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Data Science in Practice

Unlock the Power of Data: Real-World Data Science Case Studies

Real-Life Data Science Case Studies

Embark on a lunar expedition across the universe of data science, where you’ll encounter different celestial bodies of knowledge: Product Data Science, Causal AnalysisMachine Learning, Predictive Analytics, NLP, Artificial Intelligence, and Recommender Systems. Orbit through three real-life case studies, end-to-end Data Science projects, each serving as a unique moon in the galaxy of data science.

Case Studies

Real-Life Case Studies

Case Study in Product Data Science

What Makes Playlist Successfull

Case study that uses Exploratory Data Analysis (EDA) to identify and correlate features of successful music playlists with the success metrics. Then it uses Econometrics, Linear Regression for Causal Analysis to identify features that define the Playlists' success.

Case Study in Machine Learning

Predicting Salaries of Job Ads

Case Study that utilizes Machine Learning to estimate salaries based on job postings. It involves statistical analysis to identify key features and outliers in the data. Multiple Machine Learning models are trained and their performances are compared using Cross-Validation to select the best ML model.

Case Study in Recommender Systems

Building top-K Job Recommender System

Case Study that develops a Job Recommender System, a top K job recommender algorithm utilizing Natural Language Processing (NLP) and Machine Learning. It uses CountVectorizer to transform data, and KNN Algorithm for building a Collaborative Filtering algorithm that generates tailored job recommendations.

Expertly crafted

Practically executed in Python

Structured & four-part immersive learning experience​

Setting the Course

Chart your mission by setting the business goal, defining the problem, and planning the technical transformation.

Python Programming Expedition

Dive into the depths of Python programming with a step-by-step journey that ensures you stay on the right trajectory.

Data Science Analysis

Engage with the heart of data science as you analyze, interpret, and translate data insights into actionable strategies.

Path to Building Personal Portfolio

Each case study culminates in practical recommendations, enabling you to showcase your ability to apply data science insights in real-world contexts.

These case studies not only facilitate knowledge acquisition but also help build a solid personal portfolio. They provide concrete evidence of your ability to conduct a full cycle data science case study – an ability highly prized in the job market.

Set your learning trajectory with us. Enroll now, and launch your journey through the universe of data science. Your lunar learning experience begins here!

Case Study 1

Case Study in Product Data Science

What Makes a Playlist Successful

This case study dives into the world of music playlists to uncover what makes them successful. Leveraging the power of Descriptive Statistics, we identify key features that contribute to a playlist’s popularity. We define candidates for Success Metrics.

Through Exploratory Data Analysis (EDA) including Semantic Analysis (NLP), we delve deeper to find correlations between these features and the success of a playlist. We then employ Econometric technique for Causal Analysis, specifically Linear Regression, to pinpoint the defining characteristics of a successful playlist beyond correlations.

This comprehensive project is conducted in four stages, integrating Python programming at each step to provide a hands-on, real-world application of Product Data Science.

The Launchpad

The What & The Why!

Embark on a mission to unlock the secrets behind a successful playlist. We’ll establish the trajectory with a high-level goal, aligning it to both business goal and technical goal. We’ll plot our journey with a clear overview of the case study structure. This foundational stage prepares you for the thrilling voyage ahead into the expansive universe of data science.

Part 1

Discovery and Analysis

Set your sights on the uncharted galaxies of sample data. Guided by expert navigators, you’ll learn how to identify the right features for analysis and understand them through Descriptive Statistics. You’ll uncover the mysteries of data in ways you’ve never imagined. Get ready to experience the thrill of discovery!

Part 2

Success Metrics and Hypotheses

Accelerate your understanding of what constitutes success. Learn to define both short-term and long-term success metrics, an essential compass in your data science journey. Dive into feature engineering and master the art of formulating a hypothesis. This is where your mission starts to take shape!

Part 3

Exploratory Data Analysis (EDA)

Lift off to an adventure of exploration as you prepare, visualize and analyze data. You’ll test your hypothesis and engage in Exploratory Data Analysis (EDA), understanding the subtle difference between correlation and causation. This expedition will bring you one step closer to the core of data science. Additionally, Semantic Analysis (NLP) will be employed to discern the nuances of playlist titles

Part 4

Causal Analysis & Linear Regression

The final leg of our journey is the most exciting one! Understand why Econometrics, Causal Analysis and Linear regression are the keys to unlocking the data’s mysteries. 

Learn to interpret the Ordinary Least Squares (OLS) Python outputs components, dummy variable’s coefficients, and continuous variable coefficients as well as establish statistically significant impact. 

By the end, you’ll be ready to draw powerful conclusions and make insightful recommendations. You’ll emerge a pioneer in the data science realm, ready to take on your next big adventure!

Case Study 2

Case Study in Machine Learning

Predicting Salaries of Job Postings

Venture into the world of Machine Learning as we aim to predict salaries based on job postings. We begin by setting our goals and providing a Descriptive Statistics overview of our data. 

With a firm grasp on our data, we move into Exploratory Data Analysis (EDA), using Statistics for detecting outliers, and understanding probability distribution functions. 

Following this, we go through step-by-step guide on selecting, training, testing, evaluating and comparing Machine Learning models, and finally, we study the importance of features to our predictions and suggest next steps for further exploration.

The Launchpad

The What & The Why

Embark on a mission to unlock the secrets behind a Successful Playlist. We’ll establish the trajectory with a high-level goal, aligning it to both Business goal and Technical goal. We’ll plot our journey with a clear overview of the case study structure. This foundational stage prepares you for the thrilling voyage ahead into the expansive universe of data science.

Part 1

Descriptive Statistics and EDA

Steer towards the vast cosmos of data, exploring categorical and numerical features using Descriptive Statistics. Your exploration intensifies as you dive into Exploratory Data Analysis, examining Data Visualizations, probability distribution functions and detecting outliers with Boxplots. This deep dive into data exploration is sure to ignite your passion for data science.

Part 2

Machine Learning Model Selection

Now, it’s time to select your candidate Machine Learning models, the spaceship that will take you to your destination. With a universe of models to choose from, you’ll learn how to make the best choice to accomplish your mission. We will be using simple as well as more flexible models such Linear Regression, Bagging, Random Forest, Gradient Boosting Machine (GBM) and Extreme Gradient Boosting (XGBoost).

Part 3

Machine Learning Model Training

Prepare for takeoff as we navigate through the training of your chosen machine learning models. This step-by-step guide not only optimizes the learning potential of your model amidst the expansive universe of data but also illuminates the common journey of implementing and training Machine Learning models such as Linear Regression, Bagging, Random Forest, GBM and XGBoost. You’ll acquire knowledge on utilizing RMSE and K-fold Cross Validation for comparing multiple Machine Learning models and predicting the test error rate. The journey concludes with the unveiling of ‘feature importance‘, emphasizing the job features that significantly influence salary predictions.

Part 4

Results & Recommendations

As we near our destination, we will delve into the prediction model results, winning model and the importance of features in our predictions. This crucial understanding will illuminate the path to your final conclusions and next steps. This expedition is not just a learning experience, but a transformative journey that will turn you into a seasoned space explorer in the realm of data science.

Case Study 3

Case Study in Recommender System

Developing a Job Recommendations

Prepare for a quantum leap into the universe of Job Recommender Systems. We begin by defining our problem, setting goals, and preparing our data for the journey ahead. Text cleaning processes are carried out meticulously to ensure data quality. Using Counter Vectorization, we transform our text into a format suitable for machine learning. We dive deep into Recommender Systems, KNN algorithms, and how to apply them to our job recommender system. Lastly, we provide a look into our Python output, discuss suggestions for improvement, and share valuable resources for further learning.

Igniting the Engines

The What & The Why

Blast off on an interstellar mission to build a Job Recommender System. Your trajectory is defined by the high-level goal, the business goal, and the technical goal. With a detailed overview of the case study structure, you’ll have your mission protocol set and ready. Let’s turn ignition on and get this journey started!

Part 1

Unstructured Data Preparation with NLP

Steer your spaceship through the nebula of original data, explore its every nook and cranny before diving deeper into filtered data exploration. Prepare the data for the journey ahead and engage in a meticulous text cleaning process using our step-by-step NLP text preparation guide. This expedition is about leaving no stone unturned and setting a firm foundation for the journey ahead.

Part 2

Counter Vectorization

Get ready for a hyper-jump into the universe of NLP technique, Counter Vectorization. You’ll learn how to transform our text data into a format suitable for analysis. After a quick exploration of the CounterVectorizer, we’ll apply it to our data. Brace yourself for an exciting exploration into the realm of text analysis.

Part 3

Recommender Systems & AI

As we reach the heart of our journey, delve deep into the understanding of Recommender Systems and the usage of Machine Learning algorithm, KNN. Unearth the intricacies of different types of Recommender Systems, the utility of KNN, and how to use it for building our Collaborative Filtering, Job Recommender System. By the end of this part, you’ll be a seasoned data science explorer, ready to showcase your top job recommendations.

Part 4

results & Recommendations

As our journey nears its end, we’ll reflect on our progress and discuss ways to improve our Job Recommender System. You’ll also gain access to a curated list of resources to fuel your future endeavors in the universe of recommender systems. This concluding part of your expedition will ensure that you’re ready for the next step in your data science adventure.