion, and rich history. IPython provides the following features: Stronger interactive shell (Qt-based terminal) A browser-based notepad with support code, plain text, math formulas, built-in charts and other rich media Support interactive data visualization and graphical interface tools Flexible, embeddable interpreter loaded into any own project Easy to use, high performance tool for parallel computing Provided by Galvanize expert Nir Kaldero, Director of Data Analysis. GraphLab Greate GraphLab Greate is a Python library supported by the C++ engine that quickly builds large, high-performance data products. Here are some of the features of GraphLab Greate: The amount of data in units of T can be analyzed at an interactive speed on your computer. Table data, curves, text, and images can be analyzed on a single platform. The latest machine learning algorithms include deep learning, phylogenetic tree and factorization machines theory. You can run the same code on your laptop or distribution system using Hadoop Yarn or EC2 clustering. Focus on tasks or machine learning with flexible API functions. Easily configure data products with predictive services on the cloud. Create visual data for exploration and product monitoring. Provided by Galvanize data scientist Benjamin Skrainka. Pandas Pandas is an open source software with an open source license for BSD that provides high performance, easy-to-use data structures and data analysis tools for the Python programming language. Python has long been known for data changes and data preprocessing, but Python is a shortcoming in data analysis and modeling. Pands software fills this gap, allowing you to easily process all your data in Python without having to switch to a more mainstream professional language, such as the R language. Integrating the best-in-class IPyton toolkit and other libraries, its development environment for data analysis in Python delivers outstanding performance, speed, and compatibility. Pands does not perform important modeling functions beyond linear regression and panel regression; for these, refer to the statsmodel statistical modeling tool and the scikit-learn library. In order to make Python a top statistical modeling and analysis environment, we need to work harder, but we have been struggling on this path. Provided by Galvanize expert, data scientist Nir Kaldero. PuLP Linear programming is an optimization in which an object function is minimally limited. PuLP is a linear programming model written in Python. It can generate linear files and can call highly optimized solvers, GLPK, COIN CLP/CBC, CPLEX, and GUROBI to solve these linear problems. Provided by Galvanize data scientist Isaac Laughlin Matplotlib Matplotlib is a Python-based 2D (data) drawing library that produces (outputs) a publication-quality chart for a variety of paper-based original formats and cross-platform interactive environments. Matplotlib can be used in both python scripts, python and ipython shell interfaces (ala MATLAB® or Mathematica®), web application servers, and six types of GUI toolkits. Matplotlib tries to make things easier and make difficult things possible. You only need a few lines of code to generate charts, histograms, power spectra, histograms, errorcharts, scatterplots, and more. To simplify data plotting, pyplot provides an interface to MATLAB-like interfaces, especially when used with IPython. For advanced users, you can fully customize including line styles, font attributes, coordinate attributes, etc., with an object-oriented interface, or a MATLAB user-like (MATLAB) interface. Contributed by Mike Tamir, Chief Scientific Officer of Galvanize. Scikit-Learn Scikit-Learn is a simple and effective data mining and data analysis tool (library). Most notably, it is available to everyone and reused in multiple contexts. It is built on top of NumPy, SciPy and mathplotlib. Scikit uses an open source BSD license agreement and is also commercially available. Scikit-Learn has the following features: Classification – identifies which category an object belongs to. Regression – predicts the continuous value attribute of an object association Clustering – automatic grouping of similar objects Dimensionality Reduction – reducing the number of random variables to consider Model Selection – compare, validate, and select parameters and models Preprocessing – feature extraction and normalization Galvanize, Data Science Lecturer, provided by Isaac Laughlin Spark Spark consists of a driver that runs the user's main function and performs multiple parallel operations on the cluster. The most appealing aspect of Spark is its flexible distributed data set (RDD), which is a collection of elements partitioned by clustered nodes that can be used in parallel computing. RDDs can be created from a file in a Hadoop file system (or another Hadoop-backed file system file) or other existing scalar data collection in the driver. Users may want Spark to permanently store RDDs in memory to effectively reuse RDDs through parallel operations. In the end, RDDs cannot be automatically restored from the node. The second attraction in Spark is the sharing of variables in parallel operations. By default, when Spark runs a function in parallel as a task on a different set of nodes, it copies a copy of the variables used in each function to each task. Sometimes a variable needs to be shared by many tasks and drivers. Spark supports two ways of sharing variables: broadcast variables, which can be used to cache data on all nodes. The other way is the accumulator, which is a variable that can only be used to perform additions, such as in counters and additions. Provided by Galvanize data scientist Benjamin Skrainka. If you would like to learn more about data science, please go to our project our data science giveaway to get tickets for data seminars: such as PyData Seattle and Data Science Summit, or get discounts on Python resources like: Effective Python and Data Science from Scratch.
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