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Data Science

  • Sehrish Khan(Trainer)
  • 300+ (Graduates)
  • Reviews

Learn how to collect, analyze, and visualize data to make smarter decisions. This course covers the fundamentals of data analysis, statistics, Python programming, and machine learning. Perfect for beginners and professionals who want to build a career in data-driven industries.

Rs. 30,000

  • Course Duration: 3 Months
  • Live Lectures
  • 24/7 Support
  • Lifetime Access
  • Certificate of Completion
  • 15 Days Refund Policy
Get Enroll Now
DUS AI – Details & Registration

Data Science Training

  • This course is designed to equip you with practical Data Science skills using tools like Python, Pandas, NumPy, and Matplotlib. You'll learn how to analyze large datasets, build data-driven strategies, and even offer services on freelancing platforms or create your own data-based products.
  • In today’s data-driven world, Data Science is one of the most in-demand and future-ready careers. DUS Skills provides expert-led, career-focused training that prepares learners to succeed in the modern job market with real-world projects and support.
Module 1: Introduction to Data Science

  • What is Data Science?

  • Role of a Data Scientist

  • Data Science Workflow

  • Data Types (Structured, Unstructured)

  • Difference: Data Science vs Data Analytics vs Machine Learning vs AI

  • Real-World Applications

2.1 Python Basics

  • Python Installation (Anaconda / Jupyter Notebook)

  • Variables & Data Types

  • Lists, Tuples, Dictionaries

  • Loops & Conditions

  • Functions

2.2 Python for Data Handling

  • Importing Datasets

  • Working with CSV, Excel, JSON

  • Basic File Handling

2.3 Libraries

  • NumPy (Arrays, Operations)

  • Pandas (Data Cleaning, Filtering, Grouping)

  • Matplotlib (Visual Plots)

  • Seaborn (Advanced Visualization)

 
 

  • Handling Missing Values (Techniques)

  • Data Formatting & Normalization

  • Outlier Detection

  • Encoding Categorical Variables

  • Scaling Techniques (MinMax, StandardScaler)

  • Basics of Feature Selection

  • Understanding Dataset Structure

  • Numerical vs Categorical Data Analysis

  • Correlation Analysis

  • Pair Plots & Heatmaps

  • Summary Statistics

  • Pattern Detection & Insights

  • Introduction to Databases

  • SELECT, WHERE, ORDER BY, GROUP BY

  • Joins (Inner, Left, Right, Full)

  • Subqueries

  • Aggregations

  • SQL Project: Business Data Analysis

 
 

  • Power BI / Tableau

    • Connecting Datasets

    • Data Modeling

    • Creating Dashboards

    • Charts, Graphs, KPI Cards

    • Filters & Slicers

    • Publishing Dashboards

  • Mean, Median, Mode

  • Standard Deviation & Variance

  • Probability Basics

  • Probability Distributions

  • Sampling Techniques

  • Hypothesis Testing (t-test, Chi-Square)

  • Correlation vs Causation

 
 

  • 8.1 Supervised Learning

    • Linear Regression

    • Logistic Regression

    • Decision Trees

    • Random Forest

    • Gradient Boosting

    • Naive Bayes

    • KNN

    8.2 Unsupervised Learning

    • K-Means Clustering

    • Hierarchical Clustering

    • PCA (Dimensionality Reduction)

    8.3 Model Training Concepts

    • Training vs Testing Data

    • Overfitting & Underfitting

    • Cross-Validation

    • Accuracy, Precision, Recall, F1-Score

    • Confusion Matrix

    • Introduction to Neural Networks

    • Perceptron Model

    • TensorFlow / Keras Basics

    • Image Classification Introduction (CNN)

    • Text Classification Introduction (RNN)

    • ChatGPT for data analysis
    • AI code generation
    • Automated EDA tools
    • What is Big Data?
    • Hadoop ecosystem basics
    • Spark overview
    • Cloud platforms (AWS/GCP/Azure) basics
    •  

    • Students complete 3–5 practical projects, such as:

      1. Sales Data Analysis Dashboard

      2. Customer Churn Prediction

      3. House Price Prediction (Regression)

      4. Market Segmentation (Clustering)

      5. Social Media Sentiment Analysis

      6. E-Commerce Data Insights Dashboard

       
       

      • Fiverr Gigs for Data Science

      • Upwork Profile Optimization

      • Portfolio Building

      • Resume & LinkedIn Optimization

      • Interview Preparation (MCQs + Coding Tasks)

       
       

This course uses industry-standard tools including Python, Jupyter Notebook, Anaconda, NumPy, Pandas, Matplotlib, and Scikit-learn to build and test machine learning models in real-world scenarios.

  • Free Access to AI Tools List & Resources
  • Downloadable Project Files & Code Templates
  • Freelancing Guide for Data Science & ML Services
  • Mini Course: Data Visualization with Python
  • Portfolio-Building Tips & Templates
  • Exclusive Certificate with DUS Skills Branding
  • 1-on-1 Mentorship Session (Limited Slots)
  • Access to Private Learners' Community
  • Free Future Course Updates

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FAQ's

Q1: Do I need prior programming experience to take this course?
A: Basic programming knowledge is helpful, but not required. The course starts with Python fundamentals and builds up to advanced machine learning concepts.
A: You’ll need Python installed (we recommend using Anaconda or Jupyter Notebook). All tools like NumPy, Pandas, and Scikit-learn will be introduced during the course.
A: Yes! Upon successful completion, you’ll receive a Certificate of Completion from DUS Skills.
A: Absolutely. It’s designed for beginners who want to enter the world of data science and machine learning using Python.
A: Yes. The course includes practical skills that you can use to offer services on freelancing platforms, work on AI projects, or build your own tools for monetization.