Course Overview
This 14-week Advanced Data Science program covers the complete spectrum of data science — statistical modeling, machine learning, deep learning, and deployment. You will build a portfolio of 8+ real-world data science projects ready for your resume and GitHub.
What You Will Learn
- Statistical modeling — regression, hypothesis testing, Bayesian inference
- Advanced ML — ensemble methods, XGBoost, LightGBM, CatBoost
- Deep Learning with PyTorch and TensorFlow
- Natural Language Processing — BERT, sentiment analysis, text classification
- Time Series Analysis and forecasting
- Feature engineering and data preprocessing pipelines
- Model deployment — REST APIs, Docker, cloud (AWS SageMaker)
- Data Science project management and storytelling
Who Is This For?
- Data analysts moving into data science roles
- ML enthusiasts building a formal foundation
- Professionals targeting data scientist positions
Prerequisites
Python programming (intermediate) and basic ML knowledge.
Course Highlights
- ⏱ Duration: 14 Weeks
- 📜 Advanced Data Science portfolio
- 🗂 8+ portfolio projects
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What's Included
Course Curriculum
Setup your data science environment and master the core Python libraries used in every data science project.
- Python for data science: Jupyter, Anaconda setup
- Pandas: data loading, cleaning, transformation
- NumPy: numerical computing and array operations
- Matplotlib & Seaborn: data visualization
- Exploratory Data Analysis (EDA) workflow
Build a strong statistical foundation for making data-driven decisions and building robust models.
- Descriptive statistics: mean, median, variance
- Probability distributions: Normal, Binomial, Poisson
- Hypothesis testing: t-test, chi-squared, ANOVA
- Correlation and causation analysis
- Bayesian reasoning basics
Apply ML algorithms to real datasets and learn to choose the right model for any problem.
- Supervised learning: regression and classification
- Unsupervised learning: clustering and anomaly detection
- Feature engineering and selection techniques
- Pipeline building with Scikit-learn
- Cross-validation and model selection
Tackle advanced data science problems with time series, NLP, and recommendation systems.
- Time series analysis: ARIMA, SARIMA, Prophet
- Natural Language Processing with NLTK and spaCy
- Sentiment analysis and text classification
- Recommendation systems: collaborative filtering
Communicate insights effectively using Tableau, Power BI, and Plotly Dash.
- Tableau dashboard design principles
- Power BI for business reporting
- Interactive dashboards with Plotly Dash
- Storytelling with data
Build a complete end-to-end data science project from raw data to deployed API.
- Problem framing and dataset selection
- Full EDA and feature engineering pipeline
- Model training, evaluation and comparison
- FastAPI deployment and Streamlit dashboard
- Project presentation and portfolio documentation
Skills You Will Gain
Career & Salary Outcomes
Average salaries after completing this training
What Our Students Say
Real feedback from real professionals who completed this program
“The Advance Data Science Training for Professionals course at Astrikcoders was exactly what I needed to upskill. The hands-on projects were incredible.”
“I was able to transition into a new role thanks to this Advance Data Science Training for Professionals program. The instructors are very knowledgeable.”
“Highly recommend the Advance Data Science Training for Professionals training. It covers everything from basics to advanced topics with real-world examples.”
Frequently Asked Questions
Everything you need to know before enrolling