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This article was published as a part of the Data Science Blogathon.
Introduction on PyScript.jsWhat is PyScript.js?
It is a front-end framework that enables the use of Python in the browser. It is developed using Emscripten, Pyodide, WASM, and other modern web technologies.
Using Python in the browser does not mean that it can replace Javascript. But it provides more convenience and flexibility to the Python Developers, especially Machine Learning Engineers.
What PyScript Offers?
5. It provides flexibility to the developers. Now they can quickly build their python programs with the existing UI components such as buttons and containers.
This tutorial shows you how we can create our machine learning model with a web GUI using PyScript.
We will use the famous Car Evaluation Dataset to predict the car’s condition based on the six categorical features. We will discuss the dataset later but first, start with setting up the chúng tôi library.
Setting Up PyScript.jsThis section will set up our HTML Template and include the chúng tôi library.
We will use VSCode here, but you can choose any IDE.
1. Create a directory named as PyscriptTut.
$ mkdir PyscriptTut $ cd PyscriptTut2. Creating an HTML Template
Create an HTML template inside it named index.html
Inside this template, place the starter HTML code
Bootstrap CDN is used for Styling the Web Page
PyScript Installation
We will not install the library on our machine, we will directly import the library from the PyScript website.
Important Note:
You have to use a Local Server to run the HTML Code. Otherwise, you may face issues in importing several libraries in a python environment.
If you are using VSCode, then you can use its Live Server Extension.
Or you can also create a python server writing the below command in the terminal
Sample Code
You can try this sample code to check whether PyScript is successfully imported or not.
print(“Welcome to puscript tutorial”) for i in range(1, 10): print(i)
This is a simple program that prints the number from 1 to 9 using a for-loop.
If everything goes fine, the output looks like that
Hurray 🎉, our PyScript library is installed successfully in our template.
Creating GUIThis section will create a web GUI to use our machine learning model for training and testing.
As mentioned above, we will use Bootstrap Library for creating custom styling. I have also used inline CSS in some places.
1. Add Google Fonts CDN
2. Some CSS Configuration
Add the below code to your template. It will enable smooth scrolling on our web page and apply the above font.
* { margin: 0; padding: 0; } html { scroll-behavior: smooth; } body { font-family: ‘Montserrat’, sans-serif; }
3. Adding Bootstrap Navbar Component
<button type=”button” data-toggle=”collapse” data-target=”#navbarSupportedContent”
4. Adding Heading Content
We will create a small landing page with some texts and images.
The Source of the image used in this component can be found here.
5. Component to Train the Model
In this component, we will create some radio buttons and input texts, so that users can select which classifier they want to train and by how many tests split.
<input type=”radio” name=”modelSelection” Random Forest <input type=”radio” name=”modelSelection” Logistic Regression <input type=”radio” name=”modelSelection” MLP Classifier <input type=”radio” name=”modelSelection” Gradient Boosting
6. Component for Alert Messages
This component is used for alerts and success messages.
7. Component for checking the Training Results
In this, we can see the Accuracy and Weighted F1 Score of the selected model after training.
8. Component for selecting Car Parameters
We can select the six parameters to check the performance of the car.
The Submit will remain disabled until you train the model.
9. Component to Output the Result
This component displays the predicted value.
10. Footer (Optional)
This is the footer for our web page
Our GUI is now created, ✌
Small NoteFrom now onwards, we will train our machine learning model. We need to add libraries in the Python Environment
– pandas – scikit-learn – numpy
Importing LibrariesFirstly we will import all the necessary libraries
import pandas as pd import pickle from sklearn.model_selection import train_test_split, GridSearchCV from sklearn.linear_model import LogisticRegression from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier from sklearn.neural_network import MLPClassifier from sklearn.metrics import accuracy_score, f1_score from chúng tôi import open_url import numpy as np Dataset PreprocessingAs discussed earlier, we will use Car Evaluation Dataset from UCI ML Repository.
You can download the dataset from that link.
This dataset contains six categorical features, which are Buying Price, Maintenance Price, No. of Doors, No. of Persons, Luggage Capacity, and Safety Qualifications
6. Safety – low, mid, high
The Output is classified into four classes namely, unacc, acc, good, vgood
4. vgood – Very Good
Function to Upsample the Dataset
def upSampling(data): from sklearn.utils import resample # Majority Class Dataframe df_majority = data[(data['score']==0)] samples_in_majority = data[data.score == 0].shape[0] # Minority Class Dataframe of all the three labels df_minority_1 = data[(data['score']==1)] df_minority_2 = data[(data['score']==2)] df_minority_3 = data[(data['score']==3)] # upsample minority classes df_minority_upsampled_1 = resample(df_minority_1, replace=True, n_samples= samples_in_majority, random_state=42) df_minority_upsampled_2 = resample(df_minority_2, replace=True, n_samples= samples_in_majority, random_state=42) df_minority_upsampled_3 = resample(df_minority_3, replace=True, n_samples= samples_in_majority, random_state=42) # Combine majority class with upsampled minority classes df_upsampled = pd.concat([df_minority_upsampled_1, df_minority_upsampled_2, df_minority_upsampled_3, df_majority]) return df_upsampledFunction to read input data and return processed data.
def datasetPreProcessing(): # Reading the content of CSV file. data = pd.read_csv(csv_url_content) pyscript.write("headingText", "Pre-Processing the Dataset...") # This is used to send messages to the HTML DOM. # Removing all the null values data.isna().sum() # Removing all the duplicates data.drop_duplicates() coloumns = ['buying', 'maint', 'doors', 'people', 'luggaage', 'safety', 'score'] # Converting Categorical Data into Numerical Data data['buying'] = data['buying'].replace('low', 0) data['buying'] = data['buying'].replace('med', 1) data['buying'] = data['buying'].replace('high', 2) data['buying'] = data['buying'].replace('vhigh', 3) data['maint'] = data['maint'].replace('low', 0) data['maint'] = data['maint'].replace('med', 1) data['maint'] = data['maint'].replace('high', 2) data['maint'] = data['maint'].replace('vhigh', 3) data['doors'] = data['doors'].replace('2', 0) data['doors'] = data['doors'].replace('3', 1) data['doors'] = data['doors'].replace('4', 2) data['doors'] = data['doors'].replace('5more', 3) data['people'] = data['people'].replace('2', 0) data['people'] = data['people'].replace('4', 1) data['people'] = data['people'].replace('more', 2) data['luggaage'] = data['luggaage'].replace('small', 0) data['luggaage'] = data['luggaage'].replace('med', 1) data['luggaage'] = data['luggaage'].replace('big', 2) data['safety'] = data['safety'].replace('low', 0) data['safety'] = data['safety'].replace('med', 1) data['safety'] = data['safety'].replace('high', 2) data['score'] = data['score'].replace('unacc', 0) data['score'] = data['score'].replace('acc', 1) data['score'] = data['score'].replace('good', 2) data['score'] = data['score'].replace('vgood', 3) upsampled_data = upSampling(data) return upsampled_dataLet’s understand these above functions in more detail:
1. Firstly, we have read the CSV File using the Pandas library.
2. You may be confused by this line py script.write(“headingText”, “Pre-Processing the Dataset…”).
This code updates the messages component in the HTML DOM that we have created above.
You can write any message in any HTML Tag
3. Then, we have removed the null values and the duplicates. But luckily, this dataset does not contain any null values.
4. Further, we have converted all the categorical data into numerical data.
5. Finally, we have performed upsampling of the dataset.
You can observe that the number of samples in one particular class is far more than in the other classes. Our model will be biased towards a specific class because it has very little data to train on other classes.
So we have to increase the number of samples in other classes. It is also called Upsampling.
I have created a separate function named upSampling that will upsample the data.
Now we have an equal number of samples for all the classes.
Training the ModelFunction to check which machine learning model is selected by the user for training.
def model_selection(): selectedModel = document.querySelector('input[name="modelSelection"]:checked').value; if selectedModel == "rf": document.getElementById("selectedModelContentBox").innerText = "Random Forest Classifier"; return RandomForestClassifier(n_estimators=100) elif selectedModel == "lr": document.getElementById("selectedModelContentBox").innerText = "Logistic Regression"; return LogisticRegression() elif selectedModel == "gb": document.getElementById("selectedModelContentBox").innerText = "Gradient Boosting Classifier"; return GradientBoostingClassifier(n_estimators=100, learning_rate=1.0, max_depth=1, random_state=0) else: document.getElementById("selectedModelContentBox").innerText = "MLP Classifier"; return MLPClassifier()Function to train the model on the chosen classifier.
def classifier(model, X_train, X_test, y_train, y_test): clf = model clf.fit(X_train, y_train) y_pred = clf.predict(X_test) y_score = clf.fit(X_train, y_train) acc_score = accuracy_score(y_test, y_pred) f1Score = f1_score(y_test, y_pred, average='weighted') return acc_score, model, f1Score def trainModel(e=None): global trained_model processed_data = datasetPreProcessing() # Take the Test Split as an input by the user test_split = float(document.getElementById("test_split").value) # If the test split is greater than 1 or less than 0 then we will throw an error. pyscript.write("headingText", "Choose Test Split between 0 to 1") return document.getElementById("testSplitContentBox").innerText = test_split; X = processed_data[['buying', 'maint', 'doors', 'people', 'luggaage', 'safety']] y = processed_data['score'] # Splitting the Dataset into training and testing. X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_split, random_state=42) # Below function can return the classification model choosen by the user model = model_selection() pyscript.write("headingText", "Model Training Started...") acc_score, trained_model, f1Score = classifier(model, X_train, X_test, y_train, y_test) pyscript.write("headingText", "Model Training Completed.") # Writing the value of accuracy and f1-score to the DOM document.getElementById("accuracyContentBox").innerText = f"{round(acc_score*100, 2)}%"; document.getElementById("f1ContentBox").innerText = f"{round(f1Score*100, 2)}%"; # Below code is to enable the Model Training Button when the Model is successfully trained. document.getElementById("submitBtn").classList.remove("disabled"); document.getElementById("submitBtn").disabled = False; document.getElementById("trainModelBtn").classList.remove("disabled"); document.getElementById("trainModelBtn").disabled = False; if e: e.preventDefault() return False Testing the ModelIn this section, we will test our model on the six parameters that we have discussed above.
Below is the function to test the model.
def testModel(e=None): buying_price = int(document.getElementById("buying_price").value) maintanence_price = int(document.getElementById("maintanence_price").value) doors = int(document.getElementById("doors").value) persons = int(document.getElementById("persons").value) luggage = int(document.getElementById("luggage").value) safety = int(document.getElementById("safety").value) arr = np.array([buying_price, maintanence_price, doors, persons, luggage, safety]).astype('float32') arr = np.expand_dims(arr, axis=0) result = trained_model.predict(arr) condition = "" if result[0] == 0: condition = "Unaccepted" elif result[0] == 1: condition = "Accepted" elif result[0] == 2: condition = "Good" else: condition = "Very Good" pyscript.write("resultText", f"Predicted Value: {condition}") if e: e.preventDefault() return FalseFirstly, we will take the input from the user and the feed that input to the model for prediction. Then finally, we output the results.
Our machine learning model is now trained.
Conclusion
Deployed Version – Link
Before PyScript, we don’t have any proper tool to use Python on the client-side. Frameworks such as Django or Flask can mainly use Python on the backend. In recent years, Python has grown its population immensely. It has been used in Machine Learning, Artificial Intelligence, Robotics, etc.
In this article, we have trained and tested a machine learning model completely in HTML language. You can increase the model’s accuracy by tuning some hyperparameters or searching for the best parameters using Grid Search CV or Randomized Search CV.
The main focus of this article is to use the chúng tôi library, not to achieve a high accuracy classification model.
4. Finally, we have written the code to test the model based on the user’s input.
Do check my other articles also.
Thanks for reading, 😊
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Top Data Science Projects To Add To Your Portfolio In 2023
Introduction
2023 is a year that proved nothing is better than a Proof of Work to evaluate any candidate’s worth, initiative, and skill.
Pursuing any data science project will help you polish your resume. These projects will not only deepen an understanding of the concepts but also, help you gain some practical experience in the data science industry. Moreover, they serve as a great proof of work rather than merely completing courses.
Students and even professionals create their own portfolios or do professional projects that are available on various websites. These projects will give you an opportunity to network with other professionals in the same industry.
To develop a professional portfolio, it is important for you to have different projects. Each project should be well-structured and handled professionally. With your delivery skills for a particular project, you could get a job opportunity, as well. Thus, it is important for you to make sure that you develop specific skills via these projects.
As a data scientist, you must have the following skillsets in your portfolio:
communication
collaboration
technical competence
know the ‘data’ at a deeper level
take initiatives and experiment
domain expertise
Components that a Data Science Project must entail:
Problem statement: This is the prime component of any project. Your project will solve this problem and state various approaches to resolve the issues in the current model.
Dataset: This is one of the most important features of your project. It isn’t easy to find genuine, huge data. So, take your time and find datasets from authentic sources.
Algorithm: There are different algorithms that could be used to analyze the data and predict the results. Some of these algorithms are Regression Algorithms, Regression Trees, Naive Bayes Algorithm, and Vector Quantization.
Training Models: These models will help you to predict accurate outcomes of your project. Thus, it is important for you to use proper training techniques, against various inputs and outputs.
Read this article to understand how you can choose the most appropriate project for yourself.
Add these Projects to your professional journey!Real-ESRG
Language: Python
This project aims at developing practical algorithms which will help to restore the damaged images. We know how important it is for us to have crystal clear images, be it our lost photos or uploading images to our blog.
Robust Video Matting (RVM)Language: Python
RVM is a new branch of art performance. It can perform matting in real-time, uses a recurrent neural network to process the videos.
This project is going to be fun! This one is specially going to be great for people who aspire to become influencers or might like to create videos, recording all your special videos. Here, you will be able to have a green screen behind or any other background of your choice. So, while sitting at home. You could take some pleasure by feeling the beach or mountains…VIRTUALLY!
GFPGANLanguage: Python
GFPGAN develops a practical algorithm for real-face (or blind) restoration. In this project, you will be able to work on images that aren’t clear in quality and are of blind people. So, you will be required to make their facial features, especially their eyes clear.
Sometimes, it could be challenging because not all features of your face can be restored properly. This project will help you learn the technology where you will be able to restore low-quality face images via semantic aware style transformation.
Read our latest article on Implementing Computer Vision.
WHAT
Language: Python and DockerFile
Has it happened to you? That you receive a text from someone whom you don’t know and they happen to reveal your personal details, maybe some friends playing a prank on you or someone blackmailing you?
Well, not anymore! Once you pursue, the ‘what’ project, you will be able to know the unknown. Hahaha…sounds mysterious? Jokes apart, this project will help you to find details like emails, IP addresses, and more.
Pursue this project to know more!
TextualLanguage: Python, MakeFile, and TypeScript
This project is inspired by modern web development. This project uses Rich to render the rich text, so anything that Rich can render could also be done in Textual. Some of the examples: animation, calculator, grid layout, a simple textual app with scrolling markdown view – all of this could be done under this project.
Change DetectionThis project will help you learn about making simple changes on the self-hosted sources, open-source websites that help to monitor the changes and provide notifications for each change that takes place. The focus on change type will be text-related changes.
For example: On the government websites, when COVID-19 related news changes, with respect to the number of new cases/ number of people, died/ number of recovered people, and so on.
SeaLion
This project is designed to teach today’s aspiring MI Engineers, the popular machine learning concepts which give an opportunity to use different ways of application. Once you complete this project, you will be able to learn a lot of topics from machine learning via using different algorithms.
SeaLion was developed by Anish Lakkapragada, as a freshman in high school. The library is meant for beginner-level data science enthusiasts who would be interested in solving the standard libraries like iris, breast cancer, swiss roll, etc.
Deploy Machine Learning Model using Flask (with Code)
This project is one of the most practical projects you would be doing. This will not only help you learn here, in the process of completion. But also, in every sphere of data science. This project will help you learn how you will be able to put any of your machine learning models into production.
This project will introduce you to Flask which is a web application framework written in Python. The flask has multiple modules and will help web developers to write their applications without having to worry about the details like protocol management, thread management, and so on. It gives you an opportunity to work on different web applications and gives you the necessary tools that help you build a web application.
Read more on using flask for Data Science here.
Time series analysis is a vital component of the Data Science and Engineering industry where important concepts like key statistics and detecting regressions are used to forecast future trends.
KatsKats is a toolkit where you could analyze the time series data. This is a generalized project which can be used even by new people in the data science industry. The project could have an extensive framework where you could perform time series analysis. This would include understanding different concepts like key statistics and characteristics, detecting change points and anomalies.
Time Series using MerlionMerlion is a python project which will help you to polish your concepts of machine learning. This is part of the time series intelligence which includes loading and transforming the data. It will help you to learn various time series learning tasks, which include, forecasting, anomaly detection, etc. This project will specifically focus on providing engineers and researchers with, one-stop solution to develop models for multiple time-series datasets.
This project has different modules which makes it easier for the data scientists to pursue. Also, it provides a unique evaluation framework that simulates the live deployment and re-training of a model in production.
ConclusionRead about Software Engineering process for an effective Data Science project.
Happy Learning!
Related
Top 7 Free Datasets Sources To Use For Data Science Projects
Free datasets sources for data science enthusiasts
Data is preliminary for companies and corporations to analyze and obtain business intelligence. It helps in finding the correlations between the data and the unique insights for a better decision-making process. And for these
Google Cloud Public DatasetMost of us think that Google is just a search engine, right? But it is way beyond. Several datasets can be accessed through the Google cloud and analyzed to fetch new insights from the data. Google cloud has more than hundreds of
Amazon Web Services Open Data RegistryAmazon Web Services has the largest number of
Data.govThe US government is also keen on data science, as most of the tech companies are located in Silicon Valley. chúng tôi is the main repository of the US government’s open datasets that can be used for research, developing data visualizations, mobile applications, and creating the web. This is an attempt of the government to become more transparent in terms of access without registering. But some of the datasets need permissions before downloading them. chúng tôi has diverse varieties of
KaggleKaggle has more than 23,000 public datasets that can be downloaded for free. You can easily search for the dataset that you’re looking for and find them hassle-free ranging from health to cartoons. The platform also allows you to create new public datasets and can also earn medals along with the titles such as Expert, Master, and Grandmaster. The competitive Kaggle datasets are more detailed than the public datasets. Kaggle is the perfect place for data science lovers.
UCI Machine Learning RepositoryIf you are looking for interesting datasets then UCI Machine Learning Repository is a great place for you. It is one of the first and oldest data sources that are available on the internet since 1987. The datasets of the UCI are great for machine learning with their easy access and download options. Most of the datasets of UCI are contributed by different users so the data cleanliness is a little low. But UCI maintains the datasets for using them for ML algorithms.
Global Health ObservatoryIf you are from a medical background then Global Health Observatory is a great option for creating projects on global health systems and diseases. The WHO has made all their data public on this platform. This is for the good quality health information available worldwide. The health data is characterized according to various communicable and noncommunicable diseases, mental health, morality, medicines for better access.
EarthdataData is preliminary for companies and corporations to analyze and obtain business intelligence. It helps in finding the correlations between the data and the unique insights for a better decision-making process. And for these datasets sources are important to help you with your data science projects . But luckily there are many online data sources to fetch you free datasets to help with your projects by just downloading them absolutely free. Let’s learn more about the top 7 free dataset sources to use for data science projects in this chúng tôi of us think that Google is just a search engine, right? But it is way beyond. Several datasets can be accessed through the Google cloud and analyzed to fetch new insights from the data. Google cloud has more than hundreds of datasets that are hosted by BigQuery and cloud storage. Google’s machine learning can be helpful in analyzing datasets such as BigQuery ML, Vision AI, Cloud AutoML, etc. Also, Google’s Data Studio can be used to create data visualization and dashboards for better insights. These datasets have data from various sources such as GitHub, United States Census Bureau, NASA, and BitCoin, and many more. You can access these datasets free of cost.Amazon Web Services has the largest number of datasets on their registry. It is very easy to download these datasets and use them to analyze the data on the Amazon Elastic Compute Cloud. It also employs various tools such as Apache Spark, Apache Hive, and more. The Amazon Web Services is an open data registry that is part of the AWS Public Dataset Program that focuses on democratizing the access of data so that it is available to everybody. AWS open data registry is free but allows you to own a free AWS chúng tôi US government is also keen on data science, as most of the tech companies are located in Silicon Valley. chúng tôi is the main repository of the US government’s open datasets that can be used for research, developing data visualizations, mobile applications, and creating the web. This is an attempt of the government to become more transparent in terms of access without registering. But some of the datasets need permissions before downloading them. chúng tôi has diverse varieties of datasets relating to climate, agriculture, energy, oceans, and ecosystems.Kaggle has more than 23,000 public datasets that can be downloaded for free. You can easily search for the dataset that you’re looking for and find them hassle-free ranging from health to cartoons. The platform also allows you to create new public datasets and can also earn medals along with the titles such as Expert, Master, and Grandmaster. The competitive Kaggle datasets are more detailed than the public datasets. Kaggle is the perfect place for data science chúng tôi you are looking for interesting datasets then UCI Machine Learning Repository is a great place for you. It is one of the first and oldest data sources that are available on the internet since 1987. The datasets of the UCI are great for machine learning with their easy access and download options. Most of the datasets of UCI are contributed by different users so the data cleanliness is a little low. But UCI maintains the datasets for using them for ML chúng tôi you are from a medical background then Global Health Observatory is a great option for creating projects on global health systems and diseases. The WHO has made all their data public on this platform. This is for the good quality health information available worldwide. The health data is characterized according to various communicable and noncommunicable diseases, mental health, morality, medicines for better chúng tôi you are looking for data related to Earth or Space then, Earthdata is your place. This is created by NASA to provide datasets based on Earth’s atmosphere, oceans, cryosphere, solar flares, and tectonics. It is a part of the Earth Observing System Data and Information System that helps in collecting and processing the data from various NASA satellites, aircraft, and fields. Earthdata also has tools for handling, ordering, searching, mapping, and visualizing the data.
Data Science Resume: How To Make It More Appealing?
Know the best way to write your Data Science Resume and make a statement to top-tier tech companies.
The concept of life is simple, you need oxygen to live and a resume to get a job. It is essential to write an eye-catching resume to be first in a race, especially if you are applying for a
data science
job. Even if you are not a fan of writing resumes, you cannot ignore the fact that most companies require a resume in order to apply to any of their open jobs, and it is often the first layer of the interview process.
So does it matter how you write personal, educational, and professional qualifications and experience details in a resume? Yes, it does, and here are some tips about how to make your resume more appealing that will catch the eye of a recruiter or interviewer.
1. Always write a resume in briefRule number 1, always keep your resume short and engaging. Try to get all your details on one page because recruiters receive thousands of resumes every day and have a minute to look over someone’s resume and make a decision. Therefore make sure your resume speaks on your behalf and makes an impression.
2. Customize your resume according to the job descriptionWhile you unquestionably can make a solitary
resume and send that to each job you apply for, it would be a smart move to attempt to add customized changes depending upon the job description would positively intrigue the recruiter.
This doesn’t mean you have to do rework and upgrade your resume each time you go after a position. However, if you notice significant skills mentioned in the work posting (for example, skills like
Data Visualization
or Data Analysis) you should be certain about the resume you’re sending focuses on those skills and increase your chances of getting that job.
3. Pick a right layoutWhile each resume will consistently incorporate data like past work insight, abilities, contact data, and all, you ought to have a resume that is remarkable to you. That starts with the visual look of the resume, and there are various approaches to achieve a one-of-a-kind design.
Remember that the type of resume layout you pick is also significant. In case you’re applying to
with a more customary feel attempt to focus on a more traditional, curbed style of resume. In case you’re focusing on an organization with more of a startup vibe, you can pick a layout or make a resume with more colors and graphics.
4. Contact DetailsAfter the selection of your resume’s layout next step is to add contact detail. Here are some important things you need to remember about your contact details and what to put there in the context of a data science resume specifically:
If you are applying for a job in a different city and don’t want to relocate it is better not to add your entire physical address, only put in your city and state you live.
The headline underneath your name: reflects the job you’re looking to get rather than the job you currently have. If you’re trying to become a data researcher, your headline should say “Data researcher” even if you’re currently working as an event manager.
5. Data Science Projects/Publications areaQuickly following your name, feature, and contact data ought to be your Projects/Publications area. In any resume, particularly in the technology business, you should focus on highlighting the things you have created.
For a data science resume, this may incorporate machine learning projects, AI projects, data analysis projects, and more. Hiring organizations need to perceive what you can do with your mentioned skills. This is the segment where you can flaunt.
6. Highlight your skillsAt the point when you portray each project, be pretty specific about your abilities, tools, and innovations you utilized, how you made the project. Indicate the coding language, any libraries you utilized, and more. The more talk about your skills and key tools the better.
7. Professional Experience 8. About EducationIf you have relevant work experience to showcase, it is better to add your educational details closer to the bottom. But if you are fresher and applying for your first job then, in that case, you have to highlight your qualification.
9. Last thing to doWhile you unquestionably can make a solitary data researcher Remember that the type of resume layout you pick is also significant. In case you’re applying to tech companies
Top 5 Legit Ways To Make Money As A Data Science Influencer
The top 5 legit ways to make money as a data Science influencer by leveraging their knowledge and expertise Intro:
Data Science is an interdisciplinary field comprising collecting, manipulating, storing, and analyzing data. The surge in data science with new technologies being developed. Professionals with a unique set of skills and knowledge can be leveraged to make money. However, there are many legitimate ways to make money as a data science influencer.
Here are the top 5 legit ways to make money as a Data Science Influencer:
Multiple Social Media Content Creation
Influencers do make upload various content not only on one social media platform but on their multiple social media accounts. It is an effective way to reach a wider audience and establish your brand. The platforms include Instagram, Facebook, LinkedIn, YouTube, Twitter, TikTok, etc. Influencers should be active in at least as many platforms as possible as it can open themselves to more potential ad revenue, partnerships, and brand opportunities. Influencers tend to use each platform differently but with the same purpose. For eg: Instagram is ideal for sharing images, short videos, etc whereas LinkedIn is ideal for sharing long, form content such as blog posts and articles. Thus, it’s important to tailor the content to each platform and engage with the followers to build a strong community.
Affiliate Marketing
Another popular way to make money for data science influencers is affiliate marketing. Being an affiliate, influencers need to promote a brand, product, or any similar service and earn commission through the Influencer’s unique affiliate link. Influencers promote products that align with their ethics, and sharing honest opinions about the same with an experience is required in this way. This method of marketing by data science influencers enables the audience to find the products’ benefits depending on the influencer’s content. Data science influencers usually promote products in the same industry like software or tools for Data analysis and ML. The main thing about affiliate marketing is that it requires a same to be fully closed before any commission payment is released.
Teaching, Training, and Consulting Services
As a data science Influencer, one must master all trades when helping a company in data science. Al the concepts. Services like teaching, training, and consulting enable influencers to monetize their knowledge. They can sell this knowledge by taking online classes and creating courses and workshops on a topic related to data science. Keeping the prices lesser compared to other courses brings trust from the audience to you. A professional in data science will be able to create good quality educational content. Other than teaching, influencers do help with training to improve the data science capabilities of companies thus making companies make better data-driven decisions. Therefore, these services help companies to optimize their data science operations.
Collaborate with other Influencers and Creators
Influencers team up with other Influencers and Creators that help leverage and engage more followers and a perfect way to earn more money. Not only this, it expands the reach and credibility as well. As said, influencers do promote products or services that align with their values and interest and to collaborate with them, they look for the same. This helps in sharing expertise and learning from them in return. Collaboration not only helps earn but also helps share your expertise with other Influencers and creators and learn from them. It gains you reach and more followers. The different ways of collaboration include co-creation, hosting joint events, etc, etc. The only thing to be sure of is the person you are collaborating with, develop a plan and leverage each other’s audiences.
Product Creation and Selling
Learnbay: Most Acknowledged Data Science Institute Offering Comprehensive Data Science Courses
Data has become an important part of everybody’s life. Without data there is nothing. Data mining for digging insights has marked the demand for gaining knowledge of using data for business strategies. Data science is not limited to only consumer goods or tech or healthcare. There is a high demand to optimize business processes using data science from banking and transport to manufacturing. Therefore, the field of data science is growing with increasing demand.
Developing Analytical and Technical SkillsThe institute aims at securing working professionals' careers by assisting them in developing analytical and technical skills. This will enable them to make a transition into high-growth analytical job roles by leveraging their own domain knowledge and work experience at an affordable cost.
Data Science CoursesPresently, Learnbay is offering six different data science courses as follows: • Business Analytics and Data Analytics Programs for working professionals with 1+ years of experience in any domain. Course duration: 5 months with 200+ hours of classes. Project: 1 Capstone project and more than 7 real-time projects. Course Fee: 50,000 INR • Data Science and AI Certification for working professionals with 1+ years working experience in any domain Course duration: 7 months with 200+ hours of classes. Project: 2 Capstone projects and more than 12 real-time projects. Course Fee: 59,000 INR • AI and ML Certification to Become AI Expert in Product Based MNCs for working professionals with 4+ years of working experience in the technical domain Course duration: 9 months with 260+ hours of classes. Project: 2 Capstone projects and more than 12 real-time projects. Course Fee: 75,000 INR • Data Science and AI Certification for Managers and Leaders with 8 to 15 years of working experience in any domain Course duration: 11 months with 300+ hours of classes. Project: 3 Capstone projects and more than 15 real-time projects. Course Fee: 75,000 INR Course duration: 9 months with 300+ hours of classes. Project: 2 Capstone projects and more than 12 real-time projects. Course Fee: 95,000 INR. • Industrial Training in AI and Data Science for Fresh Graduates Course duration: 4 months with 200+ hours of classes. Project: 1 Capstone project and more than 7 real-time projects. Besides, there is a 6-month internship program. Course Fee: 39,999 INR.
Key Features of Learnbay Courses• 1to1 learning supports via complete live interactive classes, additional discussion sessions, etc. • 24/7 instant tech support. • Regularly updated learning modules. • Flexible installment options for course fees. • Lifetime free access to the premium learning materials and recorded videos of the attended classes. • Hands-on live industrial project-based learning.
About the InitiatorKrishna Kumar, the Founder of Learnbay, has observed the data-related job market for different industries as well as the data science training platforms very closely. He founded Learnbay in 2023. Although he started his journey with Learnbay as a founder, he worked for the institute from the very grassroots. To understand students' expectations, he took classes, conducted career counseling sessions, and provided personalized doubt clearance assistance by himself. During that time, he worked with the motto of staying connected with his students directly and revealed many of the hidden facts of the data science training business/ teaching platforms. He found that most of his students having doubts concerning the efficacy of data science courses available in the market from the perspective of learning support- even after paying a fair amount of course fees. They came to the institute with the hope of a complete industry-grade data science learning experience with dedicated learning support at affordable prices. He started focusing on the efficacy of learning assistance and placement support of his institute's courses from that time. Within one year, the institute got many impressive responses from the students. Even though he has plenty of expert faculties (trainers, counselors, project organizers, etc) today, still at some level, he maintains direct interaction with each of the Learnbay students. Based on their feedback, he keeps updating, altering, and upgrading the institute's learning modules, teaching approaches, and learning supports.
Personalized Data Science Career CounsellingThe edge of the institute’s Analytics and Data Science Program over other institutes in the industry is owing to the following factors: • Instead of a generalized course, the institute offers different courses according to Student’stheir personal career needs. • It offers personalized data science career counseling to help a student in investing in the course that best his present working experience and future growth. • Its placement assistance helps in securing a student’s first data science job. • It offers the flexibility of attending multiple sessions of the same modules instructed by different instructors for better understanding.
Internships and Placements Training on Analytical Tools Notable Awards and AchievementsThis year (2023), Learnbay is stepping into the successful journey of a total of 5 years. Within these five years, it has grown a lot and currently holds an excellent reliability percentage from data science aspirants/training seekers. In the last five years, the institute has earned highly positive responses and feedback from students, professionals, and new data science aspirants. Leranbay has already achieved industrial collaboration from the IT giant, IBM. It got placed in the top seven data science institutes listed by chúng tôi The institute ranked 3 for Bangalore and 1 for Chennai locations. Even being a Bangalore-based organization, it got massive recognition across the different metro cities of India, like Hyderabad, Kolkata, Mumbai, Delhi, Mumbai, etc. The course review of Learnbay is 4.8 on Google.
Foreign University Certification, a Key ChallengeInitially, the students were showing more interest infor foreign university certification tag, even if they had to pay three times more than the actual fees. But as mentioned earlier, the key mission of the institute is to offer the appropriate learning guide to the career transformation seeker and not to confuse or divert the students with decorative and eye-catching staff. In the real-world data science job market, what matters only is the hands-on experience and project work. Recruiters are not even interested in the student certification tag. So, the institute keeps enriching the course efficacy from the hands-on learning perspective and project works without focusing on the certification tag. Its efforts got rewarded with the IBM collaboration. From last year, the scenario has entirely changed. The continuous success of the students and plenty of available data science job market analytical insight now support its training approach. Although plenty of competitors started providing such university tags, still it's not effective for its colossal student base.
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