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.

Level

All Levels
Last Updated

06/03/2025
Certificate

Certificate 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

MOCK INTERVIEW QNA

PROJECT SUBMISSION (OPTIONAL)

Add this certificate to your resume to demonstrate your skills & increase your chances of getting noticed.

Level

All Levels
Last Updated

06/03/2025
Certificate

Certificate of completion

Demo Lecture

Course Instructor

Course Central

Data Science and Analytics

Description

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

Demo Lecture

Tags

Course Instructor

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