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Jawalakhel, Lalitpur, Nepal

Telephone

+977 9856064310
+977 9851205215

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info@dhidigital.com

Open Hours

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Course- Data Science

Data Science Using Python

Master Data Analysis, Machine Learning & Visualization with Python

Course Duration: 60 Days
Course Pricing: RS 25000 (185$)
Course Model : Online/Offline

Become a Data Science Expert with Python

Are you ready to unlock the power of data and build a successful career in Data Science, Machine Learning, and AI? Our Data Science Using Python Course is designed for beginners and professionals who want to master data analysis, machine learning, and data visualization.

With hands-on training in Python, Pandas, NumPy, Matplotlib, Seaborn, Scikit-Learn, and TensorFlow, this course will teach you how to process data, build predictive models, and make data-driven decisions. Whether you aim to become a Data Scientist, Analyst, AI Engineer, or Business Intelligence Professional, this course is your gateway to a high-paying career in tech.

Who is This Course For?

  • Aspiring Data Scientists & Analysts
  • Software Developers & Engineers
  • Business Intelligence Professionals
  • AI & Machine Learning Enthusiasts
  • Students & Researchers Interested in Data Science

Why Take This Course?

  • High Demand for Data Scientists – Unlock career opportunities in AI, ML & analytics.
  • Python-Based Data Science – Learn industry-standard tools and libraries.
  • Hands-on Projects – Work on real datasets and case studies.
  • Machine Learning & AI – Build predictive models and automate decision-making.
  • No Prior Experience Required – Beginner-friendly with step-by-step guidance.
  • Industry-Relevant Skills – Prepare for jobs in data analytics, AI, and business intelligence.

What You Will Learn

  • Python for Data Science – Master essential programming skills.
  • Data Cleaning & Manipulation – Process raw data efficiently.
  • Data Visualization – Create insightful charts and graphs.
  • Exploratory Data Analysis (EDA) – Extract meaningful patterns from data.
  • Machine Learning Basics – Train models for predictions.
  • Deep Learning (Bonus) – Introduction to neural networks.
  • Big Data & Deployment – Handle large datasets and deploy models.

Course Outcome

  • Work on Real-World Data Science Projects
  • Master Python, Pandas, NumPy, & Machine Learning
  • Data Visualization & Storytelling with Seaborn & Matplotlib
  • Build & Train Machine Learning Models
  • Learn Deep Learning & AI Applications
  • Certification Upon Completion

Tools & Software Covered

  • Python & Jupyter Notebook – Programming & Data Analysis
  • Pandas & NumPy – Data Manipulation & Processing
  • Matplotlib & Seaborn – Data Visualization & Graphing
  • Scikit-Learn & TensorFlow – Machine Learning & AI
  • SQL & Web Scraping – Data Extraction & Big Data Handling

Start Your Data Science Journey Today!

Limited Seats Available! Enroll Now to Secure Your Spot.

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Lessons:

  1. Overview of Data Science & Career Paths
  2. Setting Up Python & Jupyter Notebook
  3. Introduction to Python for Data Science
  4. Working with Libraries – NumPy, Pandas, Matplotlib
  5. Understanding Structured vs. Unstructured Data
  1. Loading & Reading Datasets (CSV, Excel, JSON)
  2. Cleaning & Handling Missing Data
  3. Data Transformation & Feature Engineering
  4. Filtering, Sorting, & Aggregating Data
  5. Working with Large Datasets Efficiently
  1. Introduction to Data Visualization
  2. Creating Line, Bar, and Pie Charts
  3. Histograms, Scatter Plots & Box Plots
  4. Advanced Seaborn Visualizations
  5. Creating Interactive Dashboards
  1. Understanding Data Distributions
  2. Identifying Outliers & Anomalies
  3. Correlation Analysis & Feature Selection
  4. Using Pandas Profiling for Quick Analysis
  5. Preparing Data for Machine Learning
  1. Understanding Supervised & Unsupervised Learning
  2. Introduction to Scikit-Learn for ML
  3. Building a Simple Regression Model
  4. Classification Models – Logistic Regression, Decision Trees
  5. Evaluating Model Performance
  1. Hyperparameter Tuning & Model Optimization
  2. Feature Engineering & Dimensionality Reduction
  3. Ensemble Learning – Random Forest & Gradient Boosting
  4. Handling Imbalanced Datasets
  5. Model Deployment Basics
  1. Introduction to Neural Networks
  2. Building a Simple Neural Network with TensorFlow
  3. Convolutional Neural Networks (CNNs) for Image Processing
  4. Recurrent Neural Networks (RNNs) for Time Series Analysis
  5. Deploying Deep Learning Models
  1. Working with Large Datasets & Cloud Platforms
  2. Using SQL for Data Extraction & Analysis
  3. Web Scraping & API Integration
  4. Real-World Case Studies & Projects
  5. Career Guidance & Resume Building for Data Science

 

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