# Data Science

Intro to Data Structures — pandas 0.17.1 documentation. We’ll start with a quick, non-comprehensive overview of the fundamental data structures in pandas to get you started.

The fundamental behavior about data types, indexing, and axis labeling / alignment apply across all of the objects. To get started, import numpy and load pandas into your namespace: Grouping &amp; Summarizing Data in R. Tools for making latex tables in R. Simple But Powerful Excel Tricks for Analyzing Data. Introduction I’ve always admired the immense power of Excel. This software is not only capable of doing basic data computations, but you can also perform data analysis using it. It is widely used for many purposes including the likes of financial modeling and business planning.

It can become a good stepping stone for people who are new to the world of data analysis. Even before learning R or Python, it is advisable to have knowledge of Excel. It has a few drawbacks as well. I feel fortunate that my journey started with Excel. Note: If you think you are a master coder in data science, you won’t find this article useful. Commonly used functions 1. Syntax: =VLOOKUP(Key to lookup, Source_table, column of source table, are you ok with relative match?) For above problem, we can write formula in cell “F4” as =VLOOKUP(B4, \$H\$4:\$L\$15, 5, 0) and this will return the city name for all the Customer id 1 and post that copy this formula for all Customer ids. 2. 3.

Cheatsheet - 11 Steps for Data Exploration in R (with codes) The Mod Function. What has modular arithmetic got to do with the real world?

The answer any experienced programmer should give you is "a lot". Not only is it the basis for many an algorithm, it is part of the hardware. Many programmers are puzzled by the mod, short for modulo, and integer division functions/operators found in nearly all languages. Modular arithmetic used to be something that every programmer encountered because it is part of the hardware of every machine. You find it in the way numbers are represented in binary and in machine code or assembly language instructions. Once you get away from the representation of numbers as bit strings and arithmetic via registers then many mod and remainder operations lose their immediate meaning so familiar to assembly language programmers. Look for These 7 Characteristics Before Hiring a Data Scientist. Data is being collected in droves, but most of the time, people don’t know what to do with it.

That’s why data scientists are hot commodities in the startup world right now. In fact, between 2003 and 2013, employment in data industries grew about 21 percent -- nearly 16 percent more than overall employment growth. It’s a fairly new concept, but these people are so valuable because they understand the significance of data for your business and how you can use it. Using analytics, firms can discover patterns and stories in data, build the infrastructure needed to properly collect and store it, inform business decisions and guide strategy.

Access to sufficient and robust data is vital to sustained startup growth. CheatSheet on Data Exploration using Pandas in Python. If some one would ask me to mention 2 most important libraries in Python for data science, I’ll probably name “pandas” and “scikit-learn”.

Pandas for the capability to read datasets in DataFrames, exploring and making them ready for modeling / machine learning and Scikit-learn for actually learning from these features created in Pandas. While there are quite a few cheat sheets to summarize what scikit-learn brings to the table, there isn’t one I have come across for Pandas. Hence, we thought of creating a cheat sheet for common data exploration operations in Python using Pandas.