Trending February 2024 # Closing The Data Science Skills Gap In India # Suggested March 2024 # Top 3 Popular

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The field of Data Science today has evolved much more and become the most demanded domain across industries. This field of study involves scientific methods, algorithms, processes, and systems to pull out meaningful insights from both structured and unstructured data for effective decision making and predictions. As a multi-disciplinary domain, data science has enabled businesses across the world to assess market trends, analyze users’ metrics, envisage potential business risks and make better decisions.

The field of Data Science today has evolved much more and become the most demanded domain across industries. This field of study involves scientific methods, algorithms, processes, and systems to pull out meaningful insights from both structured and unstructured data for effective decision making and predictions. As a multi-disciplinary domain, data science has enabled businesses across the world to assess market trends, analyze users’ metrics, envisage potential business risks and make better decisions. In India, businesses are quickly capitalizing on this newly emerging field to garner more from their data and deliver more value to customers, leading to a rise in demand of data scientists. According to a Great Learning report , the country is expected to see 1.5 lakh new openings in Data Science in 2023 , an increase of nearly 62% compared to the last year. As the competitive business landscape is evolving than ever, understanding the users and their preferences accurately has become critical for companies. This is where the role of data science comes into the scenario, making it possible to create and leverage time-saving automated models to get insights into a user’s purchase history, age, income level, and related demographics. Since the data science field is rising at an unprecedented rate, the huge demand for data science talent with less than five years of work experience is most among BFSI (38%), followed by Energy (13%), Pharma and Healthcare (12%), and E-Commerce (11%), among others. BFSI is the highest average salary offered industry to a Data Scientist stood at INR 13.56 LPA, which is followed by manufacturing and healthcare (INR 11.8 LPA each), and IT (INR 10.06 LPA), the report noted. Meanwhile, as companies across almost every industry are looking to acquire skilled and qualified talent who can navigate through everyday issues with innovative and right solutions, the supply of skilled professionals is far lesser than the demand. According to our estimations, there are major skills gaps in the field of big data professionals (58%) in 2023 at a global level. In order to close these gaps, academia, governments as well as organizations must carry out innovative ways to support future minds and derive value from that to spur economic growth. There is a need to implement re-skilling and up-skilling initiatives for data scientists at both corporate and academic levels. The data science professionals will also be expected to have specialized skills in data across all industries, making up-skilling a mandatory job. While the Great Learning report identified four unique career paths in the data science field including Data Scientist Data Analyst , Data Engineer and Business Intelligence Developer, the need for rapid up-skilling and adequate guidance has become indispensable. Moreover, investing in data-driven decisions and technology can also add value to businesses, easing the specialized talent shortage and enabling companies to drive efficiency.

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Python Treatment For Outliers In Data Science

What is Feature Engineering?

When we have a LOT OF FEATURES in the given dataset, feature engineering can become quite a challenging and interesting module.

The number of features could significantly impact the model considerably, So that feature engineering is an important task in the Data Science life cycle.

Feature Improvements

In the Feature Engineering family, we are having many key factors are there, let’s discuss Outlier here. This is one of the interesting topics and easy to understand in Layman’s terms.

Outlier

An outlier is an observation of a data point that lies an abnormal distance from other values in a given population. (odd man out)

Like in the following data point (Age)

18,22,45,67,89,125,30

An outlier is an object(s) that deviates significantly from the rest of the object collection.

List of Cities

New York, Las Angles, London, France, Delhi, Chennai

It is an abnormal observation during the Data Analysis stage, that data point lies far away from other values.

List of Animals

cat, fox, rabbit, fish

An outlier is an observation that diverges from well-structured data.

The root cause for the Outlier can be an error in measurement or data collection error.

Quick ways to handling Outliers.

Outliers can either be a mistake or just variance. (As mentioned, examples)

If we found this is due to a mistake, then we can ignore them.

If we found this is due to variance, in the data, we can work on this.

In the picture of the Apples, we can find the out man out?? Is it? Hope can Yes!

But the huge list of a given feature/column from the .csv file could be a really challenging one for naked eyes.

First and foremost, the best way to find the Outliers are in the feature is the visualization method.

What are the Possibilities for an Outlier? 

Of course! It would be below quick reasons.

Missing values in a dataset.

Data did not come from the intended sample.

Errors occur during experiments.

Not an errored, it would be unusual from the original.

Extreme distribution than normal.

That’s fine, but you might have questions about Outlier if you’re a real lover of Data Analytics, Data mining, and Data Science point of view.

Let’s have a quick discussion on those.

Understand more about Outlier

Outliers tell us that the observations of the given data set, how the 

data point(s) differ significantly from the overall perspective. Simply saying 

odd one/many. this would be an 

error during 

data collection. 

Generally, 

Outliers

affect

 statistical results while doing the EDA process, we could say a quick example is the MEAN and MODE of a given set of data set, which will be misleading that the 

data

values would be higher than they really are.

Positive Relationship 

When the correlation coefficient is closer to value 1

 Negative Relationship

When the correlation coefficient is closer to value -1

Independent

When X and Y are independent

, then the

correlation coefficient

is close to

 zero (0)

We could understand the data collection process from the Outliers and its observations. An analysis of how it occurs and how to minimize and set the process in future data collection guidelines.

Even though the Outliers increase the inconsistent results in your dataset during analysis and the power of statistical impacts significant, there would challenge and roadblocks to remove them in few situations.

DO or DO NOT (Drop Outlier)

Before dropping the Outliers, we must analyze the dataset with and without outliers and understand better the impact of the results.

If you observed that it is obvious due to incorrectly entered or measured, certainly you can drop the outlier. No issues on that case.

If you find that your assumptions are getting affected, you may drop the outlier straight away, provided that no changes in the results.

If the outlier affects your assumptions and results. No questions simply drop the outlier and proceed with your further steps.

Finding Outliers

So far we have discussed what is Outliers, how it affects the given dataset, and Either can we drop them or NOT. Let see now how to find from the given dataset. Are you ready!

We will look at simple methods first, Univariate and Multivariate analysis.

Univariate method: I believe you’re familiar with Univariate analysis, playing around one variable/feature from the given data set. Here to look at the Outlier we’re going to apply the BOX plot to understand the nature of the Outlier and where it is exactly.

Let see some sample code. Just I am taking chúng tôi as a sample for my analysis, here I am considering age for my analysis.

plt.figure(figsize=(5,5)) sns.boxplot(y='age',data=df_titanic)



You can see the outliers on the top portion of the box plot visually in the form of dots.

Multivariate method: Just I am taking titanic.csv as a sample for my analysis, here I am considering age and passenger class for my analysis.

plt.figure(figsize=(8,5)) sns.boxplot(x='pclass',y='age',data=df_titanic)

We can very well use Histogram and Scatter Plot visualization technique to identify the outliers.

mathematically to find the Outliers as follows Z-Score and Inter Quartile Range (IQR) Score methods

Z-Score method: In which the distribution of data in the form mean is 0 and the standard deviation (SD) is 1 as Normal Distribution format.

Let’s consider below the age group of kids, which was collected during data science life cycle stage one, and proceed for analysis, before going into further analysis, Data scientist wants to remove outliers. Look at code and output, we could understand the essence of finding outliers using the Z-score method.

import numpy as np kids_age = [1, 2, 4, 8, 3, 8, 11, 15, 12, 6, 6, 3, 6, 7, 12,9,5,5,7,10,10,11,13,14,14] mean = np.mean(voting_age) std = np.std(voting_age) print('Mean of the kid''s age in the given series :', mean) print('STD Deviation of kid''s age in the given series :', std) threshold = 3 outlier = [] for i in voting_age: z = (i-mean)/std outlier.append(i) print('Outlier in the dataset is (Teen agers):', outlier) Output

The outlier in the dataset is (Teenagers): [15]

(IQR) Score method: In which data has been divided into quartiles (Q1, Q2, and Q3). Please refer to the picture Outliers Scaling above.  Ranges as below.

25th percentile of the data – Q1

50th percentile of the data – Q2

75th percentile of the data – Q3

Let’s have the junior boxing weight category series from the given data set and will figure out the outliers.

import numpy as np import seaborn as sns # jr_boxing_weight_categories jr_boxing_weight_categories = [25,30,35,40,45,50,45,35,50,60,120,150]  Q1 = np.percentile(jr_boxing_weight_categories, 25, interpolation = 'midpoint') Q2 = np.percentile(jr_boxing_weight_categories, 50, interpolation = 'midpoint') Q3 = np.percentile(jr_boxing_weight_categories, 75, interpolation = 'midpoint') IQR = Q3 - Q1 print('Interquartile range is', IQR) low_lim = Q1 - 1.5 * IQR up_lim = Q3 + 1.5 * IQR print('low_limit is', low_lim) print('up_limit is', up_lim) outlier =[] for x in jr_boxing_weight_categories: outlier.append(x) print(' outlier in the dataset is', outlier) Output

the outlier in the dataset is [120, 150]

sns.boxplot(jr_boxing_weight_categories)

Loot at the boxplot we could understand where the outliers are sitting in the plot.

So far, we have discussed what is Outliers, how it looks like, Outliers are good or bad for data set, how to visualize using matplotlib /seaborn and stats methods.

Now, will conclude correcting or removing the outliers and taking appropriate decision. we can use the same Z- score and (IQR) Score with the condition we can correct or remove the outliers on-demand basis. because as mentioned earlier Outliers are not errors, it would be unusual from the original.

Hope this article helps you to understand the Outliers in the zoomed view in all aspects. let’s come up with another topic shortly. until then bye for now! Thanks for reading! Cheers!!

The media shown in this article are not owned by Analytics Vidhya and is used at the Author’s discretion.

Related

Top Data Science Jobs In Gurgaon Available For Data Scientists In 2023

Analytics Insight has churned out the top Data Science jobs in Gurgaon available in 2023 Data Scientist at Airtel

Airtel is known as one of the largest telecom service providers for customers and businesses in India. It also operates in 18 countries with products such as 2G, 3G and 4G wireless services, high-speed home broadband as well as DTH. The company consists of more than 403 million customers across the world. Responsibilities: The data scientist needs to research, design, implement as well as evaluate novel Computer Vision algorithms, work on large-scale datasets and create scalable systems in versatile application fields. The candidate is required to work closely with the customer expertise team, research scientist teams as well as product engineering teams to drive model implementations along with new algorithms. The candidate also needs to interact with the customer to gain a better understanding of the business problems and help them by implementing machine learning solutions. Qualifications: A candidate is required to have practical experience in Computer Vision and more than three years in building production-scale systems in either Computer Vision, deep learning or machine learning. There should be coding skills in one programming language and a clear understanding of deep learning CV evaluation metrics such as mAP, F_beta and PR curves as well as face detection, facial recognition and OCR. The candidate also needs to have 2-3 years of modelling experience working with Pytorch, MxNet and Tensorflow along with object detection approaches such as Faster RCNN, YOLO and CenterNet.

Data Scientist at BluSmart

BluSmart is known as the first and leading all-electric ride-hailing mobility service in India. It has a mission to steer urban India towards a sustainable means of transportation by building a comprehensive electric on-demand mobility platform with smart charging and smart parking. The company will provide efficient, affordable, intelligent as well as reliable mobility. Responsibilities: The candidate is required to do a geospatial and time-based analysis of business vectors like time-travelled, fare, trip start and many more to optimise fleet utilisation and deployment as well as develop strategies to deploy electric vehicles and chargers in Delhi-NCR along with Mumbai by using data from thousands of trip from BluSmart cabs. The data scientist will create a new experimental framework to collect data and build tools to automate data collection by using open-source data analysis and visualisation tools. Qualifications: The candidate is required to have sufficient knowledge of data analytics, machine learning, and programming languages such as R, SQL and Python. The candidate needs to have practical experience with data analytics, machine learning and business intelligence tools such as Tableau with smart mathematical skills.

Associate Data Scientist at Pee Safe

Responsibilities: The data scientist should receive actionable insights from data to be used in real-time in all decision-making processes for the company and implement multiple processes across different departments to enhance business metrics. The candidate needs to create new models or improve existing models to be used for the supply chain, demand predictions, logistics and many more. Qualifications: The candidate should have a Bachelor’s degree in Statistics, Mathematics, Computer Science, Engineering or any other relevant field. The candidate is required to have at least two to three years of practical experience in quantitative analytics or data modelling. It is essential to have a clear understanding of predictive modelling, machine learning, clustering, classification techniques, algorithms, programming language as well as Big Data frameworks and visualisation tools such as Cassandra, Hadoop, Spark and Tableau. The candidate must have strong problem-solving skills with sufficient knowledge of Excel.

Data Scientist at Siemens Limited

Siemens is popularly known as a technology company focused on industry, infrastructure, mobility as well as healthcare. It aims in creating technologies for more resource-efficient factories along with resilient supply chains to transform industries. Responsibilities:  The candidate is required to design software solutions supplemented with Artificial Intelligence and machine learning based on the customer requirements within architectural or design guidelines. The candidate also needs to be involved in the coding of features, bug fixing as well as delivering solutions to scripting and quality guidelines. The person is responsible for ensuring integration and submission of solutions into software configuration management system, performing regular technical coordination and timely reporting. Qualifications: The candidate must have a strong knowledge of Data Science, Artificial Intelligence, machine learning, deep learning, exploratory analysis, predictive modelling, prescriptive modelling and Cloud systems with a B.E/B. Tech/ CA/ M. Tech in science background. The candidate should have practical experience in data visualisation tools, statistical computer languages, data architecture and machine learning techniques. It is essential to have a good knowledge of querying SQL, no SQL databases, data mining techniques, AWS services, computing tools as well as end-to-end Data Science pipelines into production.

Data Scientist at Mastercard

Mastercard is known as the global technology company in the financial industry, especially payments. It has a mission to connect an inclusive digital economy to benefit everyone by making transactions safe and accessible. It works in more than 210 countries with secure data and networks, innovations and solutions. Qualifications: The candidate should have practical experience in data management, support decks, SQL Server, Microsoft BI Stack, Python, campaign analytics, SSIS, SSAS, SSRS and data visualisation tools. It is essential to have a Bachelor’s or Master’s degree in Computer Science, IT, Engineering, Mathematics, Statistics or any relevant field.

Join The Data Science Revolution With Datahour Sessions

Introduction

Discover Analytics Vidhya, Your Ultimate Data Science Destination! Our focus is on empowering our community and providing opportunities for professional growth. DataHour sessions are Expert-Led workshops designed to enhance your knowledge and skills. Don’t miss out on this chance to join the elite community of Data Scientists. Check out the upcoming DataHour schedule below and register today for a free and rewarding learning experience!

Who can Attend these DataHour Sessions?

Aspiring individuals looking to launch a career in the data-tech industry, including students and freshers.

Current professionals seeking to transition into the data-tech domain.

Data science professionals seeking to enhance their career growth and development.

The quintessential pre-task of most data-driven analysis is “stitching” multiple data sources together. Traditionally, an analyst’s language achieves this through “joins.” They “stitch” datasets together based on commonality in terms of shared entries within common columns across datasets.

🔗 Registration Link: Register Now

In this DataHour, Devavrat will introduce DeepMatch, an AI-powered matching or joining of data with easy-to-interact humans in the loop component. He will also demonstrate how it has been used for SKU mapping in Retail and Supply Chain for demand planning, transaction reconciliation in Banking and Financial Services, and Auditing in Insurance.

DataHour: Anomaly Detection in Time Series Data

From manufacturing processes over finance applications to healthcare monitoring, detecting anomalies is an important task in every industry. There has been a lot of research on the automatic detection of anomalous patterns in time series, as they are large and exhibit complex patterns. These techniques help to identify the varying consumer behavior patterns, detect device malfunctions, sensor data, monitor resource usage, video surveillance, health monitoring, etc. 

🔗 Registration Link: Register Now

In this DataHour, Parika will discuss the techniques used to identify both Point and Subsequence Anomalies in time series data. She will also cover the statistical and the predictive approaches, including CART models, ARIMA (Facebook Prophet), unsupervised Clustering, and many more.

DataHour: Building Python Dashboard using Plotly Dash

In this DataHour, Madhusudhan will demonstrate building a live updating dashboard using Plotly. He will cover using Plotly dash with Python to set up dashboard layout, create data visualizations, add interactivity, customize appearance, and use real-world datasets. The session will also cover adding buttons, interactive graphs, data tables, a grid layout, a navigation bar, and cards to the dashboard.

Madhusudhan Anand is the Co-Founder, Data Scientist, and CTO at Ambee. He is a Passionate problem-solver and customer-obsessed product manager. With about 17+ years of experience in product companies, over the last 5.5 years, he has worked with startups and has significant experience and interest in scaling products, technology, and operations. He builds products from conceptualization to prototyping and all the way to making them revenue-generating while ensuring the product, culture & team scales. He has won 5 national awards in the startup ecosystem for building products on IoT, ML (and AI), Internet (digital), and Mobile. 

DataHour Session: Natural Language Processing with BERT and GPT-3

Natural language processing (NLP) is an area of artificial intelligence that primarily focuses on understanding and processing human language. Recently, two powerful language models, BERT and GPT-3 have been developed to generate human-like texts, allowing them to engage in natural-sounding conversations.

🔗 Registration Link: Register Now

DataHour: An Introduction to Big Data Processing using Apache Spark

🔗 Registration Link: Register Now 

In this DataHour, Akshay will provide an overview of Apache Spark and its capabilities as a distributed computing system. Additionally, we will delve into internal data processing using Spark and explore techniques for performance-tuning Spark jobs. Also, this session aims to cover the concepts of parallel computing and how they relate to working with big data in Spark.

Conclusion

Don’t Delay! Reserve Your Spot Today! Register for the DataHour sessions that catch your interest, and join us for an hour of learning about the latest tech topics. If you have any questions about the session or its content, feel free to reach out to us at [email protected] or ask the speaker during the session. And if you happen to miss any part, you can catch up by watching the recordings on our YouTube channel or going through the resources shared on your registered mail Ids.

Connect

If you’re having trouble enrolling or would like to conduct a session with us. Contact us at [email protected]

Related

Data Science Immersive Bootcamp – Hands

“I have applied for various data science roles but I always get rejected because of a lack of experience.”

This is easily the most common issue I’ve heard from aspiring data scientists. They put in the hard work to learn the theoretical aspect of data science but when it comes to applying it in the real world, not many organizations are willing to take them on.

No matter how well you do in the interview round – the hiring manager always finds the lack of data science experience as the main sticking point.

So what can you do about this? It is a seemingly unassailable obstacle in your quest to become a rockstar data scientist.

We at Analytics Vidhya understand this challenge and are thrilled to launch the Data Science Immersive Bootcamp to help you overcome it!

This is an unmissable opportunity where you will get to learn on the job from data science experts with decades of industry experience.

“In the Data Science Immersive Bootcamp, we are not only focusing on classroom training – we provide hands-on internship to enrich you with practical experience.”

So you get the best of both worlds – you learn data science AND get to work on real-world projects.

Let’s Gauge the Benefits of the Data Science Immersive Bootcamp

This Bootcamp has been created by keeping Data Science professionals at heart and industry requirements in mind. Let’s dive in to understand the benefits of Data Science Immersive Bootcamp:

Learn on the job from Day 1: This is a golden opportunity where you can learn data science and apply your learnings in various projects manned by you at Analytics Vidhya during the course of this Bootcamp

Work with experienced data scientists and experts: The best experts from different verticles will come together to teach and mentor you at the Bootcamp – it is bound to boost your experience and knowledge exponentially!

Work on real-world projects: Apply all that you learn on the go! Real challenges are faced when you dive in to solve a practical problem and cruising through that successfully will hone and fine-tune your blossoming data science portfolio

Peer Groups and Collaborative Learning: Best solutions are derived when you learn with the community! And this internship gives you an opportunity to be part of several focused teams working on different data projects

Build your data science profile: You will get to present your work in front of Analytics Vidhya’s thriving and burgeoning community with over 400,000 data science enthusiasts and practitioners. You are bound to shine like a star after getting such an exhaustive learning and hands-on experience

Mock interviews: Get the best hack to crack data science interviews

Unique Features of the Data Science Immersive Bootcamp

There are so many unique features that come with this Bootcamp. Here’s a quick summary of the highlights:

Curriculum of Data Science Immersive Bootcamp

Data Science Immersive Bootcamp is one of the most holistic and intensive programs in the data science space. Here’s a high-level overview of what we will cover during this Bootcamp:

Python for Data Science

Linear Algebra

SQL and other Databases

Statistics – Descriptive and Inferential

Data Visualization

Structured Thinking & Communications

Basics of Machine Learning

Advanced Machine Learning Algorithms

Deep Learning Basics

Recurrent Neural Networks (RNN)

Natural Language Processing (NLP)

Convolutional Neural Networks (CNN)

Computer Vision

Building Data Pipelines

Big Data Engineering

Big Data Machine Learning

Wholesome Data Science is what we call it – everything you need to learn is presented in a single platter!

How to apply for the Data Science Immersive Bootcamp?

Here are the steps for the admission process to the Data Science Immersive Bootcamp:

Online Application – Apply with a simple form

Take the Fit Test – No knowledge of data science expected

Interaction with Analytics Vidhya team (Gurgaon) – Interview round to screen the best candidates

Offer Rollouts – The chosen candidates will be sent the official offer to be a part of the Bootcamp!

Offer Acceptance

Welcome to AV’s Data Science Immersive Bootcamp!

Fee Structure and Duration for the Data Science Immersive Bootcamp

Admission Fees: INR 25,000/-

Note: It is non-refundable and will get adjusted with the entire upfront payment (INR 3,50,000) or with 1st Installment (in case of Installment Payment Plan).

Option 1:  (If paying all upfront)

INR 3,51,000/-

Option 2: (Installment Payment Plan)

1st Installment – INR  99,000/-  (30 days before the start date)

2nd Installment – INR 1,25,000/- (within 60 days of the start date)

3rd Installment – INR 1,25,000/- (within 120 days of the start date)

Here are the details of the program:

Duration of Program: 9 months / 40 weeks

Internship Stipend (from month 1): Rs. 15,000/- per month

Number of Projects: 10+ real-world projects

No. of Seats in the Bootcamp: Maximum 30

And The Most Awaited Aspect – You Get A Job Guarantee!

As mentioned above, this Bootcamp will enrich you with knowledge and industry experience thus making you the perfect fit for any role in Data Science. Bridging the gap between education and what employers want – the ultimate jackpot Analytics Vidhya is providing!

Build your data science profile and network

Create your own brand

Learn how to ace data science interviews

Craft the perfect data science resume

Work on real-world projects – a goldmine for recruiters

Harvard Business Review dubbed Data Scientist the sexiest job of the 21st Century.

And do not forget to register with us TODAY! Only 30 candidates will get a chance to unravel the best of Data Science in this specialized Bootcamp.

Related

Introduction To Git For Data Science

The data science and engineering fields are interacting more and more because data scientists are working on production systems and joining R&D teams. We want to make it simpler for data scientists without prior engineering experience to understand the core engineering best practices.

We are building a manual on engineering subjects like Git, Docker, cloud infrastructure, and model serving that we hear data science practitioners think about.

Introduction to Git

A version control system called Git is made to keep track of changes made to a source code over time.

Typically, each user will clone a single central repository to their local system (referred to as “origin” or “remote”) which the individual users will clone to their local machine (called “local” or “clone”). Users “push” and “merge” their completed work back into the central repository once they have stored relevant work (referred to as “commits”) on their computers.

Difference between Git and GitHub

Git serves as both the foundational technology, for tracking and merging changes in a source code, and its command-line client (CLI).

An online platform called GitHub was created on top of git technology to make it simpler. Additionally, it provides capabilities like automation, pulls requests, and user management. GitLab and Sourcetree are two additional options.

Git for Data Science

In data science we are going to analyze the data using some models and algorithms, a model might be created by more than one person which makes it hard to handle and makes updates at the same time, but Git makes this all easy by storing the previous versions and allowing many peoples to work on the same project at a single time.

Let’s look into some terms of Git which are very common among developers

Terms

Repository − “Database” containing all of a project’s branches and commits

Branch − A repository’s alternative state or route of development.

Merge − Merging two (or more) branches into one branch, one truth is the definition of the merge.

Clone − The process of locally copying a remote repository.

Origin − The local clone was made from a remote repository, which is referred to as the origin.

Main/Master − Common names for the root branch, which is the main repository of truth, include “main” and “master.”

Stage − Choosing which files to include in the new commit at this stage

Commit − A stored snapshot of the staged modifications made to the file(s) in the repository is known as a “commit.”

HEAD − Abbreviation for the current commit in your local repository.

Push − Sending changes to a remote repository for public viewing is known as pushing.

Pull − Pulling is the process of adding other people’s updates to your personal repository.

Pull Request − Before merging your modifications to main/master, use the pull request mechanism to examine and approve them.

As we have discussed above do for that we need some commands that are generally used, lets discussed them below −

git init − Create a new repository on your local computer.

git clone − begin editing an already-existing remote repository.

git add − Select the file or files to save (staging).

Show the files you have modified with git status.

git commit − Store a copy of the selected file(s) as a snapshot (commit).

Send your saved snapshots (commits) into the distant repository using the git push command.

Pull current commits made by others into your own computer using the git pull command.

Create or remove branches with the git branch.

git checkout − Change branches or reverse local file(s) modifications.

git merge − merges branches with git to create a single branch or a single truth.

Rules for Handling Git Process Smooth

There are some rules for handling the smooth process of uploading a project over GitHub

Don’t push datasets

Git is used to tracking, manage, and store the codes but it is not a good practice to put the datasets over it. Keep track of the data there are many good data trackers available.

Don’t push secrets Don’t use the –force

−force method is used in various situations but it is not recommended to use it mostly because while pushing the code to git if there is an error, it will be displayed by the compiler or the CLI to use the force method to put the data on the server but it is not a good approach.

Do small commits with clear descriptions

Beginners developers may not be as good with the small commits but it is recommended to do the small commits as they make the view of the development process much clear and helps out in future updates. Also writing a good and clear description makes the same process much easier.

Conclusion

A version control system called Git is made to keep track of changes made to a source code over time. Without a version control system, a collaboration between multiple people working on the same project is complete confusion. Git serves as both the foundational technology, for tracking and merging changes in a source code, and its command-line client (CLI). An online platform called GitHub was created on top of git technology to make it simpler. Additionally, it provides capabilities like automation, pulls requests, and user management.

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