Forem: Raman Bansal

Lesser known stock market APIs for 2024 for data science and quant trading

Raman Bansal — Mon, 15 Jan 2024 03:35:13 +0000

Alpha Vantage

Alpha Vantage is a popular provider among data-driven investors & traders due to its vast accessibility. They’re known for their clear documentation, affordable pricing, and generous free tier. A great option for investors looking to gain an edge!

Coverage: Stocks, Fundamentals, Technicals, Forex, Crypto, Commodities, Financial News, Economic Data, Corporate Events, and more. 20+ years of historical data.

API DOCS: https://www.alphavantage.co/documentation/
PRICING: 25 requests per day for free

Barchart OnDemand

Barchart OnDemand is another accessible financial data platform offering customizable plans that allow customers to pick & choose which data feeds they want to pay for. They cover a broad range of endpoints from equity pricing data to futures & options.

Coverage: Stocks, ETFs, Fundamentals, Options, Futures, Forex, Crypto, Commodities, Financial News, Corporate Events, and more. 15 years of historical data.

API DOCS: https://www.barchart.com/ondemand/api

Tradier

Tradier combines market data with a brokerage account to allow users to build & test trading strategies, and also execute those trades with just one integration. (Alpaca is another similar platform.)

Coverage: Stocks, Fundamentals, Options (including greeks), and more.

API DOCS: https://documentation.tradier.com/
PRICING: https://tradier.com/individuals/pricing

Intrinio

Intrinio offers a unique pay-per-service model, allowing you to choose the specific data packages you need. A good option for sophisticated investors and app developers looking for a focused and deliberate approach to data services.

Coverage: Stocks, ETFs, Fundamentals, Technicals, Options, Financial News, Economic Data, Corporate Events, and more. 50 years of historical data.

API DOCS: https://docs.intrinio.com/documentation/api_v2/getting_started
PRICING: https://intrinio.com/pricing

Xignite

Xignite is one of the most expensive, yet powerful APIs available in the industry. They source institution-grade data from over 6,000 exchanges globally and power many high-profile fintech and wealth management companies such as SoFi & Betterment.

Coverage: Stocks, ETFs, Fundamentals, Options, Futures, Forex, Crypto, Commodities, Swaps, Mutual Funds, Fixed Income, Corporate Events, and more.

API DOCS: https://www.xignite.com/financial-data-apis
PRICING: https://www.xignite.com/pricing

Thanks for reading

The ultimate Streamlit cheatsheet for 2023

Raman Bansal — Sun, 19 Mar 2023 01:53:44 +0000

Introduction

Here is the cheatsheet that every streamlit beginner needs to know

For basic tasks

Installation and streamlit

For installing just simply use pip command. Here is how.

> pip install streamlit

To keep simple we import streamlit as st. Let me show you how.

import streamlit as st

Subheader

For adding subheader

st.subheader("A subtitle")

Text

This is for adding a paragraph in web page

st.text("Some text")

Markdown

You can simple add markdown to your page by this method.

st.markdown("# Some Markdown")

Displaying Data

dataframe and table is used to just simply display a dataframe in tabular form.

st.dataframe(my_dataframe)
st.table(my_data, width=800, title="Title of sample data")

Width of the table is specified in pixels.

Displaying Images

Here is the sample code for displaying images.

# Load image from URL
image_url = "https://example.com/image.png"
st.image(image_url, caption='Example image')

# Load image from local file
image_file = "path/to/image.png"
st.image(image_file, caption='Example image', width=300)

In streamlit, we can specify caption and width of image. We can also add alt text by just simply adding alt parameter to it. But still you can use caption as an alternative to it.

Displaying Videos

Here is the sample code for running videos.

# Load video from URL
video_url = "https://example.com/video.mp4"
st.video(video_url, width=500)

# Load video from local file
video_file = "path/to/video.mp4"
st.video(video_file, width=500)

Displaying Audio

Here is the sample code to add audio file in the web page.


audio_file = open('my_audio.mp3', 'rb')
audio_bytes = audio_file.read()

st.audio(audio_bytes, format='audio/mp3')

Displaying Interactive Charts

Here is how to add add charts.

st.line_chart(my_data)
st.area_chart(my_data)
st.bar_chart(my_data)
st.scatterplot(my_data)

Displaying Selectable Options

This is same as select tag and checkbox input in html.

option = st.selectbox("Select an option", my_options)
checkbox = st.checkbox("Check me!")

Input Forms

Here is some other input options for beginners.

text_input = st.text_input("Enter some text")
number_input = st.number_input("Enter a number")
date_input = st.date_input("Enter a date")
time_input = st.time_input("Enter a time")
file_input = st.file_uploader("Upload a file")

Buttons

button_clicked = st.button("Click me!", on_click=my_func)

Displaying Progress

Here is how to show spinner for some heavy computation.

with st.spinner("Loading..."):
    # Do some heavy computation
    st.success("Done!")

Adding Layout and styles

Layout Configuration

the page_title argument is set to "My App" and the page_icon argument is set to "😃", which will display a smiley face icon in the browser tab.

st.set_page_config(page_title="My App", page_icon=":smiley:")

App Layout

Here is how you can add columns in streamlit.

col1, col2 = st.columns(2)
with col1:
    st.header("Column 1")
    st.write("Some data")
with col2:
    st.header("Column 2")
    st.write("Some more data")

You can add more than two columns.

Styling Text

Streamlit allows you to select font and also specify the sixe of font and text colour.

st.write("This is some text", font=("Arial", 16), text_color="blue")

Styling Buttons

key is same as id of the button. The help will show a popup text that will display when the user hovers.

st.button("Click me!", key="my_button", help="This is a button")

Styling Containers

Here is the sample code for adding an individual container in streamlit.

with st.container():
    st.write("This is inside a container")

Adding Icons

Hwre is how to add icons.
You just simply have to add :<ICON_NAME>: in text

st.write(":chart_with_upwards_trend: Some text")

Some Advanced features

Caching

@st.cache
def load_data():
    # Load data from a remote source
    return my_data

Streamlit Sharing

import streamlit as st
import streamlit.analytics as st_analytics

Send an event to Google Analytics

st_analytics.write_key("GA_KEY")
st_analytics.event("Event Name", "Event Value")

For more, Visit : [Techwithpie streamlit cheatsheet](https://techwithpie.blogspot.com/2023/03/ultimate-streacheatsheet

Streamlit Intro, features and components

Raman Bansal — Sat, 25 Feb 2023 11:58:24 +0000

Introduction

In this article, i have discussed about streamlit, its features and its component. The article is designed for those who wants to learn to deploy their machine learning on internet.

What is Streamlit and How Does it Work?

Streamlit is an open-source development tool that simplifies the process of creating data science apps and web apps.It allows developers to quickly develop interactive, dynamic, and powerful applications without having to write a lot of code.

Streamlit provides a library of components that can be used to create data visualizations, dashboards, and other types of apps. Streamlit also has features such as automatic reloading of code changes and built-in support for Python libraries like NumPy, Pandas, Scikit-learn, and Matplotlib. With Streamlit's easy-to-use interface and powerful features, developers can create amazing applications with minimal effort.

How Streamlit Can Help You Create Amazing Data Science Apps in Minutes

Streamlit provides a wide range of features that help developers create amazing data science apps in minutes. It allows users to easily upload files, visualize data, and embed custom components such as charts and maps. Streamlit also supports popular Python libraries like NumPy, Pandas, Scikit-Learn and TensorFlow which makes it easier for developers to build powerful machine learning models into their applications. With Streamlit, you can quickly develop powerful web apps without having any prior experience in web app development.

Streamlit vs. Flask

We can compare streamlit and flask by creating hello world program using both of technologies

Hello world in Flask

Here is the basic hello world program in flask

from flask import Flask
app = Flask(__name__)
@app.route("/")
def home():
    return "Hello, world"
if __name__=="__main__":
    app.run(debug=True)

For hello world program flask takes 7 lines of code and the code is very complex. Due to which beginners mostly prefer streamlit.

Hello world in streamlit

Here is the basic hello world program in streamlit

import streamlit as st
st.write("Hello, World!")

It takes only two lines of code for streamlit. So, streamlit can be a better option for programmers.

Streamlit Components for web development

Streamlit comes with wide range of components. Here, I will show you the list of that components that you can use for making web apps.

1. Text Input and Output

Components for inputing and outputting text data.

2. Buttons

Components for creating clickable buttons that perform an action.

3. Sliders

Components for selecting numerical values by sliding a slider handle.

4. Select Boxes

Components for selecting an option from a list of options.

5. Checkboxes

Components for selecting one or more options from a list of options.

6. Radio Buttons

Components for selecting one option from a list of options.

7. Date Pickers

Components for selecting a date from a calendar.

8. File Upload

Components for uploading files to the application.

9. Plotting and Visualization

Components for creating interactive plots, charts, and graphs.

10. Layouts

Components for organizing the application layout, such as columns, rows, and tabs.

11. Session State

Components for storing data across user sessions.

12. Forms

Components for creating forms to input data.

Conclusion

Streamlit can be a best framework for you if you are beginner in web development but if you are already inthe field of web development you can switch to the framework that you want to learn.

Read the full article
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Top 10 Platforms💻 for Deploying Machine Learning Models

Raman Bansal — Wed, 22 Feb 2023 16:23:31 +0000

The top 10 platforms for deploying machine learning models will be discussed in this post. We will offer a thorough overview of each platform, highlighting its essential attributes, advantages, and disadvantages.

This tutorial will assist you in selecting the best platform to deploy your machine learning models successfully and efficiently, regardless of whether you are a data scientist or machine learning engineer.

1. Amazon SageMaker

AWS's (Amazon Web Services) Amazon SageMaker is a fully-managed service for creating, and deploying machine learning models at scale. It supports well-known machine learning frameworks like TensorFlow and PyTorch and offers pre-built algorithms, Jupyter notebooks, and tools for data preparation. Developers and data scientists may rapidly and easily create and deploy customised machine learning models with SageMaker.

2. Microsoft Azure Machine Learning Studio

You may create, train, and deploy machine learning models using the cloud-based integrated development environment (IDE) Microsoft Azure Machine Learning Studio. It has a drag-and-drop interface that makes building models simple and also lets you create custom R or Python code. It also offers a variety of tools for testing, deployment, and data preparation.

3. Google Cloud AI Platform

A cloud-based service called Google Cloud AI Platform enables programmers to create, test, and widely deploy machine learning models. A managed environment for running TensorFlow and other machine learning frameworks, as well as a collection of APIs for supplying predictions, are just a few of the tools and services it offers for managing and deploying models. Additionally, it provides data scientists with a streamlined workflow that makes the process of creating and distributing models easier.

4. IBM Watson Studio

IBM Watson Studio is a cloud-based platform designed by IBM for building, training, and deploying machine learning models for production. It allows teams to work together, and provides tools for automating the deployment process of model. With Watson Studio, you can easily deploy your models for production and make them available via APIs.

5. Heroku

Heroku is a cloud-based platform that allows you to deploy web applications, including machine learning models, without worrying about infrastructure management. It supports various programming languages and frameworks, and provides easy integration with other services such as databases and APIs. Heroku offers a free plan for testing and small applications, and paid plans for production-level applications.

6. Algorithmia

Algorithmia is a cloud-based marketplace for machine learning models that allows programmers to deploy their models in production and make them available via APIs. It provides a secure and scalable platform for hosting and managing models, with built-in support for popular programming languages and frameworks. Algorithmia also offers a range of tools for versioning, testing, and monitoring models in production.

7. Databricks

Databricks is a cloud-based platform for building, training, and deploying machine learning models. It provides a collaborative environment and offers tools for data processing, visualization, and model deployment. With Databricks, you can deploy your machine learning models in production easily and quickly.

8. Hugging Face

Hugging Face is a platform for building and sharing natural language processing models, including transformers. It provides pre-trained models and tools for fine-tuning them on your own data. These models can be easily deployed via an API for use in your applications.

9. Paperspace

Paperspace is a cloud-based platform for machine learning. It provides tools for building, training, and deploying machine learning models. With Paperspace, you can easily deploy your models in the cloud and access them via APIs.

10. FloydHub

FloydHub is a cloud-based platform that simplifies the training and deployment of machine learning models. It offers easy collaboration, built-in version control, and supports popular ML frameworks. You can quickly deploy your trained models on the cloud with just a few clicks.

NOTE:

Some of these platforms are not free and are not recommended for beginners

Read the full article: Platforms for deploying ml models

8 Proven Strategies for Becoming a Better Data Scientist🚀🔥

Raman Bansal — Wed, 15 Feb 2023 13:10:14 +0000

Introduction

Data science is the field that combines mathematics, statistics, and computer science to analyze large data sets and draw meaningful conclusions. This is a fast-growing field with many job opportunities for those with the right skills.

Data scientists are responsible for collecting, cleaning, analyzing and interpreting data to provide information that can be used to make informed decisions.

Data scientists should be proficient in programming languages such as Python or R. You should also learn machine learning algorithms and understand SQL and NoSQL.

Here some ways that make you more successful in the field of programming

1. Craving to Learn Latest Technology

The field of data science is constantly getting advanced with the development of new tools and techniques all the time. Therefore, it has become very important to keep yourself updated with the latest technology. Learning the latest technology will ensure that you are choosing the best tools available to solve complex problems.

2. Programming Languages

A skilled data scientist will have a knowledge of various programming languages, such as Python, Perl, C/C++, SQL, and Java, as these have become the most common and crucial coding languages required in data science roles. These programming languages help data scientists organize unstructured data sets.

3. Skill to Play with Unstructured Data

The next in the list of tips to be a more successful data scientist is playing with unstructured data. A Data Science professional should have experience working with unstructured data that comes from different channels and sources. For example, if a data scientist is working on a project to help the marketing team provide insightful research, the professional should be well adept at handling social media as well.

4. Capable of Using Critical Thinking

Data scientists are often required to think critically in order to identify patterns and insights in data. This includes being able to ask the right questions and identify assumptions that require to be tested.
And the ability to think in new ways is equally important to finding innovative solutions.

5. Creativity

Data scientists must be creative in their work to explore new ways to solve complex problems. This means breaking stereotypes and providing innovative solutions. It is also very important to be able to communicate your creative ideas effectively so that others can understand them.

6. Developing Strong Skills

Data science requires strong skills.
This includes experience with a variety of popular programming languages, machine learning algorithms, and statistical modeling techniques. Technical skills are usually apparent and include basic skills such as statistics, programming, mathematics, and data visualization.

7. Cultivate a growth mindset

Having a growth mindset allows you to see failure as an opportunity to grow, not avoid it. It also helps build the confidence that you can learn anything. Try new things, ideas, tools, and methods, and receive the gift of feedback so you can move forward and finally be inspired by the successes of others.

8. Be curious and learn more

Experienced data scientists are always interested in learning more. The important thing in data science work is an intuitive mind full of curiosity. In huge data sets, valuable information is not always obvious, and a trained data scientist needs to be intuitive and know when to drill down into information.

Thanks for reading here is the link to full article

Read the full article

Learn about Linear Regression: Theory, Examples, and Applications 💻

Raman Bansal — Sat, 11 Feb 2023 07:44:18 +0000

Introduction: What is Linear Regression and how does it work?

Linear regression is a statistical method to make predictions. It is a type of supervised machine learning model which use statistical analysis for predicting the values as per the given data.

The main goal of linear regression is to find the line which gives the minimum mean squared error. The line represents the linear relation between independent variable(x) and dependent variable{y}.

Evaluating types of linear regression

There are mainly three types of linear regression:

1. Simple linear regression

When there is only one independent variable in linear regression model is said to be simple linear regression model. In this type, there is only one weight (coeffients or slope).

2. Multiple linear regression

When there is more than one independent variable in linear regression model is said to be simple linear regression model. In this type, there is more than one weights.

3. Polynomial regression

In this type of linear regression, relation between independent and dependent variable is defined by a ploynomial function.

Other form of linear refression are ridge, lasso and Logistic Regression.

Mathematics behind linear regression

Our main goal is to find the best line which leads to min m squared error.

In case of simple linear regression, the general equation is given by:

y = a0 + a1 * x

where y is dependent variable and x in independent variable. a1 is slope, weight and a0 is y intercept.

For Multiple linear regression, the equation becomes

y = a0 + a1 * x1 + a2 * x3 + ....... + an * xn

Gradient descent

Gradient descent is an algorithm which used to minimise cost function by optimising weights and bias. In simple words, it is a algorithm which is used to find the value of x at which the f(x) is minimum.

x = x0 - η * f'(x)

Root mean squared error

The root mean squared error is also known as residual sum of squares (RSS)
This is given by:

RSS = Σ(yi - (β0 + β1xi))^2

This method is used to find the accuracy of our model. Less will be the error more will be the accuracy.

How to Implement Linear Regression in Machine Learning Projects

Here is the sample implimentation of linear regression in python using scikit learn library.

import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split

# load data into a Pandas DataFrame
df = pd.read_csv("sample.csv")

# separate the features and target variables
X = df.drop("target", axis=1)
y = df["target"]

# split the data into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# create a Linear Regression model
reg = LinearRegression()

# fit the model to the training data
reg.fit(X_train, y_train)

# make predictions using the test set
y_pred = reg.predict(X_test)

# calculate the MSE
error = mean_squared_error(X_test, y_test)
print("Error: ", error)

train_test_split method is used to get the data for training the model and testing the model.

Real-World Applications of Linear Regression in Machine Learning

Linear regression is one of the most widely used machine learning algorithms. It is used to predict the value of a dependent variable based on one or more independent variables. Linear regression can be used in a variety of predictive analytics applications, such as forecasting models for sales prediction, customer segmentation, and risk management. It can also be used to identify relationships between different variables and to detect patterns in data. In this article, we will discuss some real-world applications of linear regression in machine learning. We will look at how it can be used to make predictions about future events and how it can help businesses make better decisions.

Conclusion: Unlocking the Power of Linear Regression for Your Machine Learning Projects

Linear regression is a powerful tool for machine learning projects. It can be used to predict outcomes, identify trends, and uncover relationships between variables. By understanding the fundamentals of linear regression and how it works, you can unlock its potential to help you build better models and make more accurate predictions. With the right data and the right techniques, linear regression can be a powerful tool for your machine learning projects.

Read the full article:
Linear regression

Get Organised as a Data Scientist: Tips & Strategies

Raman Bansal — Sun, 05 Feb 2023 05:02:07 +0000

INTRODUCTION

Sometimes, beginner or intermmediate data scientist lost in their notebooks finding the cells and results of the analysis they done, due this they waste most of their figure out their code and remembering the results.

Here are some steps in which you can solve these problems and save your lot of your time

The steps are as follows.

1. Create two folders for a project

For a project, you need to create two folders. One is for raw data and other is for processed data. The raw data file folder contains raw files (or orginal data) given to you. The processed data contains files that contains the data that you preprocessed using a pipeline or any other method.

It makes you easy for working with data and also you don't need to preprocess the data again and again in the notebooks.

2. Create separate notebooks for differernt tasks

For large projects, you cannot work within a single notebook. You need to create two or more notebooks. Otherwise, you face many problems like your kernel stop working pproerly and your cells will run slowly and you will lost your precious time.

So, in order to avoid this create two or more separate notebook. Save each notebook with their name with the work they supposed to do.

3. Use Markdown

For data scientists explaination of their code and results of their analysis is very important. So, use markdown for explaining your code and note down the results.

You can markdown when

You need to give introduction
You want to write additional info about notebook
You want to write results of analysis
You want to elaborate your notebook sturcture.

4. Use comments

Data scientist mostly need to explain their codes to others. So, use comments in the cell for explaining the different parts of your cell.

For an example, in a cell you create a function that predicts the output using machine learning model. In that case you can define little bit about input and output of that function. At last remember, you should not use comments unneccesoraily.
Read the full article

A Comprehensive Guide to Using the Pytrends Python Library

Raman Bansal — Wed, 11 Jan 2023 07:26:11 +0000

Introduction

With Pytrends, users can easily visualize the data they have collected, as well as create custom reports and dashboards. Additionally, Pytrends offers tutorials and documentation to help users get started with the library. In this article, we will discuss what Pytrends is and how it works so that you can start using it for your own data analysis projects.

How to Install & Set Up the Pytrends Python Library

Installing and setting up the Pytrends Python library is easy and straightforward.

Here, is the pip command that you need to run for installation

pip install pytrends

Exploring the Different Features of the Pytrends Python Library

Connecting to google

One of the initial steps for using pytrends is to connect it with Google and we need to import TrendReq from pytrends.request. Also, pandas library for data visualization.

import pandas as pd
from pytrends.request import TrendReq
pytrend = TrendReq()

Intreset By Region

Let's say we need to find the intreset of people of different countries in a given keyword. For this, we will use interest_by_region method to gennerate a dataFrame( which shows what proportions of people are interested in the given topic).

kw = ['Python']
pytrend.build_payload(kw_list=kw)

df = pytrend.interest_by_region()
df.head()

The values goes from 0 to 100.

Dialy Trends

Now let us get the top daily search trends worldwide. To do this we have to use the trending_searches() method. If you want to search worldwide just don't pass any parameter.

Pytrends API gives the facility to checkout the dialy search trends from worldwide. trending_searches method we get a report of trending searches but for a country just pass parameter pn

df = pytrend.trending_searches(pn="india")
df.head()

You should enter the country name in lowercase

Past trends

With top_charts method we can get the top trending searches yearly.

df = pytrend.top_charts(2022, hl="en-IN", tz=300, geo='GLOBAL')
df.head()

Keyword Suggestion

Whenever we search on google, it will start giving suggestions according to the query given. To generate a list of suggestions, we need suggestions method.

keyword = 'Python'
data = pytrend.suggestions(keyword)
df = pd.DataFrame(data)
df.head()

Related Queries & Topics

Related queries means the queries related to the keyword.

pytrend.build_payload(kw_list=['Python'])
related_queries = pytrend.related_queries()
related_queries.values()

related_topic = pytrend.related_topics()
related_topic.values()

Thanks for reading.

Top 6 IDEs for Python programmers

Raman Bansal — Sat, 03 Dec 2022 14:21:51 +0000

Introduction

Python is a popular programming language for beginners as well as experienced developers. It is a versatile language with many libraries and frameworks that can be used to build applications for web, data science, machine learning and more.
One of the most important aspects of programming is the IDE or code editor that you use to write your code. This tool will make your development experience much more enjoyable.

So here are 10 of the best Python IDEs and code editors on the market today!

1) Pycharm

Pycharm is a cross-platform IDE with support for Python. It is developed by JetBrains and is available for Windows, macOS, and Linux.

The IDE provides code analysis, debugging, unit testing, and project management tools. Pycharm supports code completion, smart code navigation, refactoring, code generation and more.

It also has a built-in Python debugger that allows users to step through the code line by line or to break at any point in the program's execution.

2) Komodo Edit

Komodo Edit is an open source editor that is commonly used by Python developers due to its many useful features such as the ability to quickly go to the definitions of objects and find all references in a particular file.

3) IDLE

IDLE is a Python IDE that is designed to be simple and easy to use.

IDLE is a Python IDE that is designed to be simple and easy to use. It has all the features you need for editing, running, debugging, and understanding your program.

IDLE provides many tools for interactive testing of your programs. You can set breakpoints in your code and then run the program until it breaks. This allows you to see what's happening as the program runs without having to write any extra code or create any files other than your source file.

4) Visual studio code

Visual Studio Code is a code editor that runs on Windows, macOS and Linux. It provides support for debugging, embedded Git control, syntax highlighting, intelligent code completion, snippets and many other features that make development easier.

It is an open-source project developed by Microsoft. It is free to use and available in a variety of languages.

5) Atom

Atom is a free and open-source text editor developed by GitHub. It is written in JavaScript, C++, and TypeScript. Atom is a cross-platform text editor that runs on Windows, macOS, Linux, and Ubuntu.

Atom has been designed with the goal of improving what was already available in other editors. Atom has a large library of extensions that add support for additional file types or provide extra features such as code snippets or a project manager.

6) Spyder

Spyder is an open-source IDE that is used for the development of Python code. It has a number of features that make it stand out from other IDEs.

Some of the features include:

Syntax highlighting and auto-completion
Debugging
Interactive shell
Integrated help system
Extensive documentation on Python programming language

Read the full article - https://techwithpie.blogspot.com/2022/12/Ides-for-python-programmers.html

9 tips for writing clean code

Raman Bansal — Sat, 01 Oct 2022 01:53:29 +0000

Introduction

Software development is not just writing code. Its about writing cleaner code which can be easily understood by other developers.

In this article, I have mentioned 9 tips which will help you to write much cleaner and readable code.

1. Use describing names

Clean code is easy to understand. While writing code we use short names for variables, parameters etc. in our code but we should use describing words for declaraing variables, parameters, functions etc.

Don't

const n = 100;

const it = 200;

const number = 100;

const iterations = 200;

2. Use proper whitespaces & indentation

Many of us think that using whitespaces & indentation will affect the speed of compiler but you should use the whitespaces & proper indentation in your code because this make your code more easy to understand

Don't

function start() {
var name = 'John';
var code = 200;
}

function start() {
  var name = 'John';
  var code = 200;
}

3. Try to reduce the number of parameters in a function

Since, we are trying to make our code clean. So, we should make a easy to read function for which we should make the function small by reducing the no. to parameters in it. If we need to use more than two or three parameters, then we can send one single object as a parameter in place of three parameters

Don't

function register(name, email, password, phone, address, intresets){

}

function register(info){

}

4. Each function perform single task

Multitasking is great but not in terms of writing clean code. In many cases, developers create a function that do more than one task but we need to avoid that because it make difficult for others to understand our code. By creating one function for one task we can easily organise our code in more easily.

5. Try to reduce the size of functions

It is easier to understand small sized function in the place of functions which are large on size or contains huge code. If you are working on large projects you can use classes in the place of functions.

6. Try to reduce the characters in a line

Since,we are writing a code which is easy to understand and easy to read. So, we need to reduce the characters in our code lines. So that our code can be easily fit on screen and here is no need to scroll horizontally to see the code.

7. Always describe the reason why you create a commit

You should always define why you are committing a code in github repository. it gives us an idea what are the errors or bugs in our code some months ago and what changes are done with the code to improve. You should define this in four to ten words words or you may also use a word which can easily define your message.

8. Avoid repetition of code

Focus on creating reusable code. Most of beginner don't do that. Atleast 70% of your code should be reusable. This reduces the size of our code and also it makes easier for you to organise and work with code. You can break down your work into small task and make a function for that task.

9. Don't use unnecessary comments

Since we have already given describing and directed another, therefore, there is no need of comments in the code. Only write comments when you are using some third party APIs', applications or requests. Avoiding comments make the code much cleaner and easier to understand.

Read the full article
https://techwithpie.blogspot.com/2022/09/tips-for-writing-clean-code.html

Thank you, and wish you all a great programming experience.

Top 10 time management tips for developers

Raman Bansal — Wed, 27 Apr 2022 12:01:37 +0000

Introduction

While working on projects, about 80% of prrogrammer fails in managing their time. This become so worst when you are freelancing. Many freelancers couldn't finish their projects before deadline becuase of poor time management practises.

In this article, we will go through all the tips which will helps you in managing your time in a best way.

1. Divide large projects into samall tasks

Instead of working on large projects, you should divide them into small tasks and complete them one by one. This productive tip helps you in tracking your progress and also helps in completing projects before deadline.

To make it more productive, you should work on important task first and less important task in the end.

2. Work on urgent projects first

A freelancing developer works on various projects but sometimes he / she forgot to work on urgent projects before the deadline. In order to solve this problem, you may use ABCD method which is very productive and time saving.

In this method, you should divide into four parts - A, B, C, D. Here is the list of tasks you will do in these parts.

A : Urgent and important tasks like freelancing projects.
B : Non urgent tasks like learning new skills
C : Non urgent tasks like Practising new learned skills
D : Non urgent and also non important tasks like using social media

You should start your day with working on projects listed in A to D.

3. Create a to do list

Great programmers always create a to do list before starting a day. These to do lists helps you in managing daily tasks and also helps in tracking progress through out the day. You may also use the previous ABCD method in it to increase producitvity

4. Set time limit for every task

For every task you should set a time limit. This helps you in completing the tasks before deadline. If you set a time limit, you will try to complete the task more soon as pposible and short time limits pressurise us to do so. Therefore, Setting a time limit force us to complete the work before the deadline.

Also note that the time limit should be short.

5. Track your progress

After all doing the previous stuff, you should track your progress in order to figure out what are the mistakes done by you. Try improve yourself and also find the various methods by which you can increase productivity and concentration to your work.

6. Take breaks after completing tasks

While working on projects, you should take regular breaks in order to overcome stress and pain. Preferly should take breaks after completing a task or after an hour. This will also sometimes act as motivation. Taking breaks refresh your mind.

7. Stay away from distractions

Staying away from distractions is very-very important as a developer. Many developer face this problem. Sometimes we face many distractions which reduces our productivity and waste our lot of time. So you should try your best to remove such distractions from dialy routine. These distractions may destroy your programming carrer.

8. Remove non-essential activities

Non-essential activities including using social media for wasting time and using netflix for watching movies should eliminated from your dialy routine. This activites are not essential and also act as a barrier in your carrier. You may use them only when you have free time and in a limit. Use social media when you find the way to earn money from it.

9. Reward yourself after completing tasks

Rewarding yourself is like motivating yourself sometimes motivation encourage us to do something great. So after completing a task you should reward yourself for motivation. Also note that rewarding sometimes make us over confident and make us materialistic. Therfore we careful of this.

10. Don't Multitask

Multitasking is not a great way to increase productivity. Multitasking reduces focus and over a task. You cannot work on different task at same time. Many developers mess eith their code while doing multitasking. This practise becomes very worst when you are freelancer. You cannot finish the projects with perfection and before deadline. Therefore avoid multitasking as much as possible.
Read the full article:
Time management tips
Thanks

Top 35 resources for programmers 2022

Raman Bansal — Fri, 22 Apr 2022 12:49:47 +0000

Introduction

To order to become a great programmer, you have to conquer programming by learning it from various resources including great youtube channels to great websites.

In this article, I will present you the list of 35 best resources for learning programming.

Let's starts with some popular youtube channels.

Best Websites

There are many types of websites some of which is made for practising code and some is made for providing tutorials. Therfore, I split them into two parts.

Websites for coding practise

Websites for learning programming

Best apps

ProgrammingHub
Mimo
Sololearn
Grasshopper

There are many apps available in play store and apple app store but I like these apps and these are very popular among developers.

Read the full article:- Top 35 resources for programmers
Thanks