Data Science Bootcamp Online | Become a Data Scientist [2025]

Want to get into data science or start a career in the data science field?

An online Data Science Bootcamp is a great way to start. With flexible learning and practical experience, you’ll gain the skills needed to become a successful data scientist. Whether you're a beginner or want to improve your Data Science skills, an online Data Science bootcamp can help you reach your goals from anywhere.

What is Data Science?

Data science involves collecting, analyzing, and interpreting large amounts of data to help organizations make informed decisions. It combines knowledge from various fields, including statistics, programming, and domain expertise, to extract meaningful insights from data. In simple terms, data scientists are like detectives, searching through data to uncover trends, patterns, and insights that can solve problems or drive business success.

Fact: Data science is one of the fastest-growing fields, and experts predict that by 2025, there will be over 11 million job openings in data-related roles. Now is the perfect time to invest in your future with a comprehensive boot camp that will set you up for success in this booming industry.

Best Online Data Science Bootcamps To Join in 2025

Here are some top-rated online data science bootcamps, offered by GeeksforGeeks, to consider in 2025:

1. Data Science Training Program

Learn and master data science with this Data Science Certification Training Program. Explore ML, Python, Advanced Analytics, NLP, LLM, GenAI its application in modern AI, and much more in this comprehensive live course

• Pricing: $599.98 $286
• Duration: 10 Weeks
• Level: Beginner to Advance
• Additional Certification : IBM

Why Choose This Course?

• Comprehensive Program: Covers all key aspects of Data Science.
• Doubt Resolving Sessions: Get live support from industry experts.
• Extensive Learning: 100+ hours of live classes and recorded lectures.
• Skill Assessments: Participate in 8+ contests.
• Practical Experience: Solve 10+ design problems and case studies.
• Knowledge Tests: Complete 15+ MCQ tests.
• Career Support: Resume building and industry-recognized certificate.
• Get additional Interview Questions to prepare you for interviews
• Supplementary Certification Questions materials provided for certifications such as Google, AWS, and IBM.

2. Complete Machine Learning & Data Science Program

The Complete Machine Learning & Data Science Program is a comprehensive live course designed to take you from beginner to expert in machine learning and data science.

• Pricing: $199.98 $78
• Duration: 26 Weeks
• Level: Beginner to Advance
• Additional Certification: IBM

Why Choose This Course?

• 20+ Programming Tools & Libraries
• 40+ Industry Projects
• Weekly Live Session with Industry Mentor
• Regular Live Doubt Solving Sessions with Industry Expert
• Career Essential Soft Skills Program
• Designed for both Students & Working Professionals
• Career Guidance Session
• 6 months of one-on-one 24X7 Doubt Assistance
• Supplementary Certification Questions materials provided for certifications such as Google, AWS, and IBM.

Data Scientist Roadmap

Starting a career in data science can feel overwhelming due to the wide range of skills involved. However, having a clear roadmap can make your journey much easier. Here’s a simplified path to guide you as you work toward becoming a data scientist:

Learn Basics

• Introduction to Data Science
• What is Data?
• Python for Data Science
• Python Pandas
• Python Numpy
• Python Scikit-learn
• Python Matplotlib

Data Analysis and Processing

• Understanding Data Processing
• Python: Operations on Numpy Arrays
• Overview of Data Cleaning
• Slicing, Indexing, Manipulating and Cleaning Pandas Dataframe
• Working with Missing Data in Pandas
• Pandas and CSV
  • Python | Read CSV
  • Export Pandas dataframe to a CSV file
• Pandas and JSON
  • Pandas | Parsing JSON Dataset
  • Exporting Pandas DataFrame to JSON File
• Working with excel files using Pandas
• Python Relational Database
  • Connect MySQL database using MySQL-Connector Python
  • Python: MySQL Create Table
  • Python MySQL – Insert into Table
  • Python MySQL – Select Query
  • Python MySQL – Update Query
  • Python MySQL – Delete Query
• Python NoSQL Database
• Python Datetime
• Data Wrangling in Python
• Pandas Groupby: Summarising, Aggregating, and Grouping data
• What is Unstructured Data?
• Label Encoding of datasets
• One Hot Encoding of datasets

Data Visualization

• Data Visualization using Matplotlib
• Style Plots using Matplotlib
• Line chart in Matplotlib
• Bar Plot in Matplotlib
• Box Plot in Python using Matplotlib
• Scatter Plot in Matplotlib
• Heatmap in Matplotlib
• Three-dimensional Plotting using Matplotlib
• Time Series Plot or Line plot with Pandas
• Python Geospatial Data
• Other Plotting Libraries in Python
  • Data Visualization with Python Seaborn
  • Using Plotly for Interactive Data Visualization in Python
  • Interactive Data Visualization with Bokeh

Statistics for Data Science

• Measures of Central Tendency
• Statistics with Python
• Measuring Variance
• Normal Distribution
• Binomial Distribution
• Poisson Discrete Distribution
• Bernoulli Distribution
• P-value
• Exploring Correlation in Python
• Create a correlation Matrix using Python
• Pearson’s Chi-Square Test

Machine Learning

Supervised learning

• Types of Learning – Supervised Learning
• Getting started with Classification
• Types of Regression Techniques
• Classification vs Regression
• Linear Regression
  • Introduction to Linear Regression
  • Implementing Linear Regression
  • Univariate Linear Regression
  • Multiple Linear Regression
  • Python | Linear Regression using sklearn
  • Linear Regression Using Tensorflow
  • Linear Regression using PyTorch
  • Pyspark | Linear regression using Apache MLlib
  • Boston Housing Kaggle Challenge with Linear Regression
• Polynomial Regression
  • Polynomial Regression ( From Scratch using Python )
  • Polynomial Regression
  • Polynomial Regression for Non-Linear Data
  • Polynomial Regression using Turicreate
• Logistic Regression
  • Understanding Logistic Regression
  • Implementing Logistic Regression
  • Logistic Regression using Tensorflow
  • Softmax Regression using TensorFlow
  • Softmax Regression Using Keras
• Naive Bayes
  • Naive Bayes Classifiers
  • Naive Bayes Scratch Implementation using Python
  • Complement Naive Bayes (CNB) Algorithm
  • Applying Multinomial Naive Bayes to NLP Problems
• Support Vector
  • Support Vector Machine Algorithm
  • Support Vector Machines(SVMs) in Python
  • SVM Hyperparameter Tuning using GridSearchCV
  • Creating linear kernel SVM in Python
  • Major Kernel Functions in Support Vector Machine (SVM)
  • Using SVM to perform classification on a non-linear dataset
• Decision Tree
  • Decision Tree
  • Implementing Decision tree
  • Decision Tree Regression using sklearn
• Random Forest
  • Random Forest Regression in Python
  • Random Forest Classifier using Scikit-learn
  • Hyperparameters of Random Forest Classifier
  • Voting Classifier using Sklearn
  • Bagging classifier
• K-nearest neighbor (KNN)
  • K Nearest Neighbors with Python | ML
  • Implementation of K-Nearest Neighbors from Scratch using Python
  • K-nearest neighbor algorithm in Python
  • Implementation of KNN classifier using Sklearn
  • Imputation using the KNNimputer()
  • Implementation of KNN using OpenCV

Unsupervised Learning

• Types of Learning – Unsupervised Learning
• Clustering in Machine Learning
• Different Types of Clustering Algorithm
• K means Clustering – Introduction
• Elbow Method for optimal value of k in KMeans
• K-means++ Algorithm
• Analysis of test data using K-Means Clustering in Python
• Mini Batch K-means clustering algorithm
• Mean-Shift Clustering
• DBSCAN – Density based clustering
• Implementing DBSCAN algorithm using Sklearn
• Fuzzy Clustering
• Spectral Clustering
• OPTICS Clustering
• OPTICS Clustering Implementing using Sklearn
• Hierarchical clustering (Agglomerative and Divisive clustering)
• Implementing Agglomerative Clustering using Sklearn
• Gaussian Mixture Model

Deep Learning

• Introduction to Deep Learning
• Introduction to Artificial Neutral Networks
• Implementing Artificial Neural Network training process in Python
• A single neuron neural network in Python
• Convolutional Neural Networks
• Recurrent Neural Networks
• GANs – Generative Adversarial Network

Natural Language Processing

• Introduction to Natural Language Processing
• Text Preprocessing in Python | Set – 1
• Text Preprocessing in Python | Set 2
• Removing stop words with NLTK in Python
• Tokenize text using NLTK in python
• How tokenizing text, sentence, words works
• Introduction to Stemming
• Stemming words with NLTK
• Lemmatization with NLTK
• Lemmatization with TextBlob

Possible Career Paths in Data Science Field

Wondering what job you can get after an online Data Science Bootcamp?

Note: Salary ranges can vary based on factors like location, experience level, industry, and company size. Generally, salaries are higher in tech hubs like Silicon Valley, New York, and major metropolitan areas.