This Data Science training includes 25+ industry-relevant projects from various domains to help you master concepts of Data Science. A few of the projects that you will be working on are mentioned below:
In this Data Science course, You will go through dedicated mentor classes to create a high-quality industry project, solving an industry-relevant problem by leveraging the skills and technologies learned throughout the Data Science online course. The capstone project will cover all the key data extraction, cleaning, and visualization aspects to build and tune data models. You also can choose the domain/industry dataset you want to work on from the available options.
After successfully submitting the project, you will be awarded a capstone certificate for Data Science that can be showcased to potential employers as a testament to your learning.
Course End Projects:
Projects that mimic real-world business problems to help apply the concepts learned during a specific course. Typically these projects take 3 – 4 hours to complete.
Projects are as follows:
BUILDING A USER-BASED RECOMMENDATION MODEL FOR AMAZON
The dataset provided contains movie reviews given by Amazon customers. Perform data analysis on the Amazon customer movie reviews dataset and build a machine learning recommendation algorithm that provides the ratings for each user.
COMCAST TELECOM CUSTOMER COMPLAINTS
Comcast is an American global telecommunication company. The firm has been providing terrible customer service. They continue to fall short despite repeated promises to improve. Utilize the existing database of customer complaints as a repository to improve customer satisfaction.
MERCEDES-BENZ GREENER MANUFACTURING
Reduce the time a Mercedes-Benz spends on the test bench. Work with a data set representing different permutations of features in a Mercedes-Benz car to predict the time it takes to pass testing. Optimal algorithms will contribute to faster testing, lowering carbon dioxide emissions without reducing Mercedes-Benz’s standards.
RETAIL ANALYSIS WITH WALMART
One of the leading retail stores in the US, Walmart, would like to predict sales and demand accurately. The business is facing a challenge due to unforeseen demands and runs out of stock occasionally. It’s discovered that a machine learning algorithm is at the core of this issue. Build an ideal ML algorithm to accurately predict demand and incorporate factors like economic conditions, CPI, unemployment index, etc.
MOVIE LENS CASE STUDY
Perform analysis using the exploratory data analysis technique. You need to find features affecting the ratings of any particular movie and build a model to predict the movie ratings.
CUSTOMER SERVICE REQUESTS ANALYSIS
Domain: Customer Service
Perform data analysis on New York City 311 service request calls. You will focus on data wrangling techniques to understand data patterns and create visualizations to categorize and prioritize complaints like economic conditions, including CPI, Unemployment Index, etc.
COMPARATIVE STUDY OF COUNTRIES
Create a dashboard to compare various parameters of different countries using the sample insurance data set and the world development indicators data set.
SALES PERFORMANCE ANALYSIS
Build a dashboard that will present monthly sales performance by product segment and category to help clients identify the segments and categories that have met or exceeded their sales targets and those that have not met their sales targets.
PREDICT THE DEMAND FOR LOANS BASED ON THE REGION
This project provides learners with insights into the banking sector. Learners must build a statistical model to predict the demand for loans for a particular region. To show the results, learners must provide an online dashboard that shows the plan and its progress to all stakeholders.
BUILD A MODEL TO PREDICT DIABETIC PATIENTS
The project is aligned with NIDDK (National Institute of Diabetes and Digestive and Kidney Diseases) data sets representing one of the most chronic and consequential diseases. This project aims to build a model to predict patients with diabetes by utilizing the given data set.
CUSTOMER SEGMENTATION OF RETAIL CUSTOMERS
Perform customer segmentation using RFM analysis. The resulting segments can be ordered from most valuable (highest recency, frequency, and value) to least valuable (lowest recency, frequency, and value)