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Data Science Track

Data Science:
Level-01

4.8 (1,230 reviews)

Introduction to Data Science Level-01. Dive into machine learning, model building, and statistics. Work on projects in prediction, classification, and clustering.

Created by Avanteia
12,580 Total Enrolled
15 September 2024 Last Updated
Enroll Now
Data Science Level-01 Course
2 Months Duration
Certificate On Completion
Level-01 Level
8 Modules Syllabus
2 Months Duration
English Language
Certificate Included

Overview

Dive into machine learning, model building, and statistics. Work on projects in prediction, classification, and clustering.

Python Excel Pandas Analysis Visuals Data Clean

Learning Outcome

Apply machine learning algorithms, data cleaning, and model evaluation. Work on real-time datasets and industry cases.

Syllabus

Click any module to expand and view topics and hands-on labs included.

  • What is Data Science?
  • Real-world applications & career paths
  • Python fundamentals (variables, data types, loops, functions)
  • Libraries overview: NumPy, Pandas, Matplotlib
Hands-on Lab
Setup Google Colab/Jupyter Notebook Write Python programs (calculator, loops, list handling) Use Pandas to load & explore CSV data
  • Data collection (CSV, Excel, APIs, web scraping basics)
  • Cleaning missing values, duplicates, outliers
  • Encoding categorical data (One-hot, Label encoding)
  • Normalization & Standardization
Hands-on Lab
Load a dataset from Kaggle Perform data cleaning using Pandas Handle missing values & outliers
  • Graphs: bar, line, scatter, histogram, boxplot, heatmaps
  • Correlation analysis
  • Feature importance overview
Hands-on Lab
Use Matplotlib & Seaborn for visualization Create a heatmap of correlations Visualize trends in real-world dataset (COVID, Sales, etc.)
  • Descriptive statistics (mean, median, variance, std dev)
  • Probability distributions (Normal, Binomial, Poisson)
  • Hypothesis testing (t-test, chi-square test, ANOVA)
Hands-on Lab
Simulate coin toss & dice using Python Perform hypothesis testing on dataset in Colab
  • SQL basics (SELECT, WHERE, JOIN, GROUP BY, HAVING)
  • Query optimization
  • Connecting SQL with Python
Hands-on Lab
Use SQLite / MySQL free version Write queries on sample dataset Integrate SQL with Pandas
  • ML pipeline (train-test split, model evaluation)
  • Regression: Linear, Multiple, Polynomial
  • Classification: Logistic Regression, k-NN, Decision Trees
Hands-on Lab
Predict house prices (Regression) – Kaggle dataset Build a classification model (Iris dataset)
  • Random Forest, Gradient Boosting, XGBoost
  • Feature engineering & selection
  • Cross-validation techniques
Hands-on Lab
Build a Random Forest model for Titanic survival prediction Use Scikit-learn feature selection methods
  • k-Means Clustering
  • Hierarchical clustering
  • PCA (Dimensionality Reduction)
Hands-on Lab
Customer segmentation (K-Means) Reduce dimensions of dataset using PCA

What You Will Learn

Python & SQL for Data Science

Master Python fundamentals, SQL querying, and integrate databases with data science workflows.

Machine Learning Models

Build and evaluate regression, classification, and ensemble models using Scikit-learn.

Feature Engineering & Selection

Apply advanced feature engineering, selection techniques, and cross-validation for robust models.

Unsupervised Learning

Implement clustering algorithms and dimensionality reduction for pattern discovery and segmentation.

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