DEV Community: Karthik Bhandary

Roadmap to becoming a DevOps Engineer with Resources

Karthik Bhandary — Wed, 01 Jun 2022 12:15:49 +0000

In this blog, I am going to be sharing the resources for you to become a DevOps engineer that will also be helpful in becoming or getting a job as SRE/Cloud Engineer.

WHAT DOES A DEVOPS ENGINEER DO?
In brief, A DevOps engineer introduces new processes, tools and methodologies to maintain a balance in software production from coding to deployment. He/She also makes sure that the changes made to the project are in a single machine for efficient maintenance. He/She makes sure that the software expands along with the data getting added by the customers. Monitoring the feedback from the customers is also done.

Now we know what a DevOp does. Before getting into the roadmap let me just say that

THIS MIGHT TAKE ANYWHERE FROM 3 TO 6 MONTHS

Before you get discouraged about getting started or feeling like quitting, let me tell you that it takes the same time for any technology to learn! So don’t quit and just bear with it.

THE RESOURCES
I included the things you need to learn along with their links for resources

Programming:
Python: https://www.youtube.com/watch?v=t8pPdKYpowI

Golang: https://www.youtube.com/watch?v=YS4e4q9oBaU

Networking:
Link: https://www.youtube.com/watch?v=IPvYjXCsTg8&list=PL9gnSGHSqcnoqBXdMwUTRod4Gi3eac2Ak&index=4

Terminal:
Bash Script: https://www.youtube.com/watch?v=e7BufAVwDiM

Link 2: https://www.youtube.com/watch?v=2PGnYjbYuUo

VIM: https://www.youtube.com/watch?v=RZ4p-saaQkc

Linux:
Linux: https://www.edx.org/course/introduction-to-linux

Devops basics: https://www.youtube.com/watch?v=Wvf0mBNGjXY&t=0s

Ccna: https://www.youtube.com/watch?v=rv3QK2UquxM&t=0s

Git:
Link 1: https://www.youtube.com/watch?v=apGV9Kg7ics

YAML:
Link 1: https://www.youtube.com/watch?v=IA90BTozdow

Or link 2: https://www.youtube.com/watch?v=GOk4IoYhM9U

Containers — Docker:
Docker : https://www.youtube.com/watch?v=9zUHg7xjIqQ

Link 2: https://www.youtube.com/watch?v=GwXLNAcHk-k

Link 3: https://www.youtube.com/watch?v=KINjI1tlo2w

Link 4: https://aurelievache.gumroad.com/l/understanding-docker-visual-way

Link 5: https://iximiuz.com/en/categories/?category=Containers

Continuous Integration & Continuous Deployment (CI/CD):
Link 1: https://www.youtube.com/watch?v=3a8KsB5wJDE

Link 2: https://codefresh.learnworlds.com

Cloud:
AWS: https://www.youtube.com/watch?v=SOTamWNgDKc&t=0s

Container Orchestration — Kubernetes:
Link 1: https://www.youtube.com/watch?v=X48VuDVv0do

Link 2: https://kube.academy

Link 3: https://www.civo.com/academy

Link 4: https://www.edx.org/course/introduction-to-kubernetes

Link 5: https://saiyampathak.gumroad.com/l/cksbook

Monitoring:
Link 1: https://www.youtube.com/watch?v=7gW5pSM6dlU

Link 2: https://www.youtube.com/watch?v=cF2P9d7rBlg

Link 3: https://www.youtube.com/watch?v=B5UY-qeW96I

Link 4: https://www.youtube.com/watch?v=gS_nHTWZEJ8

Link 5: https://www.youtube.com/watch?v=aMZoUIG-mgY&t=12s

Infrastructure Provisioning & Configuration Management (IaC):
Link 1: https://www.edx.org/course/introduction-to-service-mesh-with-linkerd

Link 2: https://www.youtube.com/watch?v=SLB_c_ayRMo

Link 3: https://www.youtube.com/watch?v=9Ua2b06oAr4

Link 4: https://www.youtube.com/watch?v=V4waklkBC38

Few more useful links:
How to become a DevOps in 6 months: https://spacelift.io/blog/how-to-become-devops-engineer

https://roadmap.sh/devops

https://www.youtube.com/channel/UCjfE5EmNU1kyf1wwrtYlnGQ

https://www.youtube.com/c/Thetips4you

https://www.commclassroom.org/devops?r_done=1

https://www.youtube.com/watch?v=ZbG0c87wcM8&list=PL9gnSGHSqcnoqBXdMwUTRod4Gi3eac2Ak&index=1

Best Websites to learn DevOps:
https://kodekloud.com

https://acloudguru.com
https://www.upgrad.com/devops-certification-pgc-iiitb/

https://intellipaat.com/devops-architect-masters-training-program/
https://codedamn.com (coming soon)
https://www.guvi.in/mlp/devops-program

If I missed any topics please comment them down and if you know any other resources please share them. It will be very helpful for others who want to learn. Follow me on LinkedIn

Roadmap And Resources To Become a Full Stack Developer

Karthik Bhandary — Wed, 01 Jun 2022 12:06:53 +0000

Wedon’t have a day without using the Internet. Without Websites, There would be nothing on the Internet. In this blog, I am going to be sharing with you the Roadmap and the Resources that you require to become a Full Stack Developer.

Usually, web developers do either the Front end or Back end. But nowadays companies are looking for people who can do all this. If you are trying to become a Full Stack Web Developer then this article is for you!!

ROADMAP AND RESOURCES
Things you need to know

Basics

HTML
CSS
JavaScript
SQL
MERN Stack

React
NodeJS
ExpressJS
MongoDB
Mobile Dev

React Native (works for both android and ios)

Full Stack Development:

Full-stack MERN: https://www.udemy.com/course/the-web-developer-bootcamp/

https://www.udemy.com/course/the-complete-web-development-bootcamp/

Extra Practise
For java script practice: https://www.udemy.com/course/javascript-web-projects-to-build-your-portfolio-resume/

For react practice: https://www.udemy.com/course/complete-react-developer-zero-to-mastery/

Extra topics: https://www.udemy.com/course/the-complete-junior-to-senior-web-developer-roadmap/

Mobile Development:
React native for app dev: https://www.udemy.com/course/react-native-the-practical-guide/

Good to have:
Canva(design): https://www.youtube.com/watch?v=un50Bs4BvZ8
https://www.udemy.com/course/complete-web-designer-mobile-designer-zero-to-mastery/
Blockchain Development:
https://www.udemy.com/course/ethereum-and-solidity-the-complete-developers-guide/

Useful websites:
Medium.com
https://dev.to/
Udemy.com
https://www.freecodecamp.org/ (free)
https://www.appacademy.io/course/app-academy-open (free)
https://www.youtube.com/user/TechGuyWeb
https://www.youtube.com/watch?v=pQN-pnXPaVg
https://fullstackopen.com
https://scrimba.com
https://upskillcourses.com/courses
https://codedamn.com
Get Clients with these Freelance Websites:

Upwork
Freelancer
Fiverr
99designs
Toptal
Guru
PeoplePerHour
Dribbble
LinkedIn
DesignHill
Twine

I hope that this will be helpful for you guys. If I missed something, please share that and its resource in the comments, it will be much appreciated as it. Follow me on LinkedIn

Roadmap And Resources To Become a Graphic Designer

Karthik Bhandary — Wed, 01 Jun 2022 12:01:03 +0000

Have you ever seen a logo and wondered — Why is it so good and at the same time very much unique? How did they come up with it? It may have crossed your mind which is not so uncommon and might have wanted to do something similar but don't know how to, I will tell you how you can do it.

Well, they are that good because of Graphic Designers. They are the ones who design the logos for companies and businesses. And if you are looking to be one, then you are in the right place because I am going to be sharing with you the roadmap and the resources that are necessary for you to become a Graphic Designer.

I will make this clear at the beginning itself.

Graphic Designers don’t use any coding

But it is good to have a little knowledge of HTML, CSS, and JS. With that said let’s move on to the roadmap and the resources.

ROADMAP AND RESOURCES
In graphic design, there are few things that you need to Learn :

Line
Shape
Color Theory
Texture
Typography
Space
Image
Logo Design

You can search for each of the topics and learn for free on youtube.

Courses you need to do:
https://www.udemy.com/course/graphic-design-theory-for-beginners-course/

https://www.udemy.com/course/graphic-design-masterclass-everything-you-need-to-know/

https://www.udemy.com/course/graphic-design-masterclass-the-next-level/

https://www.coursera.org/specializations/graphic-design

Softwares you need to learn:
Photoshop — https://www.udemy.com/course/adobe-photoshop-cc-essentials-training-course/

Adobe Illustrator — https://www.udemy.com/course/adobe-illustrator-course/

After Effects for motion graphics — https://www.udemy.com/course/motion-graphics-secrets/

https://www.udemy.com/course/motion-design-with-figma-animations-motion-graphics-uxui/

UI/UX — https://www.udemy.com/course/complete-web-designer-mobile-designer-zero-to-mastery/

Good to have (this will give you an edge over other designers):
AdobeXD — https://www.udemy.com/course/ui-ux-web-design-using-adobe-xd/

InDesign — https://www.udemy.com/course/indesign-tutorial-basics-course/

Figma — https://www.udemy.com/course/figma-ux-ui-design-user-experience-tutorial-course/

Canva — https://www.udemy.com/course/canva-master-course-graphic-design-for-beginners/

Premier pro (video editing) — https://www.udemy.com/course/adobe-premiere-pro-cc-essentials-training-course/

Aseprite for pixel art — https://www.udemy.com/course/pixel-art-master-course/

Procreate (for iPad) — https://www.udemy.com/course/procreate-masterclass/

*Blender for animation *— https://www.udemy.com/course/blendertutorial/

Best Websites to learn:
Pluralsight for graphic design
Domestika for graphic design/(UI/UX)
Udemy.com
Hack-Design.org
Interaction-design.org
https://designshift.in/
For Portfolio and inspiration:
Behance
Dribble
Youtube channels to follow:
https://www.youtube.com/c/PiXimperfect

https://www.youtube.com/c/GFXMentor

https://www.youtube.com/c/breakdesignsco

https://www.youtube.com/c/SatoriGraphics

https://www.youtube.com/c/DOPEMOTIONS

https://www.youtube.com/c/thefuturishere

How to Get Clients with these Freelance Websites (never work for free):

Upwork
Freelancer
Fiverr
99designs
Toptal
Guru
PeoplePerHour
Dribbble
LinkedIn
DesignHill
Twine

If I missed any topics please comment them down and if you know any other resources please share them. It will be very helpful for others who want to learn. You can follow me on LinkedIn if you liked what you’ve read.

Roadmap and Resources to Become an Ethical Hacker

Karthik Bhandary — Wed, 01 Jun 2022 11:55:03 +0000

When you hear the word Hacking, the first thing that pops in your head is how cool they look in the movies. I mean they are cool in real life as well. But it does not look the same in real life. Hackers are not only there to break into your websites or accounts. They are there to provide security and improve it as well.

They do it by finding different ways to break into an application — These are called vulnerabilities, and they report them to the company that owns the application. The company then sees to it that, it does not happen again by improving their security.

In this blog, I am going to be sharing the Roadmap and the resources that you can use to become an Ethical Hacker.

ROADMAP AND THE RESOURCES:
Programming
Python: https://www.youtube.com/watch?v=0NQ2aMxBYNE

Database:
SQL: https://www.youtube.com/watch?v=2nXOxLpeu80

Terminal:
CLI, Bashterminal: https://www.youtube.com/watch?v=kGVHUBGZm3E

Networking:
https://www.youtube.com/watch?v=3Kq1MIfTWCE

https://www.youtube.com/watch?v=X4eRbHgRawI

Useful links:
https://www.youtube.com/watch?v=3Kq1MIfTWCE&list=PLWKjhJtqVAbnklGh3FNRLECx_2D_vK3mu

https://www.udemy.com/course/complete-ethical-hacking-bootcamp-zero-to-mastery/

https://www.udemy.com/user/zaidsabih/

https://www.udemy.com/user/nathan-house/

https://www.coursera.org/specializations/intro-cyber-security

https://www.youtube.com/watch?v=fNzpcB7ODxQ

https://www.youtube.com/channel/UC1szFCBUWXY3ESff8dJjjzw

https://www.youtube.com/c/InsiderPhD/videos

https://www.youtube.com/c/STOKfredrik/videos

https://owasp.org/www-project-web-security-testing-guide/latest/

https://null-byte.wonderhowto.com

https://www.hackingloops.com

http://www.securitytube.net

https://www.hackthissite.org

http://www.enigmagroup.org

Other useful websites for free and paid courses:
https://www.guvi.in/mlp/master-cyber-security

https://intellipaat.com/cyber-security-course-certification/

https://portswigger.net/web-security

https://pentesterlab.com

https://www.pentesteracademy.com

https://www.cybrary.it (best)

https://www.udacity.com/school-of-cybersecurity

https://cyberstart.com

https://www.offensive-security.com

https://www.hackthebox.com

https://www.hackerone.com (bug bounty)

https://tryhackme.com

https://my.ine.com/learning-paths

https://www.virtualhackinglabs.com

https://hackersacademy.com

Roadmap and Resources to Become an IoT Engineer

Karthik Bhandary — Wed, 01 Jun 2022 11:52:16 +0000

IoT — This name has been quite popular nowadays. I don’t know how many of you are aware of this but most of the items that we are using, are IoT devices, like your fitness band, or smartphone.

IoT stands for Internet of Things, meaning that things are connected to the internet and can communicate with each other through it. In this blog, I am going to be sharing with you the Roadmap and the resources that will let you become an IoT engineer.

ROADMAP AND THE RESOURCES:
Programming:

Embedded C programming: https://www.youtube.com/watch?v=c7pi9-VQmLk

Python: https://www.youtube.com/watch?v=_uQrJ0TkZlc

Electronics:
https://youtu.be/r-X9coYTOV4

Arduino:
https://youtu.be/fJWR7dBuc18

RaspberryPi:
https://youtu.be/1WDagiA8fdU

In-depth Courses:
Audit the Coursera courses to learn for free

https://www.coursera.org/specializations/iot

https://www.coursera.org/specializations/uiuc-iot

https://iiit-h.talentsprint.com/iot/

www.simplilearn.com/iot-certification-training-course

https://intellipaat.com/internet-of-things-iot-training/

https://www.emertxe.com/trainings/iot-courses/iot-courses-projects-with-placements/

https://www.upgrad.com/internet-of-things-certification-pgc-iiitb/

Other useful links:
https://www.electronics-tutorials.ws

https://learn.adafruit.com

https://www.instructables.com/circuits/

https://www.robotshop.com/community/tutorials

https://www.electronicsforu.com

https://www.howstuffworks.com

https://electrical-engineering-portal.com

https://www.electrical4u.com

https://www.allaboutcircuits.com

https://www.engineersgarage.com

https://www.circuitlab.com

https://www.tinkercad.com

https://www.altium.com

https://makezine.com

https://hackaday.com

https://www.electronicshub.org

https://www.electronics-notes.com

YouTube channels to follow:
https://www.youtube.com/c/Afrotechmods

https://www.youtube.com/c/greatscottlab

https://www.youtube.com/c/explainingcomputers

https://www.youtube.com/c/BenEater/videos

https://www.youtube.com/c/Kevindarrah

https://www.youtube.com/c/Lesics

https://www.youtube.com/c/RealEngineering

https://www.youtube.com/c/Theengineeringmindset

Analysis And Prediction On HR Data Set For Beginners

Karthik Bhandary — Fri, 11 Feb 2022 12:10:28 +0000

Are you a newbie when it comes to Data Analysis and Data modelling? If yes, then you are in the right place.
In this blog, we are going to be performing some Exploratory Data Analysis on the HR Dataset available in Kaggle. We’ll also be using RandomForest to predict who left their company. This is a beginner-friendly dataset and it is easy to work with. With that out of the box, let’s get into the juicy stuff.

IMPORTING

The first thing we always do when we start with our work is to import the libraries. You don’t need to import every single library that you’ll be using in the notebook right at the beginning itself. You can start with the bare minimum and those include:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline

Just the above are enough when you start working on a problem. From then on you can just add them at the start or you can just import them where ever you are in the notebook.
Now that we are done with importing let’s load the data.

df = pd.read_csv("../input/hr-analytics/HR_comma_sep.csv")
df.head()

CHECKING FOR THE NEATNESS OF THE DATA

Now that the data is loaded. Let’s take a look at how the data is, i.e, the data types, the no of NaN values etc., We can do that by using the .info() method

df.info()

As you can see above, all the cols are filled with no NaN values. float, int and object are the data types of the columns.

for col in df.columns:
    print(f"{col} - ", df[col].unique())
    print()

There seem to be no weird values in the data, which is very good. Let’s look at the metrics.

df.describe()

Now that we took a look at the data, Let’s get into my favourite part, EDA.

EXPLORATORY DATA ANALYSIS (EDA)

In this, we will be visualizing the data. By doing this we will be able to further understand how the data is and if there is any work that is to be done.

sns.set()#sets the style of the plot.
fig = plt.figure(figsize=(12,6))#Used to display the plot
sns.barplot(x='Department', y='satisfaction_level', hue='salary', data=df, ci=None)
plt.title("Satisfaction_level Vs Department", size=15)
plt.show()

The ones with higher salaries are more satisfied. But product_mng said hold my cup. It seems like the ones with low salaries are more satisfied than the ones with high salaries. The same is for the land.

fig = plt.figure(figsize=(12,6))
g = sns.barplot(x='Department', y='last_evaluation', data=df, ci=None)
g.bar_label(g.containers[0])
plt.title("Last Evaluation Vs Department", size=15)
plt.show()

Everybody has a similar level of evaluation done.

fig = plt.figure(figsize=(12,6))
sns.barplot(x='Department', y='number_project', data=df, ci=None)
plt.title("Number Project Vs Department", size=15)
plt.show()

fig = plt.figure(figsize=(12,6))
g = sns.barplot(x='Department', y='Work_accident', data=df, ci=None)
g.bar_label(g.containers[0])
plt.title("Work Accident Vs Department", size=15)
plt.show()

fig = plt.figure(figsize=(12,6))
g = sns.barplot(x='Department', y='satisfaction_level', hue='left', data=df, ci=None)
g.bar_label(g.containers[0])
g.bar_label(g.containers[1], rotation=90)
plt.title("Left Vs Department with satisfaction level", size=15)
plt.show()

fig = plt.figure(figsize=(12,6))
sns.lineplot(x='Department', y='time_spend_company', data=df, ci=None, color='r', marker='o')
plt.title("Time spent per each Department", size=15)
plt.show()

fig = plt.figure(figsize=(12,6))
sns.lineplot(x='Department', y='average_montly_hours', data=df, ci=None, color='g', marker='o')
plt.title("Avg Hours spent in the company per Department", size=15)
plt.show()

The chart looks hilarious lol🤣 Don’t know why🤷‍♂️

fig = plt.figure(figsize=(12,6))
sns.lineplot(x='Department', y='promotion_last_5years', data=df, ci=None, color='black', marker='o')
plt.title("Promotions of Last 5 years in the company per Department", size=15)
plt.show()

The management and the marketing department have more promotions when compared to the others.

fig = plt.figure(figsize=(12,6))
salary = df['salary'].value_counts()
sns.lineplot(x=salary.values, y=salary.index, ci=None, color='orange', marker='o')
plt.title("Salary (Counts) in the company", size=15)
plt.show()

Now that we’ve seen how the data is let’s get into modelling.
Before getting into that let’s take a look at the correlation.

sns.heatmap(df.corr(), center=0, linewidth=1, annot=True, fmt='.2f')
plt.show()

Here if we observe we can see that satisfaction_level and left columns are highly negatively correlated, which I think is quite obvious.

MODELLING

We need to split our data into a training set and test set so that the model doesn’t remember the data by heart. Before doing that let us drop the categorical columns ‘Development’ and ‘salary’.

df = df.drop(['Department', 'salary'], axis=1)

Here we are passing in a list of col names that we want to drop. By specifying axis=1 we are telling it to remove the column.
You can do it like this as well

df.drop(['Department', 'salary'], axis=1, inplace=True)

By doing this you don’t need to specifically assign it df, since it is going to do it in place.
Now that we got rid of them let's split our data.
First, separate the target var(the one we want to predict) and the rest of the data.

X = df.drop('left', axis=1) # we will predict who left
y = df['left']

Then we pass the X, y into the following function.

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)

From the name of the function, we can say that it is going to be splitting our data into training and test sets.
Now we just need to fit the training data to the model of our choice. Since we are trying to predict we can go with RandomForest.
RandomForest can do both prediction and regression.
we are also going to be using GridSearchCV to find the best params for our model.

from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV
param = [
    {'n_estimators': [100, 200, 300, 400, 450, 500], 
     'max_depth': [3, 4, 6, 8, 10, 12], 
     'max_leaf_nodes': [15, 20, 25]}, 
]
rf = RandomForestClassifier()
gs_rf = GridSearchCV(rf, param, cv = 5, n_jobs = -1, verbose = 1)
gs_rf.fit(X_train, y_train)
rf_best = gs_rf.best_estimator_
pred = gs_rf.predict(X_test)
gs_rf.best_estimator_

So finally we are finished with the modelling. The RandomForest gave us 98% accuracy. Wow really nice. Well done if you followed until the end.

CONCLUSION

In this blog we have seen:

A basic workflow of things
How to implement RandomForest
How to implement GridSearchCV

You can check out my Kaggle notebook here and give it an upvote if you found it helpful

I really hope that you found this analysis helpful and interesting. If you liked my work then don’t forget to follow me on Medium and YouTube, for more content on productivity, self-improvement, Coding, and Tech. Also, check out my works on Kaggle and follow me on LinkedIn. I also write in HackerNoon, you can check that out as well.

5 Programming Languages To Learn In 2022

Karthik Bhandary — Thu, 06 Jan 2022 13:31:21 +0000

Well first of all A very HAPPY NEW YEAR Everyone. I hope that you have a great year ahead of you. Now to the main point. In this article, I am going to be sharing with you 5 Programming Languages to learn in 2022. So stick around till the end to find out.
So the first one on the list is

PYTHON

Right now python is in the boom, mainly due to Data Science and Machine Learning. The reason being is that it is a simple Language, very similar to English. It also has a plethora of Libraries to use which makes our work easy. All this makes us feel
"Man, it's great being a python user😎"
Even if you are not from a CS background it is very easy to get into python.

The second one on the list is

C++

You can say that C++ is basically C on Zenkai boost (dragon ball reference). It is mainly used in Embedded programming, IoT, Game development and cloud computing. It is also popular among Competitive Programmers. If you are into any of these then I would suggest checking this one out.

The third one is

JAVA/SCALA

Java is that gem that does not lose its shine. It is a very powerful language. I think it was around the 1990s that it became popular and till now it did not lose its popularity. This is mainly used in making Enterprise Applications. We use this because it is secure, powerful and easily scalable. Webservers are also made using java. Scala is used heavily on big data applications.

The fourth one is

JAVASCRIPT

Every web developer knows the importance of javascript. HTML and CSS give the website its shape and style. Javascript gives the website its interactivity. There are other applications where it can be used. If you are interested in web Development then consider checking this one out.

The Last one on the list is

GO

This is a coming of age language. Nowadays this is used in creating APIs with the front-end. It is also possible to use it in Cloud and network services, command-line interface, web development and much more. I would suggest taking a look at it.

Do you agree with my list? Are there any other languages that should be on the list? Let me know in the comments. Also, it would be really helpful if you follow me on LinkedIn. I also write on Medium and I have a YouTube channel as well, feel free to visit, I post content related to programming, self-development and Progression.

6 Places to Start a Career in Data Science in 2022

Karthik Bhandary — Sun, 26 Dec 2021 08:25:07 +0000

Are you planning on learning Data Science?🤩 But have no idea where to and how to learn?😫Don't worry because I got you covered😎. In this article, I am going to be sharing with you the resources that you will be needing for learning Data Science along with a roadmap for you to follow. Don't forget to leave thoughts after reading the article😁

Before getting into the resources I want to mention that you will have to buy their subscription. I know😅, but this is about your career, so a little investment is good😄. Also by paying you will be at least motivated so that it doesn't go to waste. But for your sake, I am going to present you with one resource which has free content. Now that's out of the box let's get into knowing the resources. (This article is not sponsored by anyone, this is just my findings. Click on the resource title to get to their website)

1. DataQuest

Dataquest is along with the others that are going to be mentioned in this article are mainly focused on Data Science. Along with Data Science, they also have some related courses like Data Analysis, Data Engineer etc., you can look it up yourself on the website.

Coming to how you learn. You have text on the left and the editor on the right. You read the text which has the lesson and you perform the task given at the end of the lesson. You will be learning and practicing at the same time. It saves a lot of time. Think of it as a blog but you actually learn Data Science by reading that blog.

For a better idea look at the image I got from FreeCodeCamp:

Dataquest looks similar to this one. I think if you like reading blogs then this will be your cup of tea.

2. DataCamp

In this, we have video lectures. The videos last up to a max of 5 min. Right after the video, you get to practise what you learnt. They also provide you with assessments, projects, competitions where if you win you will have prizes.

If you are not into reading, then I would suggest you go with this. I personally use this myself and you won't be disappointed. They also have a very great certification system. Where you have to go through a series of tests before getting the certificate.

3. iNeuron

INeuron is similar to Udemy, but instead of dealing with everything, they only deal with data science and related things. One problem for me with ineuron is that they provide video lectures but we have to practice on our own device on our own (unlike in Datacamp and Dataquest). Apart from that everything else is very good. They also make you do projects and the cool part is that they also provide you with an internship, which you can complete at your own pace. Once you finish your internship you will receive a certificate as well.

4. 365DataScience

This platform same as iNeuron, provides you with video lectures. I just found this recently so I don't have much of an idea on this, but I did hear good things about this, so I decided to mention here. If you are using this then please let me know in the comment section if it is worth it or not? It will be helpful for others as well.

I have mentioned only these, even though there are plenty of others because these 4 only focus on Data Science and related things.

As I've promised I am going to be sharing with you the roadmap which you can follow to learn DataScience. This is also the same one that provides free resource links. Let me explain.

5. DataScienceMaster.org

This website is a gem. They provide you with a plan to follow. From math to Machine Learning. The cool part is that they also provide you with the link from where you can learn for free and for Coursera courses you can audit them.

Let's say that you want to learn math, which is very important when it comes to ML. You want to learn Linear algebra, you can click on the link right next to it and start learning.

It is the same with everything. You click on the link and start learning, easy as that.

6.ProjectPro

You can be sure that this is the place for you to do projects, along with the option of deploying them. You can learn a lot, with 250+ projects in your arsenal. They also provide you with tips to increase your performance as well.

They also have 2000+ free code recipes and 120+ reusable project solutions. What better way to learn and develop your skills. I suggest that you take a look and give this one a try!!

I hope you found this article helpful. Also, it would be really helpful if you follow me on LinkedIn. I also write on Medium and I have a YouTube channel as well, feel free to visit, I post related to programming, self-development and Progression.