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DiplomaX Online Learning Institute

Data Science With Python Course

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.

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What Will You Learn?

  • Understand fundamentals of Data Science
  • Learn statistics and probability for data analysis
  • Master Python programming for data science
  • Work with NumPy and Pandas for data manipulation
  • Create data visualizations using Matplotlib and Seaborn
  • Clean and prepare raw datasets
  • Perform exploratory data analysis (EDA)
  • Learn machine learning basics
  • Build predictive models (regression & classification)
  • Understand clustering and unsupervised learning
  • Evaluate model accuracy and performance
  • Work with real-world datasets
  • Learn feature engineering techniques
  • Build data dashboards and reports
  • Understand data storytelling techniques
  • Use Jupyter Notebook for analysis
  • Learn basics of SQL for data handling
  • Apply AI/ML concepts in real projects
  • Build a professional data science portfolio

Course Content

Data Science With Python

  • 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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