Data Science - Learn how to analyze and interpret data correctly.

The Master’s program in Data Science by College de Paris is a Minimum 12-month online professional program for students looking to start or advance a career in data science and offers strong preparation in statistical modeling, machine learning, optimization, management and analysis of massive data sets, and data acquisition. The program focuses on topics such as reproducible data analysis, collaborative problem solving, visualization and communication, and security and ethical issues that arise in data science.

Data Science and Artificial Intelligence have completely changed the world. Companies around the world are using artificial intelligence to eliminate repetitive tasks and improve the customer experience. Robots are taking the world by storm, continuously building an intelligence that rivals the human brain. Artificial intelligence and machine learning are the highest paying jobs in the world. Data science is an interdisciplinary field that uses methods and theories from mathematics, statistics, computer science, domain knowledge, and information science. It lies at the intersection of statistical methodology, computational science, and a wide range of application domains. According to recent estimates, more than 90% of his companies plan to use artificial intelligence in some way to develop or improve their products and services. These companies are looking for people who are proficient in data science and AI. Unfortunately, the industry faces a serious shortage of qualified employees to fill the void. Luckily, College de Paris decided to be part of the solution and started its Masters program in Data Science to help people use our services and earn their Data Science Certificate of Completion online.

Program Highlights

See which benefits you can derive from joining this program.
Online Program
  • Minimum 12-month online program
  • Industry Expert Mentor
  • Highly Experienced Faculties

Collège de Paris has designed agreements and conventions with academic institutions in France and abroad. This allows students to keep updated with the global learning pedagogy.

Dedicated Support Team for your Academic Journey
  • Industry Experts Live Sessions
  • Grievance Redressal System
  • Dedicated Tech & Academic Support on how to leverage the platform features.
Become Job-ready
  • Real-world case studies to build practical skills
  • Hands-on exposure to analytics tools & techniques such as Python, Tableau, SQL
  • Learn industry insights through multiple industry knowledge sessions

Program Curriculum

An overview of what you will learn from this program.
  • Learn to represent and store data using Python data types and variables, and use conditionals and loops to control the flow of your programs.
  • Learn SQL fundamentals such as JOINs, Aggregations, and Subqueries. Learn how to use SQL to answer complex business problems.
  • Learn how to program in R and how to use R for effective data analysis.
  • Learn the tools you need to clean and validate data, visualize distributions and relationships between variables, and to use regression models to predict and explain.
  • Learn to leverage the capabilities of deep learning tools to fix complex problems and unlock next-level results for modern business problems.
  • Learn to create custom data analysis solutions for different industries and predictions with real-world examples
  • Learn What machine learning is, how it is related to statistics and data analysis and uses computer algorithms to search for patterns in data.
  • Learn the tools you need to clean and validate data, visualize distributions and relationships between variables, and to use regression models to predict and explain.
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Industry Experts
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Industry Experts

Capstone Projects

Test your skills and mettle with a capstone project.

  • Finance & Accounts :- Techniques used: Conditional Inference Tree, Logistic Regression, CART and Random Forest
  • Entrepreneurship /Start Ups:- Techniques used: Univariate and Bivariate Analysis, Multinomial Logistic Regression, Random Forest
  • Insurance:- Techniques used: NLP (Natural Language Processing), Vector Space Model, Latent Semantic Analysis
  • Retail:- Techniques used: Market Basket Analysis, Brand Loyalty Analysis
  • Healthcare:- Techniques used: Logistic Regression, Random Tree, ADA Boost, Random Forest, KSVM
  • Supply Chain:- Techniques used: Text Mining, Kmeans Clustering, Regression Trees, XGBoost, Neural Network
  • Banking:- Techniques used: Linear Discriminant Analysis, Logistic Regression, Neural Network, Boosting, Random Forest, CART
  • Web & Social Media:- Techniques used: Topic Modeling using 9 Latent Dirichlet Allocation. K-Means & Hierarchical Clustering
  • E-commerce:- Techniques used: Text Mining, Kmeans Clustering, Regression Trees, XGBoost, Neural Network
  • Retail:- Techniques used: Market Basket Analysis, RFM (Recency-Frequency Monetary) Analysis, Time Series Forecasting

After Academic Requirements for your Data Science Masters’ degree

🔶 Obtain, clean/process, and transform data

🔶 Analyze and interpret data using an ethically responsible approach

🔶 Use appropriate models of analysis, assess the quality of input, derive insight from results, and investigate potential issues

🔶 Apply computing theory, languages, and algorithms, as well as mathematical and statistical models, and the principles of optimization to appropriately formulate and use data analyses

🔶 Formulate and use appropriate models of data analysis to solve hidden solutions to business-related challenges

🔶 Interpret data findings effectively to any audience, orally, visually, and in written formats


Find answers to all your queries and doubts here.

A : Data science and business analytics are unique disciplines, and the biggest difference is the scope of the problems covered. The science of data using algorithms, statistics, and technology is called data science. It provides actionable insights into a wide variety of structured and unstructured data that solve a broader perspective, such as customer behavior.

On the other hand, statistical analysis of mostly structured business data is called business analysis. We provide solutions to specific business problems and obstacles.

A : The Masters in Data Science course from College de Paris is a Minimum 12 month long online program.

A : Data is meaningless until it is transformed into valuable information. Data science mines large datasets of structured and unstructured data to identify hidden patterns and uncover actionable insights. The importance of data science lies in its myriad applications, from mundane activities like asking Siri or Alexa for recommendations to more complex applications like operating a self-driving car. The interdisciplinary fields of data science include computer science, statistics, inference, machine learning algorithms, predictive analytics, and emerging technologies.

A : Data science is in high demand across many industries, from IT, finance and e-commerce to manufacturing, healthcare and retail. The fastest growing job on LinkedIn, he is expected to create 11.5 million jobs by 2026. This makes data science a very lucrative career choice.Also, very few people actually have the skills needed to become a full data scientist. So while data scientists are in high demand, the supply of qualified talent is in short supply. As such, data scientists can demand as much salary as they want, and companies must meet that demand.

A: This data science master's program covers several topics related to data science. Some of them are: Regression, predictive modeling, clustering, time series forecasting, classification and more. I have an exercise where I need to structure a business problem using statistics and data modeling in an analytical framework. There are also topics on data cleansing, data transformation, deep learning, and natural language processing (NLP).


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Data Science

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