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Data Science: Level-02

Data Science Track

Data Science:
Level-02

4.8 (1,230 reviews)

Introduction to Data Science Level-02. Master deep learning, NLP, and scalable ML systems. Build AI solutions for healthcare, finance, IoT, and more.

Created by Avanteia
12,580 Total Enrolled
15 September 2024 Last Updated
Enroll Now
Data Science Level-02 Course
3 Months Duration
Certificate On Completion
Level-02 Level
12 Modules Syllabus
3 Months Duration
English Language
Certificate Included
Introduction

Overview

Master deep learning, NLP, and scalable ML systems. Build AI solutions for healthcare, finance, IoT, and more.

Python Excel Pandas Analysis Visuals AI Link
Goal

Learning Outcome

Master advanced ML, deep learning, and automation. Optimize models for performance and solve complex business problems.

Curriculum

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
  • Basics of neural networks
  • Activation functions & backpropagation
  • TensorFlow & Keras introduction
Hands-on Lab
Build a simple ANN in Google Colab (TensorFlow/Keras) MNIST digit classification
  • Text preprocessing (tokenization, stopwords, stemming, lemmatization)
  • Bag of Words, TF-IDF
  • Sentiment analysis basics
Hands-on Lab
Perform text cleaning using NLTK Build a sentiment classifier using Scikit-learn
  • Introduction to Big Data (Hadoop, Spark)
  • Cloud tools (Google BigQuery, AWS free tier, Azure ML Studio)
  • Handling large datasets efficiently
Hands-on Lab
Run queries on Google BigQuery free tier Process data using PySpark on Colab
  • End-to-End Data Science Project
  • Model deployment using Flask + Streamlit + GitHub
  • Best practices in reporting & documentation
Hands-on Lab
Choose a dataset (finance, healthcare, retail, social media) Perform EDA → ML/DL → Visualization → Deployment Deploy on Streamlit Cloud / Heroku (free)
Results

What You Will Learn

Deep Learning & Neural Networks

Build and train neural networks using TensorFlow and Keras for complex pattern recognition tasks.

Natural Language Processing

Process and analyze text data, build sentiment classifiers, and extract insights from unstructured content.

Big Data & Cloud Integration

Handle large-scale datasets using Hadoop, Spark, and cloud platforms for scalable data science solutions.

End-to-End Deployment

Deploy complete data science projects using Flask, Streamlit, and cloud hosting for real-world impact.

Testimonials

What Our Students Say

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