Premium Course
IT Courses
beginner
Updated: Jul 15, 2026
Data Science
This course provides a comprehensive introduction to data science, covering statistical foundations, Python programming, machine learning, and advanced techniques such as NLP and time series forecasting. Learners gain practical experience in building, evaluating, and communicating…
Duration
8h
Lectures
24
Language
English
Level
beginner
Course Curriculum
Lessons
6 items
Chapter 1: What Is Data Science? Role & Distinctions
Data Science is one of the fastest-growing and most influential fields in today's digital world. Organization…
Locked
Chapter 2: The Data Science Workflow
Chapter 2: The Data Science Workflow Data science projects are rarely completed by simply collecting data and…
Locked
Chapter 3: Statistics & Probability Foundations
Statistics and probability form the mathematical foundation of Data Science and Machine Learning. Almost ever…
Locked
Chapter 4: Programming Foundations for Data Science
Chapter 4: Programming Foundations for Data Science Programming is one of the three fundamental pillars of Da…
Locked
Chapter 5: Data Wrangling & Exploratory Data Analysis
Before any machine learning model can be built, the collected data must first be carefully examined, cleaned,…
Locked
Assignment
MODULE 1 ASSIGNMENT Using a sample dataset, perform exploratory data analysis identifying at least three dat…
Locked
Quizzes
0 items
No quizzes added to this topic yet.
Lessons
6 items
Chapter 1: Introduction to Machine Learning & Model Types
Machine Learning (ML) is one of the most important branches of Artificial Intelligence (AI) and forms the fou…
Locked
Chapter 2: Supervised Learning: Regression
Supervised Learning is one of the most widely used branches of Machine Learning because it enables computers…
Locked
Chapter 3: Supervised Learning: Classification
Supervised Learning is one of the most widely used branches of Machine Learning because it enables computers…
Locked
Chapter 4: Unsupervised Learning: Clustering & Dimensionality Reduction
Machine Learning techniques are generally divided into Supervised Learning and Unsupervised Learning. In supe…
Locked
Chapter 5: Model Evaluation & Validation
Building a Machine Learning model is only one part of a successful data science project. A model that perform…
Locked
Assignment
MODULE 2 ASSIGNMENT Using a sample labeled dataset, build and evaluate a simple regression or classification…
Locked
Quizzes
0 items
No quizzes added to this topic yet.
Lessons
6 items
Chapter 1: Feature Engineering & Feature Selection
Machine Learning models are often judged by the algorithms they use, such as Linear Regression, Decision Tree…
Locked
Chapter 2: Introduction to Deep Learning & Neural Networks
This chapter introduces learners to the fundamental concepts of Deep Learning and Neural Networks. The goal i…
Locked
Chapter 3: Natural Language Processing for Data Science
Natural Language Processing (NLP) is a branch of Artificial Intelligence and Data Science that focuses on ena…
Locked
Chapter 4: Time Series Analysis & Forecasting
Time Series Analysis and Forecasting is an important area of Data Science that focuses on analyzing data coll…
Locked
Chapter 5: Working with Big Data & Cloud Platforms
In this chapter, learners are introduced to the concepts of Big Data and Cloud Platforms, which are essential…
Locked
Assignment
MODULE 3 ASSIGNMENT Choose a dataset with a time or text component and describe which advanced technique from…
Locked
Quizzes
0 items
No quizzes added to this topic yet.
Lessons
6 items
Chapter 1: Model Deployment & MLOps Basics
Chapter 1: Model Deployment & MLOps Basics — Detailed Explanation This chapter introduces learners to the…
Locked
Chapter 2: Data Ethics, Bias & Responsible Data Science
This chapter introduces learners to the importance of ethical and responsible practices in data science. As m…
Locked
Chapter 3: Communicating Data Science Results to Stakeholders
This chapter focuses on one of the most important skills for a data scientist: communicating technical result…
Locked
Chapter 4: Data Science Careers & Skill Roadmap
This chapter introduces learners to the different career opportunities available in the field of Data Science…
Locked
Chapter 5: Capstone: End-to-End Data Science Project
This final chapter brings together all the concepts learned throughout the course by guiding learners through…
Locked
Assignment
MODULE 4 ASSIGNMENT Submit a one-page deployment and monitoring plan for a chosen model, including one fairne…
Locked
Quizzes
0 items
No quizzes added to this topic yet.
Lessons
0 items
No lessons added to this topic yet.
Quizzes
1 item
Final Assessment
Quiz included in this topic.
More Details
Course Overview
Full Course Description and Learning Context
This course provides a comprehensive introduction to data science, covering statistical foundations, Python programming, machine learning, and advanced techniques such as NLP and time series forecasting. Learners gain practical experience in building, evaluating, and communicating predictive models while understanding the complete data science workflow and responsible deployment practices.
Duration
8h
What You’ll Learn
Understand the complete data science workflow from problem definition to deployment.
Analyze and prepare data using Python and statistical techniques.
Build supervised and unsupervised machine learning models.
Apply feature engineering, NLP, and time series forecasting.
Evaluate model performance using validation and testing methods.
Target Audience
Aspiring data scientists.
Data analysts and BI professionals.
Software developers transitioning into data science.
Students preparing for data science careers.
Materials Included
Practical case studies.
Hands-on assignments.
Final assessment.
Requirements / Instructions
No prior data science experience is required.
Basic computer skills are recommended.
A laptop or desktop with internet access.
Reviews & Rating
Please log in to rate this course.
Start Building Your Future Today
Join the course and get immediate access to the learning materials.
Enroll Now