NumPy 🔥

It is a Linear Algebra Library for #Python, the reason it is so important for Data Science is that almost all of the libraries in the PyData Ecosystem rely on NumPy as one of their main building blocks👨‍🏫.

Here's everything you need🧵👇

#DataScience #100DaysOfCode
1⃣Numpy Arrays

NumPy arrays are the main way we use Numpy. Numpy arrays essentially come in two flavors: vectors and matrices. Vectors are strictly 1-d arrays and matrices are 2-d (but you should note a matrix can still have only one row or one column). Image
2⃣Built-in Methods

There are lots of built-in ways to generate Arrays
- zeros
- ones
- eye
- arange
- linspace Image
3⃣Random

Numpy also has lots of ways to create random number arrays:
- rand
- randn
- randint Image
4⃣Array Attributes and Methods

Let's discuss some useful attributes and methods of an array:
- shape
- reshape
- max
- min
- std
- var Image
5⃣Arithmetic

You can easily perform array with array arithmetic, or scalar with array arithmetic. Let's see some examples: Image
6⃣Universal Array Functions

Numpy comes with many universal array functions, which are essentially just mathematical operations you can use to perform the operation across the array. Let's show some common ones: Image
7⃣NumPy Indexing and Selection

- Bracket Indexing and Selection
- Indexing a 2D array (matrices) Image
For more visit the official site 👇

numpy.org
Credit for some of the above-mentioned code 👇

udemy.com/course/python-…
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More from @PiyalBanik

3 Jul
Want to learn Data Science but confused about where to start and what to follow?

Here are the ultimate 12 months Learning path to becoming a Data Scientist 👨‍🎓

Note: I'm personally following this roadmap

🧵👇

#DataScience #MachineLearning #100DaysOfCode #66DaysOfCode #Python
Since we're currently in July, so start from this month.

Understanding Data Science and getting started with Python
- what is data science?
- what does a data scientist do?
- find out various resources
- Set up the system
- Learn Python basics
- Introduction to Pandas & Numpy
August -

Mathematics, Statistics & SQL
- Linear Algebra
- Introduction to Probability
- Statistics - inferential & descriptive
- Exploratory Data Analysis
- SQL for Data science
- Projects on EDA and SQL

Start engaging in the Data Science & Machine Learning community
Read 14 tweets
3 Jul
Ever wondered how a Data Scientist thinks about a problem? Here are the major steps involved in solving a data science problem.

🧵👇

#DataScience #MachineLearning #100DaysOfCode #66DaysOfData
1. Business Understanding: We should have clarity of what is the exact problem we are going to solve.

What is the problem that we are trying to solve? - Asking the right questions as a Data Scientist starts with understanding the goal of the business.
2. Analytical Approach: How can we use data to answer the question? We should decide the analytical approach to follow which can be of 4 types
- Descriptive
- Statistical
- Predictive
- Prescriptive
and it indicates the necessary data content, formats, and sources to be gathered
Read 12 tweets
2 Jul
Best Data Science blogs to follow in 2021

🧵👇

#DataScience #66daysofdata #100DaysOfCode
1. Towards Data Science

TDS is a Medium publication having audience-oriented content about Data Science, along with blogs on related fields such as Machine Learning, Programming, Visualization, and Artificial Intelligence.

towardsdatascience.com
2. Data Science Central

DSC is one of the leading repositories of Data Science content that is regularly updated with the latest trends across domains such as Artificial Intelligence, Machine Learning, Deep Learning, Analytics, Big Data, and much more.

datasciencecentral.com
Read 9 tweets
19 Jun
Ever wondered how a Data Scientist thinks about a problem? Here are the major steps involved in tackling a data science problem.

Thread 🧵👇

#DataScience #MachineLearning #100DaysOfCode
1. Business Understanding: We should have clarity of what is the exact problem we are going to solve.

What is the problem that we are trying to solve? - Asking the right questions as a Data Scientist starts with understanding the goal of the business.
2. Analytical Approach: How can we use data to answer the question? We should decide the analytical approach to follow which can be of 4 types
- Descriptive
- Statistical
- Predictive
- Prescriptive
and it indicates the necessary data content, formats, and sources to be gathered
Read 12 tweets
18 Jun
Top 7 interesting careers related to Data Science to explore. Pick one and start learning.

Thread 🧵👇

#DataScience #ArtificialIntelligence #MachineLearning #BigData
1. Data Scientist

Data scientist use their analytical and technical capabilities to extract meaningful insight from data.
2. Machine Learning Engineer

Machine Learning engineer's final output is the working software, and their audience for this output consists of other software components that run automatically with minimal human supervision. The decisions are made by machines.
Read 9 tweets
18 Jun
Everything you need to know about Strings in Python for Data Science

Thread 🧵👇

#DataScience #Python #100daysofcodechallenge
📌Looping Through a String

Since strings are arrays, we can loop through the characters in a string, with a for loop.
📌String Length
To get the length of a string, use the len() function.

📌Check String
To check if a certain phrase or character is present in a string, we can use the keyword in.
Read 8 tweets

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