About Course
The Data Science Course is a complete, practical training program designed to help beginners and professionals learn how to collect, analyze, and interpret data to solve real-world problems. Data Science is one of the most in-demand fields in technology, used by companies to make smarter decisions, build predictive systems, and improve business performance.
In this course, you will start with the fundamentals of data science, including data types, statistics, and data analysis techniques. You will then move into programming with Python, learning how to use powerful libraries like NumPy, Pandas, and Matplotlib for data manipulation and visualization.
As you progress, you will explore machine learning concepts, where you will learn how to build predictive models that can forecast outcomes, classify data, and detect patterns. The course also introduces you to data cleaning, feature engineering, model evaluation, and real-world dataset handling.
You will gain hands-on experience working on real projects such as sales prediction, customer segmentation, recommendation systems, and data dashboards. You will also learn how to communicate insights effectively using charts, reports, and storytelling techniques.
By the end of this course, you will be able to analyze complex datasets, build machine learning models, and apply data science techniques to real-world business problems. Whether you want to become a data scientist, data analyst, or machine learning engineer, this course gives you the practical skills and confidence needed to start your career.
Course Content
Data Science With Python
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Introduction to Data Science with Python
13:44 -
What is Data Science?
10:00 -
Applications of Data Science
00:11 -
Installing Python & Anaconda
02:13 -
Setting Up Jupyter Notebook
16:50 -
Introduction to Google Colab
18:23 -
Python Syntax Basics
07:04 -
Input and Output in Python
02:46 -
Python Operators
23:00 -
Conditional Statements
03:02 -
Loops in Python
16:58 -
Functions in Python
11:52 -
Lambda Functions
01:47 -
Lists in Python
10:19 -
Tuples in Python
13:28 -
Sets in Python
01:23 -
Dictionaries in Python
09:00 -
String Manipulation
00:17 -
File Handling
02:07 -
Exception Handling
15:00 -
Modules and Packages
17:38 -
Object-Oriented Programming Basics
03:07 -
Python Best Practices
16:13 -
Python Practice Project
18:11 -
Introduction to NumPy
06:24 -
Creating NumPy Arrays
00:32 -
Array Operations
11:00 -
Array Indexing and Slicing
10:00 -
Mathematical Functions in NumPy
04:34 -
Broadcasting in NumPy
18:16 -
Random Numbers in NumPy
07:06 -
Array Reshaping
02:31 -
Performance Optimization with NumPy
19:00 -
NumPy Practice Exercises
06:25 -
Introduction to Pandas
12:00 -
Pandas Series and DataFrames
18:00 -
Reading CSV and Excel Files
17:00 -
Data Selection and Filtering
08:15 -
Data Cleaning Techniques
01:36 -
Handling Missing Values
11:05 -
Data Transformation
06:00 -
Merging Data Frames
19:39 -
Group By Operations
01:12 -
Pivot Tables
14:14 -
Working with Date & Time Data
07:00 -
Exporting Data
14:00 -
Exploratory Data Analysis (EDA)
03:10 -
Pandas Practice Exercises
12:14 -
Data Analysis Project
09:00 -
Introduction to Data Visualization
05:00 -
Matplotlib Basics
02:21 -
Line Charts
10:09 -
Bar Charts
05:16 -
Pie Charts
12:36 -
Histograms
03:00 -
Scatter Plots
17:00 -
Box Plots
03:26 -
Seaborn Basics
17:00 -
Data Visualization Project
04:15 -
Introduction to Statistics
13:37 -
Mean, Median and Mode
13:21 -
Variance and Standard Deviation
00:24 -
Probability Basics
05:31 -
Probability Distributions
09:00 -
Correlation and Covariance
04:13 -
Hypothesis Testing
16:18 -
Linear Algebra Basics
04:49 -
Matrices and Vectors
23:49 -
Feature Scaling
05:26 -
Feature Engineering
08:17 -
Statistics Practice Project
23:09 -
Introduction to Machine Learning
00:21 -
Types of Machine Learning
06:42 -
Supervised Learning
05:26 -
Unsupervised Learning
01:29 -
Linear Regression
15:06 -
Logistic Regression
18:56 -
Decision Trees
14:10 -
Random Forest Algorithm
02:44 -
K-Nearest Neighbors (KNN)
10:00 -
Support Vector Machines (SVM)
23:24 -
Naive Bayes Algorithm
11:15 -
K-Means Clustering
16:21 -
Hierarchical Clustering
18:39 -
Model Training
08:37 -
Model Evaluation Metrics
02:07 -
Final Capstone Project: End-to-End Data Science with Python
13:11
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