Course Contents

Learn about foundation in data science, using a structured methodology. including working on exploratory data analysis, data wrangling and preprocessing
The learners will be able to build Supervised and Unsupervised Learning models using Python
Participants are required to work on minimum one project

Statistics for Data Science
Python for Data Analysis and Data Preprocessing
Exploratory Data Analysis
Python Libraries
Matplotlib, Seaborn, Sklearn, Pandas, Numpy
Machine Learning Algorithms using Python
Supervised Learning
Regression/Prediction and Classification models
Logistics, KNN, Decision Trees, Random Forest Naïve Bayes
Unsupervised Learning
Clustering, Recommendation Engine
K- Means, SVM, PCA
Model Evaluation Techniques
Model Improvement Techniques
Model Deployment using Flask

Who Can Take This Course? Anyone and everyone who loves numbers and computer codes! From students to working professionals and data analysts and machine learning specialists, this course is open to all.

Basic computer skills
An understanding of high school mathematics or statistics (don’t worry, it’s not as scary as it sounds!)
A strong zeal to learn and upskill yourself. Inquisitiveness and passion are the key!

Instructor Profile

Dimple Sanghvi

Expert in Lean Six Sigma | Data Scientist | Predictive Analytics | Storytelling with Data |Certified as LSS Master Black Belt by Indian Statistical Institute