Data Science Training Syllabus & Modules
Complete dynamic pacing topics, hand-on tools, and project milestones.
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Detailed Syllabus
Below is the comprehensive, module-by-module curriculum. As this training is strictly 1-to-1, we can adjust the syllabus scope or spend more time on specific modules based on your learning speed.
Course Prerequisites
Basic understanding of school-level algebra and Excel formulas. No prior database or python coding background is required.
Full Curriculum Structure
Module 1: Python for Data Science & Advanced Wrangling
4 Weeks- High-Performance Vectorized Computations with NumPy
- Data Manipulation, Cleaning & Transformation with Pandas 2.2
- Complex SQL Analytics, Joins & Window Functions
- Automating Data Ingestion from APIs, JSON & Databases
Module 2: Exploratory Data Analysis & Statistical Inference
4 Weeks- Exploratory Data Analysis (EDA) with Seaborn & Plotly
- Descriptive & Inferential Statistics for Business Decisions
- Hypothesis Testing (A/B Testing, T-tests, ANOVA, Chi-Square)
- Probability Distributions and Sampling Techniques
Module 3: Predictive Machine Learning Modeling
4 Weeks- Supervised ML: Linear/Logistic Regression, Decision Trees
- Ensemble Learning with Random Forest, XGBoost 2 & LightGBM
- Unsupervised Learning: K-Means Clustering, PCA & Dimensionality Reduction
- Feature Importance & Model Explainability (SHAP / LIME)
Module 4: Business Intelligence & Live Data Science Internship
4 Weeks- Deploying ML Models as REST APIs with FastAPI & Streamlit
- Interactive Executive Dashboards with Plotly Dash
- Model Versioning and Lifecycle Management with MLflow
- Live Industry Predictive Analytics Project Internship
Tools & Technologies Mastered
You will gain hands-on operational capability in these tools during screenshare coding loops, creating real repositories.
Hands-on Lab Assignments & Projects
- Project 1: Sales Analysis interactive dashboard built in Power BI.
- Project 2: Structured SQL analytical reporting on database transactions.
- Project 3: Descriptive & predictive analysis on hospital datasets using Python.
- Project 4: A/B testing analytics pipeline committed to Git.
Syllabus FAQs
When does the next 1-to-1 training intake start?
Intakes start twice monthly on the 1st and 15th. The next upcoming 1-to-1 intake starts on October 1, 2026 (with secondary intake on October 15, 2026).
Is the Data Science & Analytics syllabus updated for modern industry standards?
Yes. Our syllabus is continuously updated to cover the latest versions of Python, Pandas, NumPy, Matplotlib, Seaborn, Scikit-Learn, SQL, Jupyter and modern production software engineering practices.
Can the syllabus be customized for my current skill level?
Because all sessions are strictly 1-to-1, your mentor can adjust module depth or accelerate topics based on your existing knowledge and target career goals.
Does the syllabus focus on theoretical concepts or hands-on coding?
Over 80% of training time is dedicated to live hands-on coding, terminal execution, pull request reviews, and building production applications.
Student Success Stories
Real feedback from students who completed our 1-to-1 virtual Data Science Training training.
"I wanted to switch my career to Data Science but found large batch classes very overwhelming. At CACTS, the mentor adapted the schedule to my learning pace. They walked me through complex statistics and data cleaning loops on a real project environment."
Priya S.
Junior Data Scientist, Kothrud, Pune"I needed to learn Python data analysis and SQL queries for business analytics. The 1-on-1 virtual coaching was extremely practical. We worked on cleaning messy datasets using Pandas and building regression models."
Ashish N.
Business Analyst, Shivane, Pune"The best part about CACTS is that you write code from day one. There are no boring PowerPoint presentations. Learning SQL joins, subqueries, and Python visualization libraries under a mentor's guidance was very effective."