Python for Data Scientists
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Intro to Python Continue
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Description: # Intro to Python Python is a simple, easy-to-learn, pseudo-code resembling programming language. It is rich with all the features of any object oriented language. Scientists and Mathematicians have been using python since its inception and hence is popular for analytical tasks. Python is also known for its brevity.  ## Why is Python a Favorite of Data Scientists?
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Python Advanced Continue
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Description: # Python Advanced A continuation of the previous lesson "Introduction to Python", here we introduce more advanced concepts. ## Operators
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Dataframes Continue
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Description: # Dataframes ## Dataframe Basics ### The Pandas Library
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SQL in Python. Continue
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Description: # SQL in Python. As mentioned in earlier lessons, Python is very flexible and has a wide range of libraries and third-party modules to support many operations. SQL (Structured Query Language) can be executed from within Python using sqlite3. The sqlite3 module offers support to connect to an external database and execute SQL queries. However, this module does not offer the complete querying capability of a typical SQL engine and functions as a light-weight API version of the querying engine. Other modules like MySQLdb (same as mysql-python), offer a more extensive range of functions and query processing abilities. We will be discussing sqlite3 module, as it is the widely used. Though it is a light-weight module, it supports almost all basic sql operations and can be implemented for a database of up to 140 Terabytes in size.
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Numpy Continue
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Description: # Numpy ## Arrays and Lists Before getting introduced to Numpy library, we need to be familiar with a very widely used data structure called 'array'. An array is a collection of homogenous variables. Here homogenous means variables of the same data type. And so an array can be a collection of integers (int datatypes), collection of fractions/decimal values (float datatypes) or a collection of characters (char datatype) also referred to as a string.
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Introduction to Statistics Continue
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Description: # Introduction to Statistics ## Mean, Median and Mode When working with a large data set, it can be useful to represent the entire data set with a single value that describes the "middle" or "average" value of the entire set. In statistics, that single value is called the central tendency and mean, median and mode are all ways to describe it.
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Linear Algebra - Basics Continue
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Description: # Linear Algebra - Basics # Introduction to Linear Algebra Linear algebra is a branch of mathematics that deals with equations of straight lines. A line is made up of multiple points. A point in 2 dimensional (2D) space is represented using two coordinates (x,y).
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Array Shape Manipulation Continue
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Description: # Array Shape Manipulation Sometimes a Data Analyst might be required to run shape manipulation algorithms on the data which needs to be changed in shape, dimensions or property so as to be merged or concatenated with another array. Therefore it is really important to know how to perform these fucntions in Python. Array shape manipulation is performed by the following functions -
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N-dimensional array in Python Continue
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Description: # N-dimensional array in Python An array is a list or collection of homogenous elements, i.e., same type of items. An N-dimensional array is a collection of such arrays, and in simplest terms can be described as an array of arrays. A two dimensional array, also called a matrix (plural: matrices), is very common and most of us would be familiar with it. An array of matrices can be visualized as a 3 dimensional array. An array can be defined using the '.array' method of the numpy module. A range of functions such as dtype, shape, size, etc., are available to find out about various attributes of the array.
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Data Visualization Continue
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Description: # Data Visualization ## Data Visualization ### Data Visualization
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