66% OFF OFFER Dell Ms116 275-BBCB Optical w ired Mouse at just 219/- Data science consulting companies are a hot choice if you’re looking for a job in the field. They offer numerous development opportunities, access to the latest technologies, and provide data-based solutions for top-notch companies across the globe. Furthermore, on top of generous salaries, they seem to have tons of cool perks – from unlimited vacation days and free meals to hair salons and masseuses on site. This doesn’t make your choice any simpler, though. With so many industries and companies out there, it’s hard to keep track of who-offers-what-and-where. So, watch this video to find out which companies provide the best overall employee experience in 2020! Freshers for Data Science Roles: Mu Sigma, Fractal Analytics, Exponentia, Clover Infotech,...
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Learn Data Science in Step by Step using Python STEP 1:Learn Python 1.1 Install python 1.2 Install pycharm
CONCEPTS: Variables,Numbers,string,list,ifstatement,forloop,Functions,Dictionaries,Install Python modules,modules,working with JSON,Exceptional Handling,Classes and Objects
VARIABLES: A Python variable is a reserved memory location to store values. In other words, a variable in a python program gives data to the computer for processing. Every value in Python has a datatype. Different data types in Python are Numbers, List, Tuple, Strings, Dictionary, etc.
NUMBERS: Python supports integers, floating-point numbers and complex numbers. They are defined as int , float and complex class in Python. Integers and floating points are separated by the presence or absence of a decimal point.
STRING: Stringsare arrays of bytes representing Unicode characters. However, Python does not have a character data type, a single character is simply a string with a length of 1. Square brackets can be used to access elements of the string.
LIST:
Lists are just like the arrays, declared in other languages. Lists need not be homogeneous always which makes it the most powerful tool in Python. A single list may contain DataTypes like Integers, Strings, as well as Objects. Lists are mutable, and hence, they can be altered even after their creation.
List in Python are ordered and have a definite count. The elements in a list are indexed according to a definite sequence and the indexing of a list is done with 0 being the first index. Each element in the list has its definite place in the list, which allows duplicating of elements in the list, with each element having its own distinct place and credibility
IF STATEMENT:
Decision-making statements in programming languages decides the direction of the flow of program execution. Decision-making statements available in python are:
FUNCTIONS: A function is a block of code that only runs when it is called. You can pass data, known as parameters, into a function. A function can return data as a result.
DICTIONARIES: Dictionary in Python is an unordered collection of data values, used to store data values like a map, which unlike other Data Types that hold only single value as an element, Dictionary holds key:value pair. Key value is provided in the dictionary to make it more optimized.
INSTALL PIP:
MODULES:
A module allows you to logically organize your Python code. Grouping related code into a module makes the code easier to understand and use. A module is a Python object with arbitrarily named attributes that you can bind and reference.Simply, a module is a file consisting of Python code. A module can define functions, classes, and variables. A module can also include runnable code.
WORKING WITH JSON: The full-form of JSON is JavaScript Object Notation. It means that a script (executable) file which is made of text in a programming language, is used to store and transfer the data. Python supports JSON through a built-in package called json. To use this feature, we import the json package in Python script. The text in JSON is done through quoted-string which contains a value in key-value mapping within { }. It is similar to the dictionary in Python. JSON shows an API similar to users of Standard Library marshal and pickle modules and Python natively supports JSON features.
EXCEPTIONAL HANDLING: Like other languages, python also provides the runtime errors via exception handling method with the help of try-except. Some of the standard exceptions which are most frequent include IndexError, ImportError, IOError, ZeroDivisionError, TypeError. The exception is the base class for all the exceptions in python.
CLASSES AND OBJECTS: Objects are an encapsulation of variables and functions into a single entity. Objects get their variables and functions from classes. Classes are essentially a template to create your objects
66% OFF OFFER Dell Ms116 275-BBCB Optical w ired Mouse at just 219/- Data science consulting companies are a hot choice if you’re looking for a job in the field. They offer numerous development opportunities, access to the latest technologies, and provide data-based solutions for top-notch companies across the globe. Furthermore, on top of generous salaries, they seem to have tons of cool perks – from unlimited vacation days and free meals to hair salons and masseuses on site. This doesn’t make your choice any simpler, though. With so many industries and companies out there, it’s hard to keep track of who-offers-what-and-where. So, watch this video to find out which companies provide the best overall employee experience in 2020! Freshers for Data Science Roles: Mu Sigma, Fractal Analytics, Exponentia, Clover Infotech,...
Make sure you understand that Data Scientist and Data Engineer are not the same thing. A Data Scientist builds models using mathematics, statistics and machine learning to explain and predict complex behavior, and codifies those models into real-world software. A Data Engineer designs and builds data architectures for ingestion, processing, and surfacing data for large-scale data-intensive applications. Often the Data Scientist and Data Engineer will work together to build an end-to-end solution for companies requiring advanced analytical models that are operationalized at scale. The Data Scientist is interested in large scale architecture only insomuch as it allows the "science to scale." Thus any Big Data project should have a Data Scientist alongside the Data Engineer to ensure that what gets built is analytically sound (no point in engineering a big data architecture that doesn't prepare and process data in a way that supports the specific models built by ...
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