
Data Science and Analytics
Description
The Data Science course is designed to equip learners with the knowledge and skills required to analyze, interpret, and visualize complex data. It covers a broad range of topics including statistics, machine learning, data visualization, data wrangling, and programming in languages such as Python and R. Through hands-on projects and real-world case studies, students learn how to extract valuable insights from data and apply them to solve practical problems in various industries like finance, healthcare, marketing, and technology.
This course is suitable for both beginners and professionals looking to upgrade their data skills. It blends theoretical concepts with practical experience, helping students build a strong foundation in data science tools and techniques. By the end of the course, learners will be capable of building predictive models, creating compelling visualizations, and making data-driven decisions, preparing them for roles such as Data Analyst, Data Scientist, or Machine Learning Engineer.
-
LevelAll Levels
-
Last Updated06/03/2025
-
CertificateCertificate of completion
Demo Lecture
Course Curriculum
COURSE INSTRUCTION
-
Read Before You Start
LETS GET STARTED
-
Module 1 – Introduction to Data Science
-
Module 2 – Introduction to Data
-
Module 3 – Data Wrangling
-
3.1. Data Cleaning and preprocessing
-
3.2. Standardization and Normalization of data
-
3.3. Outlier Detection and Removal
-
Module 4 – Exploratory Data Analysis (EDA)
-
4.1 .Introduction to EDA
-
4.2 .Detailed Exploratory Data Analysis
-
Module 5 – Data Visualization
-
5.1 Data Visualization
-
5.2 Advanced Data Visualization
-
Module 6 – Statistical Analysis
-
6.1 Probability distributions
-
6.2 Hypotheses Testing
-
6.3 Correlation and Regression Analysis
-
Module 7- Machine Learning Fundamentals
-
7.1 Linear Regression
-
7.2 Logistic Regression
-
7.3 Decision Trees
-
7.4 Random Forest Classifier
-
7.5 SVM
-
7.6 KNN
-
7.7 Naive Bayes
-
Module 8 – Model Evaluation
-
Module 9 – Data Engineering
-
9.1 SQL Basics
-
9.2 Introduction to Power BI
-
9.3 Report Creation in Power BI
-
9.4 Introduction to Data Warehousing
-
Module 10 – Advanced Topics and Applications
-
10.1 K-Means Clustering
-
10.2 PCA and t-SNE
-
10.3 Sentiment Analysis
-
10.4 Neural Networks
PROJECT
-
Grade A Project – Spaceship-Titanic
-
Bonus Project – NLP using Naive-Bayes for Text Classification
MOCK INTERVIEW QNA
-
Data Science Mock Interview Questions and Answers
PROJECT SUBMISSION (OPTIONAL)
-
Project Submission
Certificate you will get
Add this certificate to your resume to demonstrate your skills & increase your chances of getting noticed.

-
LevelAll Levels
-
Last Updated06/03/2025
-
CertificateCertificate of completion