Data Science (DS)
Discover a comprehensive roadmap to mastering data science. From the fundamentals of data science to statistics, data wrangling, machine learning, big data, data visualization, and realworld applications across industries.
Introduction
Data science is an interdisciplinary field that combines principles and techniques from statistics, mathematics, computer science, and domainspecific knowledge to extract insights and knowledge from structured and unstructured data.
The core components of data science include:

Data Acquisition and Preparation: Collecting, cleaning, and organizing data from various sources, dealing with missing values, outliers, and inconsistencies.

Data Exploration and Visualization: Analyzing data using statistical methods and visualizing patterns, trends, and relationships through graphs, charts, and other visual representations.

Data Modeling: Applying machine learning algorithms, statistical models, and other computational techniques to discover patterns, make predictions, and gain insights from data.

Model Evaluation and Deployment: Assessing the performance and accuracy of models, interpreting results, and deploying models into production systems or decisionmaking processes.

Communication and Storytelling: Presenting findings and insights from data analysis in a clear and compelling manner to stakeholders, decisionmakers, or audiences.
Data scientists work with large and complex datasets from diverse sources, such as databases, sensors, web logs, social media, and more. They use programming languages like Python, R, SQL, and tools like Hadoop, Spark, and Tableau to process, analyze, and visualize data.
The goal of data science is to extract actionable insights and knowledge from data that can inform decisionmaking, drive innovation, optimize processes, and solve realworld problems across various domains, including business, finance, healthcare, social sciences, and many others.
Data Science Learning Path
This roadmap covers the essential topics for learning data science, starting with an introduction to the field and Python programming. It then delves into statistics, data wrangling, machine learning, deep learning, big data, and data visualization. The roadmap also includes sections on practical applications, ethics, and emerging trends in data science.
 Introduction to Data Science
 Python Programming for Data Science
 Statistics and Probability
 Data Wrangling and Preprocessing
 Machine Learning
 Deep Learning
 Big Data and Distributed Computing
 Data Visualization and Communication
 Data Science Use Cases and Applications
 Future Trends and Emerging Technologies
 Resources and Further Learning
Introduction to Data Science
Learn what data science is, the role of a data scientist, applications of data science, and the data science lifecycle in this beginnerfriendly introduction.
 What is Data Science?
 The Role of a Data Scientist
 Applications of Data Science
 The Data Science Lifecycle
Python Programming for Data Science
Master Python programming for data science, including data structures, libraries like NumPy and Pandas, data manipulation, analysis, and visualization.
 Introduction to Python
 Python Data Structures
 Python Libraries (NumPy, Pandas, Matplotlib)
 Python for Data Manipulation and Analysis
 Python for Data Visualization
Statistics and Probability
Gain a solid foundation in statistics and probability  descriptive statistics, probability theory, distributions, hypothesis testing, and regression analysis.
 Descriptive Statistics
 Probability Theory
 Probability Distributions
 Hypothesis Testing
 Correlation and Regression Analysis
Data Wrangling and Preprocessing
Techniques for cleaning, transforming, engineering features, reducing dimensionality to prepare data for machine learning models.
 Data Cleaning and Handling Missing Values
 Data Transformation and Normalization
 Feature Engineering
 Dimensionality Reduction Techniques
Machine Learning
Explore supervised and unsupervised machine learning algorithms like regression, classification, clustering, dimensionality reduction, and model evaluation.
 Introduction to Machine Learning
 Supervised Learning
 Linear Regression
 Logistic Regression
 Decision Trees
 Support Vector Machines
 Ensemble Methods
 Unsupervised Learning
 Clustering Algorithms (KMeans, Hierarchical)
 Dimensionality Reduction (PCA, tSNE)
 Association Rule Mining
 Model Evaluation and Validation
Deep Learning
Dive into neural networks  feedforward, convolutional (CNNs), recurrent (RNNs)  and deep learning libraries like TensorFlow and Keras.
 Introduction to Neural Networks
 Feedforward Neural Networks
 Convolutional Neural Networks (CNNs)
 Recurrent Neural Networks (RNNs)
 Deep Learning Libraries (TensorFlow, Keras)
Big Data and Distributed Computing
Overview of big data, Hadoop ecosystem, NoSQL databases, data streaming for handling and processing large datasets.
 Introduction to Big Data
 Hadoop Ecosystem (HDFS, MapReduce, Spark)
 NoSQL Databases
 Data Streaming and Realtime Processing
Data Visualization and Communication
Best practices for visualizing data, advanced techniques, storytelling skills to effectively communicate insights from data.
 Principles of Data Visualization
 Advanced Data Visualization Techniques
 Data Storytelling and Presentation Skills
Data Science Use Cases and Applications
Explore data science use cases across industries like finance, healthcare, logistics, manufacturing, retail, and telecommunication.
 Data Science Use Cases in Asset Management
 Data Science Use Cases in Automotive
 Data Science Use Cases in Banking
 Data Science Use Cases in Ecommerce
 Data Science Use Cases in Energy
 Data Science Use Cases in Finance
 Data Science Use Cases in Healthcare
 Data Science Use Cases in Insurance
 Data Science Use Cases in IT Industry
 Data Science Use Cases in Logistics
 Data Science Use Cases in Manufacturing
 Data Science Use Cases in Marketing
 Data Science Use Cases in Oil and Gas
 Data Science Use Cases in Retail
 Data Science Use Cases in Sales
 Data Science Use Cases in Telecom
Future Trends and Emerging Technologies
Stay ahead with automated machine learning (AutoML), explainable AI, federated learning, quantum computing for data science.
 Automated Machine Learning (AutoML)
 Explainable AI (XAI)
 Federated Learning and PrivacyPreserving AI
 Quantum Computing and Data Science
Resources and Further Learning
Find valuable resources for learning data science  online courses, books, research papers, communities, conferences, tools, and other relevant artifacts.
 Online Courses and Tutorials
 Books and Research Papers
 Online Communities and Forums
 DS Conferences and Events
 DS Development Tools and Frameworks
 DS Ethics and Policy Resources
Conclusion
We hope you find our Data Science (DS) learning path useful.
Discover everything you need to know about building for the emerging web by following these structured learning paths at your own pace.