Data Science

Master Python, SQL, Machine Learning, Deep Learning, NLP, Time Series, Big Data & Cloud, MLOps, and build real-world data science projects.

Learning Mode

Online

Real-Time Projects

Data Analysis, ML & DL Use Cases

Expert Mentorship

Guidance from Industry Experts

Career Support

Placement Assistance & Certification

About the Course

The Data Science program by SkillDzire equips you with foundational and advanced skills in Python, SQL, Statistics, Machine Learning, Deep Learning, NLP, Time Series Forecasting, Big Data & Cloud, MLOps, and Responsible AI. You will gain hands-on experience with real-world datasets and industry-relevant projects to become a confident data professional.

Learn Python, SQL, Pandas, NumPy, Scikit-learn, TensorFlow, Keras, PySpark

Hands-on projects on E-Commerce, Netflix, Climate, Health, Fashion datasets

Time Series Forecasting, NLP, Computer Vision, MLOps deployment

Ethical AI, bias mitigation, explainability & responsible data practices

Curriculum

Foundations of Data Science & AI

  • What is Data Science? Scope and Impact
  • The AI-ML-DL-DS Relationship
  • Business Use Cases of AI/ML
  • CRISP-DM & OSEMN Frameworks

Programming Essentials for Data Science (Python + SQL)

  • Python Setup, IDEs: Jupyter, VS Code, Colab
  • Python Basics: Variables, Data Types, Loops, Functions, Error Handling
  • Working with Collections: Lists, Tuples, Sets, Dictionaries
  • File Handling, List Comprehensions
  • SQL Essentials: SELECT, WHERE, GROUP BY, JOINS, Subqueries
  • Using SQLite and PostgreSQL

Applied Math & Stats for Data Science

  • Descriptive Statistics & Data Distributions
  • Inferential Statistics & Hypothesis Testing
  • Probability Concepts, Bayes Theorem
  • Linear Algebra: Vectors, Matrices
  • Correlation, Covariance, Multicollinearity

Data Wrangling & Preprocessing

  • Data Import, Cleaning, Formatting using Pandas
  • Missing Data, Outliers, Duplicates
  • Feature Engineering: Scaling, Encoding, Binning
  • Pipelines and Custom Transformers with Scikit-learn

Exploratory Data Analysis (EDA) & Visualization

  • Univariate, Bivariate, Multivariate Analysis
  • Graphs with Matplotlib, Seaborn, Plotly
  • Pandas Profiling, Sweetviz, AutoViz
  • Insights Reporting with Dashboards

Machine Learning – Core Algorithms

  • Supervised ML: Linear/Logistic Regression, Decision Trees, KNN
  • Unsupervised ML: K-Means, Hierarchical, DBSCAN
  • Dimensionality Reduction: PCA, t-SNE
  • Evaluation Metrics: Accuracy, Precision, Recall, ROC-AUC
  • ML Pipelines with Scikit-learn

Advanced Machine Learning & Feature Tuning

  • Ensemble Models: Random Forest, XGBoost, LightGBM, CatBoost
  • Feature Selection: RFE, Chi-Square, Mutual Info
  • Cross-Validation Techniques
  • Hyperparameter Tuning: GridSearchCV, Optuna
  • AutoML Tools: H2O.ai, Google Vertex AI (Intro)

Deep Learning & Neural Networks

  • Basics of Neural Networks & Activation Functions
  • Introduction to TensorFlow and Keras
  • CNNs for Image Data, Data Augmentation
  • RNNs, LSTMs for Sequential Data
  • Model Optimization & Transfer Learning

Natural Language Processing

  • Text Cleaning, Tokenization, POS Tagging
  • Bag of Words, TF-IDF, Word Embeddings
  • Sentiment Analysis & Text Classification
  • Transformers: BERT, GPT (Overview)
  • Building Simple Chatbots

Time Series Forecasting

  • Time Series Decomposition
  • Lag Features & Rolling Statistics
  • ARIMA, SARIMA, Prophet Models
  • Forecasting & Evaluation Techniques

Big Data & Cloud Integration

  • Introduction to Hadoop Ecosystem
  • PySpark DataFrames & MLlib Basics
  • Cloud Tools: AWS S3, Lambda, SageMaker
  • BigQuery & GCP AI Platform
  • Databricks and Collaborative Notebooks

MLOps & Model Deployment

  • Pickle, Joblib, ONNX Serialization
  • Flask/FastAPI Model APIs
  • Docker, Kubernetes Concepts
  • MLflow for Experiment Tracking
  • CI/CD for ML using GitHub Actions

Responsible AI & Ethics

  • AI Fairness & Bias Mitigation Techniques
  • Explainability with SHAP & LIME
  • Data Privacy: Anonymization, GDPR
  • Model Interpretability Tools

Hands-On Real-Time Exposure Tasks

  • E-Commerce Sales Data Analysis & Customer Insights
  • Global Life Expectancy & Health Data Analysis
  • Netflix Movies & TV Shows Data Analysis
  • Climate Data & Daily Temperature Analysis
  • Heart Disease Prediction using Machine Learning
  • Sentiment Analysis on Social Media/Reviews
  • Car Price Prediction using Regression Models
  • Hotel Booking Demand & Cancellation Analysis
  • Fashion Product Image Classification (Deep Learning)
  • Web Scraping & Data Extraction for Real-Time Insights
Download Full Curriculum (PDF)

Hands-On Projects

E-Commerce Sales Data Analysis & Customer Insights

Analyze large-scale sales datasets to identify trends, top products, and revenue strategies using Python and visualization libraries.

Global Life Expectancy & Health Data Analysis

Perform statistical analysis on world population and health datasets to understand factors affecting life expectancy.

Netflix Movies & TV Shows Data Analysis

Explore Netflix datasets to uncover insights about genres, release trends, and predict trending content.

Student Testimonials

"This Data Science course helped me understand ML algorithms and apply them on real datasets. The hands-on projects were amazing."

- Priya Sharma

"I gained practical skills in Python, SQL, and data visualization. The mentorship and guidance were excellent."

- Arjun Reddy

"SkillDzire’s Data Science program gave me confidence to solve real-world business problems using AI and ML."

- Sneha Patel