About Course

Data Science Course Overview:

Dive into the world of Data Science and unlock the power of data! In this course, you will learn the essentials of Data Science, including:

Data Analysis: Understand and interpret data trends, patterns, and insights.
Data Processing: Learn how to clean, prepare, and transform raw data for analysis.
Python Programming: Master popular tools and libraries for data manipulation and visualization.
Machine Learning: Build intelligent models that predict outcomes and make decisions.
Deep Learning and NLP: Explore advanced concepts for handling images, text, and unstructured data.

By the end of the course, you will have the skills to solve real-world problems using data-driven techniques, preparing them for exciting opportunities in technology and business.

By the end of the Data Science Course, students will learn:
Analyze Data Effectively: Use statistical techniques and tools to derive meaningful insights from raw data.
Master Data Visualization: Create impactful graphs and dashboards to communicate findings clearly.
Build Predictive Models: Apply machine learning algorithms to forecast outcomes and make data-driven decisions.
Understand Deep Learning Basics: Work with neural networks for advanced tasks like image recognition and natural language processing (NLP).
Program with Confidence: Develop proficiency in Python for data manipulation, analysis, and automation.
Solve Real-World Problems: Handle end-to-end data projects, including data cleaning, feature engineering, and model deployment.
Use Industry Tools: Work with libraries like Pandas, NumPy, Matplotlib, Scikit-learn, TensorFlow, and more.
Students will leave the course equipped to take on roles like Data Analyst, Data Scientist, or Machine Learning Engineer, ready to make an impact in the data-driven world.

Course Content

Python for Data Science
This course is designed to provide students with a strong foundation in Python programming and introduce them to the exciting fields of Artificial Intelligence (AI), Machine Learning (ML), Data Science, and Deep Learning (DL). By learning these in-demand technologies, students will develop problem-solving skills, logical thinking, and the ability to work with real-world data. This knowledge will not only enhance their academic growth but also open up a wide range of career opportunities in the tech industry and beyond.

Data Science

01:09
Introduction to Colab Notebook

03:06
Python Variables

12:48
Python Data Types

15:02
Python Functions

15:01
User-Defined functions

12:29
Python Conditional Statement

15:00
Python while loops

10:49
Python For loop

08:16
Python Basic quiz
Capstone Project on Rock Paper Siccors

Data Structures in Python
Data structures are a very important part of the entire course. In this lesson, students will learn about the different types of data structures available in Python and how to use them to store and organize data effectively.

List in Python

15:01
List Comprehension

24:33
Pandas Series

16:33
Numpy in Python

15:01
Numpy 2

28:05
Pandas DataFrame

15:01
Tuples in Python

15:01
Python Dictionaries

14:46
Data Structures quiz
Capstone Project on Data Structures

Data Processing and Analysis
Data Processing and Analysis is a crucial step in the Data Science workflow. In this module, students will learn how to clean, transform, and organize raw data to make it suitable for analysis. They will also explore techniques for analyzing data to extract meaningful insights and support data-driven decision making.

Data Visualization using Matplotlib, Seaborn
Data Visualization using Matplotlib and Seaborn is a key component of Data Science. In this module, students will learn how to represent data visually to better understand patterns, trends, and relationships. They will explore various types of plots such as line charts, bar graphs, histograms, scatter plots, and heatmaps using Matplotlib and Seaborn, and learn how to customize these visualizations for clear and effective communication.

Meteorite Dataset Visualization

07:52
Box plot quiz
Meteorite Data Slicing

12:28
Meteorite Cartograms

15:01
Cartogram Quiz
Meteorite Data Count Plots

13:32
Count Plot Quiz
Meteorite Data Visualization using Histogram

15:01
Histograms 2

07:05
Histogram Quiz
Meteorite Visualization using Annotate Bars

14:40
Annotate Bars part 2

10:33
Capstone Project on Data Visualization

Artificial Intelligence
Artificial Intelligence (AI) is the development of computer systems capable of performing tasks that typically require human intelligence, such as learning, reasoning, problem-solving, and decision-making. In this lesson, students will be introduced to the fundamentals of AI, exploring its real-world applications and understanding its growing impact on various aspects of daily life. By examining examples like virtual assistants and recommendation systems, students will gain insight into how AI is shaping the future and the importance of responsible and ethical use of this technology.

Introduction to AI

03:40
Difference between AI, ML, DL and Data Science

04:09
Quiz on Overall AI
Machine Learning Model

10:12
Machine Learning Quiz
Car data exploration

14:59
Car Data exploration 2

08:45
Quiz on Data Exploration
Car Price Prediction – Feature Encoding

14:55
Car dataset Training to Linear Regression

06:10
Linear Regression
Interpreting p-value

41:15
P-Value Quiz
Car Price Prediction – RFE

14:54
RFE 2

11:57
Quiz on RFE
Model Evaluation

00:00
Model Evaluation 2

15:24
Model evaluation quiz
Capstone Project on House Price Prediction

04:08

Student Ratings & Reviews

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About Course

Data Science Course Overview:

What Will You Learn?

Course Content

Data Science

Introduction to Colab Notebook

Python Variables

Python Data Types

Python Functions

User-Defined functions

Python Conditional Statement

Python while loops

Python For loop

Python Basic quiz

Capstone Project on Rock Paper Siccors

Data Structures in Python Data structures are a very important part of the entire course. In this lesson, students will learn about the different types of data structures available in Python and how to use them to store and organize data effectively.

List in Python

List Comprehension

Pandas Series

Numpy in Python

Numpy 2

Pandas DataFrame

Tuples in Python

Python Dictionaries

Data Structures quiz

Capstone Project on Data Structures

Meteorite Data Analysis

Data quiz

Meteorite Dataset Visualization

Box plot quiz

Meteorite Data Slicing

Meteorite Cartograms

Cartogram Quiz

Meteorite Data Count Plots

Count Plot Quiz

Meteorite Data Visualization using Histogram

Histograms 2

Histogram Quiz

Meteorite Visualization using Annotate Bars

Annotate Bars part 2

Capstone Project on Data Visualization

Introduction to AI

Difference between AI, ML, DL and Data Science

Quiz on Overall AI

Machine Learning Model

Machine Learning Quiz

Car data exploration

Car Data exploration 2

Quiz on Data Exploration

Car Price Prediction – Feature Encoding

Car dataset Training to Linear Regression

Linear Regression

Interpreting p-value

P-Value Quiz

Car Price Prediction – RFE

RFE 2

Quiz on RFE

Model Evaluation

Model Evaluation 2

Model evaluation quiz

Capstone Project on House Price Prediction

Student Ratings & Reviews

Data Structures in Python
Data structures are a very important part of the entire course. In this lesson, students will learn about the different types of data structures available in Python and how to use them to store and organize data effectively.