Data Science – Advanced Course

Join the 4-month Data Science program to build machine learning models using Python, Pandas, NumPy, and Scikit-Learn. The focus of this course is to train learners to work with real datasets to perform data analysis, feature engineering, and predictive modeling tasks widely applied in business intelligence, research, and technology-driven sectors.

Why Join Cronus Consultants to Learn Data Science?

Seasoned data scientists at Cronus Consultants guide the trainees through Machine Learning models, statistical analysis, and real-world data projects. They even learn to build strong professional portfolios and career-ready technical expertise.

Curriculum Overview

What is Data Science? The Data Science Lifecycle.
Data Science vs. Data Analytics vs. AI/ML.
Key Roles: Data Scientist, Data Engineer, ML Engineer.
Setting up the environment: Jupyter Notebook, Anaconda/Python installation.

Python Basics: Data Types, Variables, Control Statements (If-else, loops).
Python Data Structures: Lists, Tuples, Dictionaries, Sets.
Functions, Modules, and Libraries.
Hands-on: Solving basic programming problems.

NumPy: Arrays, Reshaping, Indexing, and Array Operations.
Pandas: Series and DataFrames.
Data Cleaning: Handling Missing Values, Removing Duplicates, Data Type Conversion.
Data Manipulation: Merging, Joining, Concatenating, and GroupBy.
Hands-on: Loading CSV files and cleaning raw datasets.

Descriptive Statistics: Mean, Median, Mode, Variance, Standard Deviation.
Data Visualization Libraries: Matplotlib (Line plots, Bar charts).
Advanced Visualization: Seaborn (Heatmaps, Pair plots, Box plots).
Hands-on: EDA on a sample dataset (e.g., Titanic or Housing data).

Probability Distributions: Normal Distribution, Bernoulli, Binomial.
Inferential Statistics: Hypothesis Testing, P-values, Z-test, T-test.
Correlation vs. Causation.

Introduction to Machine Learning: Supervised vs. Unsupervised Learning.
Regression Algorithms: Linear Regression, Logistic Regression.
Classification Algorithms: Decision Trees, Random Forest, KNN.
Model Evaluation Metrics: Accuracy, Precision, Recall, Confusion Matrix.
Hands-on: Building a prediction model (e.g., Customer Churn or Stock Price).

End-to-End Project Workflow: Defining the problem, EDA, Modeling.
Introduction to Model Deployment: Saving models (Pickle), basic deployment.
Project: A short Capstone Project demonstrating the full pipeline.

Design real-world data science workflows.
Deploy pipelines on secure cloud systems.
Handle large datasets with cloud storage.
Present projects with live cloud access.

Key Features

ML model build

Train regression and clustering models on datasets.

Feature engineering

Select and transform features for better model accuracy.

Libraries (DS tools)

Use Numpy, Pandas, and Scikit-learn for data science.

Projects

Use data science workflows to solve real-world problems.

Who can pursue this course?

BBA B.A. B.Sc (Computer Science) M.Sc (Computer Science) B.TECH M.TECH Commerce MBA

INTERMEDIATE

₹ 15,000 /one-time

Duration: 4 Months

Enroll Now Talk to Advisor

✔ Live mentorship
✔ Placement Assist
✔ Course Completion Certification

Certification & career support

Skill certification

Earn a certificate and validate the practical skills you have learned.

Professional edge

Add your earned certification to your resume and stand out to employers.

Career advancement

Show your certification that highlights your skills, achievements, and job-readiness.