Trending February 2024 # Top Data Science Projects To Add To Your Portfolio In 2023 # Suggested March 2024 # Top 6 Popular

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Introduction

2024 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!

GFPGAN

Language: 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!

Textual

Language: 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 Detection

This 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.

 Kats

Kats 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 Merlion

Merlion 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.

Conclusion

Read about Software Engineering process for an effective Data Science project.

Happy Learning!

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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 Dataset

Most 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 Registry

Amazon Web Services has the largest number of

Data.gov

The 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

Kaggle

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 lovers.  

UCI Machine Learning Repository

If 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 Observatory

If 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.  

Earthdata

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 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.

Make Amazing Data Science Projects Using Pyscript.js

This article was published as a part of the Data Science Blogathon.

Introduction on PyScript.js

What 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.js

This 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 PyscriptTut

2. 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 GUI

This 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 Note

From now onwards, we will train our machine learning model. We need to add libraries in the Python Environment

– pandas – scikit-learn – numpy

Importing Libraries

Firstly 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 Preprocessing

As 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_upsampled

Function 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_data

Let’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 Model

Function 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 Model

In 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 False

Firstly, 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, 😊

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

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Meet These Top Successful Data Science Companies For 2023

Data science is a new field that is constantly growing and evolving. With so many tools being developed for brands to communicate with the audience, it is quite pertinent for marketers to figure out the needs and demands of these audiences, and what better tool to get such insights other than data science? Here are the top data science companies that data professionals can choose from in 2023.  

UpGrad

UpGrad is an online platform that offers educational services on various topics like digital marketing, product management, entrepreneurship, data analytics, data-driven management and digital technology management. Founded in 2024, the company collaborates with world-class faculty and industry to facilitate access to career-oriented courses and assists Indian students and working professionals who want to upgrade their careers.  

Urban Company

Urban Company (formerly UrbanClap) is India and the UAE’s largest home services company. It is an all-in-one platform that helps users hire premium service professionals, from beauticians and masseurs to sofa cleaners, carpenters, and technicians. Since its inception, Urban Company has built a network of 25,000+ trained service professionals and served over 5 million customers across major metropolitan cities of India, Dubai, Abu Dhabi, Sydney, and Singapore.  

Verizon Data Services India

Verizon Data Services India is one of the world’s leading providers of technology, communication, information and entertainment products. Founded in 2000, the company is transforming the way people, businesses and things connect. As innovation hub, Verizon India is a big part of the global teams that brought 5G to life. The company plays a critical role in both the development of new technologies and the day-to-day operation of business, building systems and working on initiatives to help deliver unmatched experiences to consumers and businesses.  

Wipro

Wipro Limited is a leading global information technology, consulting, and business process service company. Wipro harnesses the power of cognitive computing, hyper-automation, robotics, cloud, analytics, and emerging technologies to help its clients adapt to the digital world and make them successful. The company is recognized for its comprehensive portfolio of services, a strong commitment to sustainability, and good corporate citizenship.  

Zeta Suite

Data science is a new field that is constantly growing and evolving. With so many tools being developed for brands to communicate with the audience, it is quite pertinent for marketers to figure out the needs and demands of these audiences, and what better tool to get such insights other than data science? Here are the top data science companies that data professionals can choose from in 2023.UpGrad is an online platform that offers educational services on various topics like digital marketing, product management, entrepreneurship, data analytics, data-driven management and digital technology management. Founded in 2024, the company collaborates with world-class faculty and industry to facilitate access to career-oriented courses and assists Indian students and working professionals who want to upgrade their careers.Urban Company (formerly UrbanClap) is India and the UAE’s largest home services company. It is an all-in-one platform that helps users hire premium service professionals, from beauticians and masseurs to sofa cleaners, carpenters, and technicians. Since its inception, Urban Company has built a network of 25,000+ trained service professionals and served over 5 million customers across major metropolitan cities of India, Dubai, Abu Dhabi, Sydney, and Singapore.Verizon Data Services India is one of the world’s leading providers of technology, communication, information and entertainment products. Founded in 2000, the company is transforming the way people, businesses and things connect. As innovation hub, Verizon India is a big part of the global teams that brought 5G to life. The company plays a critical role in both the development of new technologies and the day-to-day operation of business, building systems and working on initiatives to help deliver unmatched experiences to consumers and businesses.Wipro Limited is a leading global information technology, consulting, and business process service company. Wipro harnesses the power of cognitive computing, hyper-automation, robotics, cloud, analytics, and emerging technologies to help its clients adapt to the digital world and make them successful. The company is recognized for its comprehensive portfolio of services, a strong commitment to sustainability, and good corporate citizenship.Zeta® is in the business of providing a full-stack, cloud-native, API-first neo-banking platform including a digital core and a payment engine for the issuance of credit, debit, and prepaid products that enable legacy banks and new-age fintech institutions to launch modern retail and corporate fintech products. Its cloud-based smart benefits suite called Zeta Tax Benefits focuses on digitizing tax-saving reimbursements for employees, like mobile reimbursements, fuel reimbursements, gadget reimbursements, gift cards, and LTA.

How To Build A Career In Data Science

Is there a sector with better job prospects than Data Science? It’s unlikely. Virtually every company now relies heavily on data analytics software – which requires data pros to use most effectively.

In this Webinar, we’ll discuss:

How to get started in Data Science — without a four year college degree.

Some of the most meaningful and lucrative career paths in Data Science.

General tips on building a career in Data Science.

The remarkable future of Data Science as a career option.

Please join this wide-ranging discussion with a top leader in the Data Science sector, Kirk Borne, Principal Data Scientist, Booz Allen Hamilton

Borne:

You don’t need that, actually. I think I have to focus on a two-stage process here. The first step is just getting your foot in the door, which starts by just learning the skills of data science, the coding, the algorithms, the techniques, the methods, the process, all these things.

But if you’re really going to have a long-standing career, I always say that a degree gives you that extra career of padding, that is when the organizations look to promote people, maybe to leadership positions or whatever, it’s not just that you happen to know some coding skills, there’s a lot more that goes into that, and that comes with the things you learn in formal education programs, which are outside of the sciences, right?

The PhD is a research degree. Okay, so if you want to be a research scientist, that is where you want to go, but most data scientists aren’t gonna be research scientists, and by that I mean, you’re actually publishing papers in research journals, peer-reviewed journals, peer-reviewed conferences, probably at an academic institution trying to get tenure.

But if you want to have a successful data science analytics career, I’d say having a master’s degree teaches those professional skills of communication, leadership, collaboration, the things that go beyond just the academic stuff you learn in bachelor’s and beyond, and different from the research things you learn in a PhD program.

So I say, yeah, you can get into this field right away without a degree, but for the long-term career success, think about the collegiate education as well.

Borne:

The number of jobs opening is far and excess than the number of people available. Once you’ve certified in any of those things, you’re gonna get a job.

I know a number of data scientists who’ve gone on to become founders of companies, so they’re sort of managing a company now. So, a master’s in business analytics is a pretty impressive thing to have under your belt as well, because then that business analytics gives you both the analytics and the business experience.

But I do wanna say yes, there are certification programs. [There’s the] certified analytics professional, the CAP certification, but there’s also lots of boot camps. So boot camps can teach you skills like in 12 weeks or 16 weeks that will get you the job.

There’s also master’s, there’s a lot of master’s degree programs that are basically 11-month programs, so you get the full master’s degree, but it’s a full-time job, you can’t have a job or a life, pretty much for 11 months.

And master’s programs are different from certifications in that any college degree programmers require state accreditation, and they have to meet certain minimum standards, like 30 credit hours, certain number of courses. And whereas at boot camp, you just take a boot camp in Python and you can get their Python certification and go get a Python job, there’s no sort of state university regulation over a boot camp.

Borne:

So anyway, so I think in terms of specific jobs, the AI engineer, machine learning engineer, cloud engineer, they surpass data scientists in terms of lucrative sort of salary. and the reason I say that is because these are the people who actually have to build it out, deploy it, put it in their production, keep it running. Data scientists are also well paid and you can have a really nice satisfying job as a data scientist, but most of the time you’re building models, you’re playing with data, tweaking with data, exploring data, finding the right algorithms. And that’s fine, that’s great, and that’s sort of what gets the foot in the door towards business value creation from the data, which is really what my message always is, is focus on the business value creation.

When it’s deployed, put into production and maintained, and that’s where the AI engineer, machine learning engineer, cloud engineer, is gonna be the person or team of people who is going to accomplish that. So everyone has a value in the chain, but that engineer who’s gonna deploy, build, and keep it in production is the one who can say, “I’ll take any salary I want.” [laughter]

So if you’re gonna build that [extensive deployment], you have to have a way more capability than your traditional data scientist, but, nevertheless, people are being hired as AI engineers and machine learning engineers because they’re being hired to do what data scientist’s job, which is to explore the data and build models from the data. And the job title doesn’t really match what I would call the data scientist job, and vice versa.

Borne:

A few years ago, I starting thinking of sort of the key skills or soft skills… I should say aptitudes, not really a skill, soft skill of a successful data scientist, and things like being curious and creative and critical thinking, collaborator, communication. I started thinking, “Oh, those things all start with the letter C.”

But I think for sure, being a curious person, I mean, I can just say, for example, we had students in our PhD program at the university who, in some cases, were not curious people. I can just say it bluntly. That is, they just… When they put together a proposal to do a doctoral dissertation, it was really, “I wanted to build this software system to do data science.”

But the one we’ve already hit upon is this continuous lifelong learning, I mean for me that’s super-duper important. But another big important one, which you may not think of, is number 10 on this list, which says “consultative.” If you’re doing data science for a company, an employer, a stakeholder, whoever, you have to be able to communicate. Not just communicate, but listen to what they’re saying and ask the right questions to make sure you build the right system, so that’s really a business focus.

The principal of system engineering is, there’s a difference between building the system right and building the right system. So they followed the letter of the law and the requirements document, and they built the system right, but it was completely not functional for science and the research needed. It didn’t build the system that scientists would want to use.

It’s hard to even say exactly why. My first day on the job, I worked at the Hubble project 10 years, and it was in my seventh year that I got appointed to be this NASA project scientist for the data archive. And my first day on the job, the previous guy who was the archive project scientist handed a big box to me, about the size of a typical Xerox box full of reams of paper. Literally thousands of pages. There was a lot of discussion on the system requirements and the functional requirements, but if you know anything about user experience and design thinking, no one was talking user experience and design thinking 30 years ago. [chuckle]

Oh, that’s another one of the Cs on my list there, was that compassion. Again, the forced letter C there, meaning more like empathy, that is being able to understand that you’re dealing with users of this thing you’re building, and if it’s opaque and not understandable and uses complex terminology to explain it, you’re not being very empathetic with your end user. [chuckle]

  

Borne:

Yeah, I’m looking for my crystal ball right now, let’s see…[laughter]. I think as time goes on, we’re just seeing the data science is more being blended into organizations. There was a time where it was sort of like a side project or the team was off to the side, “Here’s our data science team.” But for one thing, I think there’s gonna be some data democratization that has to happen. [There are ] two aspects of the culture. One is a culture of experimentation, that is being able to test data for patterns that might give business insight for better actions and decisions, so culture of experimentation. And the other is a culture of, if you see something, say something. So where have we heard that before, right? [chuckle]

If you’ve ever been in the New York City subway, you see the signs everywhere, “If you see something, say something.” And the same thing with data. If you see something, it’s “Oh it was not my job. It’s someone else’s job”. No, we are… If we’re a digital organization, if we are undergoing digital transformation, then we all need to be empowered to work with and learn from and take actions from digital data.

Anyway, so I think data science, the future of is we’ll see less of it emphasized as data science and more in terms of its other dimensions, which the applications, like machine learning and AI. Well, AI being the application, machine learning being the technology for the actual implementations, which include cloud and other things. So we’ll start seeing more focus on those, but we’ll still be doing data science. We just may not use that word to describe the job title.

Borne:

So essentially becoming immersed in data first. At that point you sort of… If you don’t catch the bug there, you’re not gonna catch it at all, ’cause once you get immersed in data, you realize there’s power, there’s patterns and trends, and correlations in data. So once you get that experience, then I’d say first thing to look at is unsupervised learning, because unsupervised learning is basically just finding the patterns in the data without any regard to any preconceived notion of what you’re looking for. Now, supervised learning is specifically designing algorithms that can diagnose or predict an outcome based upon training data. So you could start there too. A lot of people do start there because they feel like, “Hey, I can predict the future.” [chuckle] So supervised learning, it gives you a rush because you’re actually predicting something pretty cool.

Borne:

Yeah, yeah, it’s not really easy to answer that because there’s so many thousands out there. There are websites that do a compilations of surveys of what data scientists recommend, and so… KD Nuggets. If people are not familiar with KD Nuggets, they should check that, chúng tôi has been around, Greg Shapiro started that you know in 1980s.

Borne:

Yeah. Absolutely. I’m actually a keynote speaker for a conference in Peru at the end of July. I’m giving two keynote talks, one on AI and one on data business analytics basically. So Peru is really ramping up. I know that in Africa, it’s an enormous activity going on right now, a lot of activity in Nigeria. So even a few years ago, I was invited to the South African Embassy in Washington DC, which is a very moving experience because it was the week that Nelson Mandela died. It was really an emotional experience, but they’re talking about the importance of data analytics to basically rise up the whole South African continent in terms of agriculture, economics, and business, and healthcare, and medicine, and so on, and so on. Just how the power of data to inform, to inspire, for innovation and insights… It was just really impressive, and so I don’t think anyone is gonna be immune from the benefits of this if you just go after it.

Top 10 Business Analytics & Data Science Awards To Chase After

The role of business analytics & data science awards comes in to boost stakeholder confidence

Decision-makers, these days, are starting to have a change of heart. It’s getting progressively clearer that classic approaches to teaching are no longer adequate, and a more Data-Driven solution is now necessary. Rather than engaging a brute force approach, a quick, effective dive into available data can reveal useful information that maximizes teaching effectiveness. In this respect,

The Fisher Center Summit & Berkeley World Business Analytics Awards

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The BIG Awards for Business

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IIBA Corporate Leadership Excellence in Business Analysis Awards Data Science and Engineering Analytics Award

Data Science Research Awards Every year, Adobe funds a university faculty research program to promote the understanding and use of data science in the area of marketing. Its goal is to encourage both the theoretical and empirical development of solutions to problems in marketing. Adobe will provide funding support of up to US$50,000 to a North American academic institution, college, or university for each selected research proposal. Awards will be in the form of an unrestricted gift to the academic institution under the names of the researchers who submitted the proposal.

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The TAG Data Science & Analytics Society aims to connect and inspire analytical minds to foster innovative uses of data. Every year we provide a platform for the best and brightest to show off their incredible accomplishments in this increasingly pivotal functional area. The purpose of the Innovation Awards is to highlight innovative and impactful solutions in Data Science, Analytics, Big Data, and related domains.

Data Science External Awards

MinneAnalytics offers scholarships to undergraduate students who display a passion for pursuing a career in analytics and a commitment to engaging with the community. Each year the Department of Statistics and Data Science applies for an award and nominates a student from the Department who displays a passion for pursuing a career directly related to data science and analytics and has an ongoing commitment to community engagement.

DataIQ Awards

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