Data Science – Basic Course | Cronus Consultants

Data Science – Basic Course

Get acquainted with Python programming, data reprocessing, and statistical concepts used in data science. Learn in depth how datasets are processed and analyzed to generate insights for modern businesses and technology applications.

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.

Build an end-to-end data science project.
Deploy ML models on cloud platforms.
Manage datasets using cloud storage tools.
Showcase portfolio with live deployments.

Key Features

Python basics

Use Python syntax and libraries for data handling tasks.

Data wrangling

Use Pandas efficiently to clean and transform datasets.

Graphic building

Use Matplotlib to create plots and understand data.

Basic ML model introduction

Apply basic machine learning algorithms to datasets.

Who can pursue this course?

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

BASIC

₹ 4,000 /one-time

Duration: 2 Months

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✔ 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

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Career advancement

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