1. https://www.youtube.com/playlist?list=PLVlQHNRLflP8ojs1_66Q3txPQg5jemQrG
2. https://www.analyticsvidhya.com/blog/2020/04/the-ultimate-numpy-tutorial-for-data-science-beginners/
3. https://www.edureka.co/blog/python-numpy-tutorial/?ranMID=42536&ranEAID=a1LgFw09t88&ranSiteID=a1LgFw09t88-QXKWRouXddnlRPaf1NSmjQ&LSNSUBSITE=Omitted_a1LgFw09t88
4. https://www.datacamp.com/community/tutorials/python-numpy-tutorial
5. https://www.machinelearningplus.com/python/101-numpy-exercises-python/

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Let’s see how to learn Data science by self-study, what all you need to cover.

1. Select a programming language:

You can select any from :

1. Python
2. R
3. Java

My suggestion is you use Python or R because there are many libraries

Python is widely used and is a favorite tool along being a flexible and open-sourced language. Its massive libraries are used for data manipulation and are very easy to learn even for a beginner data analyst.

If you go with python here is list of topics you must learn:

1. what is python
2. How to define variables
3. Different kinds of data Structures: Lists, Tuples, Strings, Dictionaries
4. Functions
5. Exception Handling
6. OOP

2. Numpy:

NumPy (Numerical Python) is a linear algebra library in Python. It is a very important library on which almost every data science or machine learning

Topics you must take care:

1. Define Numpy
2. How to import NumPy
3. Creating arrays
4. Basic Operations: Indexing and Slicing
5. Indexing techniques
6. Inbuild functions

3. Pandas:

Pandas provide essential data structures like series, dataframes, and panels which help in manipulating data sets and time series.

It is free to use and an open-source library, making it one of the most widely used data science libraries in the world.

You need to learn pandas in depth

what are the different operations in Feature engineering, Scaling, Handling missing data.

4. Maths:

The amount of math you’ll need depends on the role. First, every data scientist needs to know some statistics and probability theory.

Learn Statistics: Mean, Median, Mode, Percentile, Distributions, learn use cases, know the answer “Why to use, why it is doing“.

Linear Algebra and Calculus can be learned while learning maths behind machine learning algorithms.

5. Matplotlib, Seaborn:

These are visualization libraries, understand how to use it

6. Hands-on Exploratory Data Analysis:

This is one of the important section

Exploratory Data Analysis or (EDA) is understanding the data sets by summarizing their main characteristics often plotting them visually

Learn how to get information from a dataset, do some projects on the same

7. Understanding Machine Learning:

Understand all the Algorithms and maths behind them, Try to implement using SKlearn, Play with parameters.

Find solutions to “ Why to use these Algorithms?” , “what is the difference between these algorithms?”

8. Understanding Deep Learning:

Understand all the Algorithms and maths behind them, there are mainly 3 important algorithms: ANN, RNN, CNN

understand where to use them, learn advanced technology of CNN, Transfer Learning.

10. Learn other libraries:

1. Tensorflow
2. Keras
3. Theano
4. PyTorch

11. Learn Databases:

SQL database: SQL

NOSQL database: MongoDB

12. Learn Visualization Tools:

Learn Some Visualization tools:

1. PowerBI
2. Qlik Sense
3. Tableau

I think this much will be enough to get a good knowledge of Data science and help you get a job

For more detailed learning you can read research papers, some good blogs.

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WHAT IS NUMPY LIBRARIES IN PYTHON

NumPy is the fundamental package for scientific computing in Python.

It provides a high-performance multidimensional array object and tools for working with these arrays.

NumPy arrays are called ndarray or N-dimensional arrays and they store elements of the same type and size. It is known for its high-performance and provides efficient storage and data operations as arrays grow in size.

CREATING AN NUMPY ARRAY

NumPy comes pre-installed when you download Anaconda. But if you want to install NumPy separately on your machine, just type the below command on your terminal:

pip install numpy

Now you need to import the library:

import numpy as np

Basic Array:

To create a very basic ndarray, you use the np.array() method

Arrays with Zero:

NumPy lets you create an array of all zeros using the np.zeros() method. All you have to do is pass the shape of the desired array:

Arrays with one:

You could also create an array of all 1s using the np.ones() method:

Random numbers in ndarray:

Another very commonly used method to create ndarrays is np.random.rand() method. It creates an array of a given shape with random values from [0,1):

An array of your choice:

you can create an array filled with any given value using the np.full() method. Just pass in the shape of the desired array and the value you want

Imatrix:

the method is np.eye() that returns an array with 1s along its diagonal and 0s everywhere else.

An Identity matrix is a square matrix that has 1s along with its main diagonal and 0s everywhere else. Below is an Identity matrix of shape 3 x 3.

Note: A square matrix has an N x N shape. This means it has the same number of rows and columns.

PROPERTIES OF ARRAY OBJECT

We can get info about an array:

1. Type: Datatype of element inside the array/matrix
2. Size: No of elements in array/matrix
3. Dimensions: Number of Dimensions I,e: 1d/2d/multiD
4. Shape: no of rows and no of columns

PROCESS THE ELEMENTS WITHIN THE ARRAY/MATRIX

GRAB SUBMATRIX FROM MATRIX USING SLICING

We can grab submatrix from the matrix using slicing below is the syntax:

[row_lower : row_upper, col_lower : col_lower]

Below is the Example:

CREATING 1d,2d,Nd ARRAY USING NDARRAY()

we can create any dimension of array by defining its Shape

here for 1d,2d I took input from the user but in 3d array i specified the value you can take input from the user just in a loop instead of arr[i][j][k]=val, you can specify arr[i][j][k]=int(input())

How does shape(2,3,3)works:

RESHAPE OF AN ARRAY

The reshape() function is used to give a new shape to an array without changing its data

Code on Github: https://github.com/kemypatel/Python_Coding_Exercises

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Numpy for Data Science was originally published in The Startup on Medium, where people are continuing the conversation by highlighting and responding to this story.

Elements in the dictionary are represented by {key : value} pair Separated by comma and enclosed in curly brackets{}

In this section we will learn:

1. Constructing a Dictionary
2. Accessing element from a dictionary
3. Nesting Dictionaries
4. Basic Dictionary Methods

CONSTRUCTING A DICTIONARY

ACCESSING ELEMENTS FROM A DICTIONARY

Elements in the dictionary are accessed using their keys

NESTING DICTIONARIES

Hopefully, you’re starting to see how powerful Python is with its flexibility of nesting objects and calling methods on them. Let’s see a dictionary nested inside a dictionary:

BASIC DICTIONARY METHODS

There are a few methods we can call on a dictionary. Let’s get a quick introduction to a few of them:

Code on Github: https://github.com/kemypatel/Python_Coding_Exercises

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In this section we will go through:

1. Constructing Tuples
2. Basic Tuple Methods
3. Immutability
4. When to Use Tuples

CONSTRUCTING TUPLES

The construction of a tuple use () with elements separated by commas. For example:

We can use indexing to access elements of a tuple

In tuple, Slicing is same as lists

BASIC TUPLE METHODS

Methods that add items or remove items are not available in the tuple. Only the following two methods are available.

1. count()
2. index()

IMMUTABLITY

It can’t be stressed enough that tuples are immutable. To drive that point home:

Because of this immutability, tuples can’t grow. Once a tuple is made we can not add or delete anything.

WHEN TO USE TUPLES

You may be wondering, “Why bother using tuples when they have fewer available methods?” To be honest, tuples are not used as often as lists in programming, but are used when immutability is necessary. If in your program you are passing around an object and need to make sure it does not get changed, then a tuple becomes your solution. It provides a convenient source of data integrity

Code on Github: https://github.com/kemypatel/Python_Coding_Exercises

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In this section we will see:

1. Creating Lists
2. Indexing and Slicing Lists
3. Basic List Methods
4. Nesting Lists
5. List Comprehensions

Code up on my GitHub

CREATING LISTS

Created a list of 4 elements of different types and assigned list with variable name st.

we can check the length of list i,e number of elements in a list

INDEXING AND SLICING LISTS

Indexing

Q: How to access elements of the list?

A: We can access the list element using their Index.

let’s take an example:

In Python Indexing of element starts from 0

so first element 50 is at index location 0, 80 is at index 1, and so on

Elements can also access by negative indexing in the list.

Slicing:

We can access a range of items in a list by using the slicing operator :

syntax : [start : stop : stepsize]

BASIC LISTS METHODS

append() — Add an element to the end of the list

extend() — Add all elements of a list to the another list

insert() — Insert an item at the defined index

remove() — Removes an item from the list

pop() — Removes and returns an element at the given index

clear() — Removes all items from the list

index() — Returns the index of the first matched item

count() — Returns the count of the number of items passed as an argument

sorted() — Sort items in a list in ascending order

reverse() — Reverse the order of items in the list

copy() — Returns a shallow copy of the list

NESTING LISTS

A nested list is a list within a list.

We can again use indexing to grab elements, but now there are two levels for the index. The items in the matrix object, and then the items inside that list!

LIST COMPREHENSION

Python has an advanced feature called list comprehensions. They allow us for the quick construction of lists.

syntax : new_list = [expression for item in list]

Code on Github: https://github.com/kemypatel/Python_Coding_Exercises

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Hey Guys, I will be sharing Ebooks related to Python, Machine Learning, Deep Learning, Statistics and probability, and many more.

1. Python workbook

2. Python for everybody

3. Introduction to Data

4. Machine Learning with Python

5. Python CookBook

6. Machine learning book

7. Python Crash Course

8. Pattern Recognition and Machine Learning

9. Machine Learning Yearning

10. Principles of Soft Computing

Github: https://github.com/kemypatel

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Python was designed by Guido van Rossum and first released in 1991.

Python is a cross-platform programming language, which means that it can run on multiple platforms like Windows, macOS, Linux, and has even been ported to the Java and .NET virtual machines. It is free and open-source.

Now no more introduction let’s directly jump to programming

Your first Python Program

Write the below code and run

Congratulations you just wrote your first python code.

Python Datatypes

There are various types of DataTypes here are some important:

1. Numbers
2. Lists
3. Tuple
4. Strings
5. Set
6. Dictionaries

NUMBERS

There are mainly three types of number:

1. Integers: Positive or negative whole numbers
2. Floating Point: Float, or “floating-point number” is a number, positive or negative, containing one or more decimals.
3. Complex: Complex numbers are written with a “j” as the imaginary part

To check the type, we use type() function

LISTS

The list is an ordered sequence of items. It is one of the most used datatype in Python and is very flexible. All the items in a list do not need to be of the same type.

Elements in the list are separated by a comma enclosed in square brackets [].

TUPLES

The tuple is an ordered sequence like the list we can store elements of different datatypes. The only difference is that tuples are immutable i.e, Tuples once created cannot be modified.

Elements in Tuples are separated by a comma and enclosed in round brackets()

STRINGS

A string is a sequence of Unicode Characters We can use single quotes or double quotes to represent strings. Multi-line strings can be denoted using triple quotes “ “ “ or ‘ ‘ ‘.

SET

Set is a collection of unordered, unindexed, and unique items.

Elements in sets are separated by a comma and enclosed within curly brackets{}

DICTIONARIES

Dictionaries are an unordered collection of key-value pairs.

Elements in the dictionary are represented by {key : value} pair Separated by comma and enclosed in curly brackets{}

Code on Github: https://github.com/kemypatel/Python_Coding_Exercises

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Machine Learning is a subfield of Artificial Intelligence.

Definition:

1. Arthur Samuel: Machine Learning is the field of study that gives the computers the ability to learn without being explicitly programmed
2. Tom Mitchell: A Computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.

Examples of Machine Learning :

Here are some machine learning examples in day to day life:

1. Facebook’s Face Recognition System
2. Spam Detection in Gmail
3. Netflix’s Movie Recommendation System
4. Amazon’s Product Recommendation
5. Handwriting Recognition
6. Search Engine Result Refining
7. Speech Recognition
8. Image Recognition
9. Diagnosis of Diseases
10. Google Maps

Types of Machine Learning:

There are mainly 3 types of Machine Learning Algorithms:

1. Supervised Learning
2. Unsupervised Learning
3. Reinforcement Learning

Details Explanation of types will be in the next blog

Happy Learning