Data Science Subjects and Syllabus [Latest Topics Included]
Imagine diving into the details of data analysis, predictive modeling, and ML. Envision yourself unraveling the insights and patterns for making informed decisions that shape the future. The concept of Data Science was first used at the start of the 21st century, making it a relatively new area of research and technology. Before you decide to make your career in this field, check out the subjects in data science. In this article, we will be covering different data science subjects and what they can teach you.
Table of contents
- What is Data Science?
- Top 10 Data Science Subjects
- Introduction to Data Science
- Mathematics and Statistics Fundamentals
- Programming and Software Engineering
- Data Wrangling and Preprocessing
- Machine Learning Algorithms
- Deep Learning and Neural Networks
- Data Visualization and Communication
- Big Data and Distributed Computing
- Advanced Topics in Data Science
- Capstone Projects and Hands-on Experience
- Data Science Subjects in Top Courses
- Data Science Tools
- Data Science Projects
- Best Books to Read for Data Science
- What Are the Important Areas in Data Science?
- Frequently Asked Questions
What is Data Science?
Data Science involves gathering, analyzing, and interpreting different data or information to derive conclusions. It uses specialized expertise to understand various structured and raw data to obtain the necessary insights. It refers to various scientific operations, including algebra, calculus, charts, graphs, computer algorithms, computer code, etc.
Top 10 Data Science Subjects
Explore the top 10 subjects in data science along with some data science course details:
Introduction to Data Science
The fundamental concepts of data science deal with multiple dataset kinds and accepted methods for data exploration.
Mathematics and Statistics Fundamentals
The basic concepts of statistical analysis and mathematics cover linear algebra, mathematical calculus, and probabilities. It deals with fundamental ideas in probability and statistics to help students learn how to use them in data analysis applications.
Programming and Software Engineering
Major programming languages for data science include Python and R. An explanation of their syntax, fundamental instructions, and how they help in data analysis.
Data Wrangling and Preprocessing
Different procedures are involved in preprocessing the data, whether text or numerical. Deep learning algorithms built using neural networks perform well on larger data sets. Data preprocessing also includes handling missing or null values, dealing with anomalies, and converting variables.
Machine Learning Algorithms
Data science is incomplete without machine learning since it uses various statistical methods to create predictions and solutions according to the issue statement. The other parts of data science come together in machine learning, which can make the model more complicated by simultaneously utilizing all the other parts.
Deep Learning and Neural Networks
Deep learning is a subsection of Machine Learning. Neural networks support data processing, identifying patterns, and determining the results. Biological neural networks inspire neural networks. Unstructured text, image, and audio data are the most common data types for deep learning.
Data Visualization and Communication
Big Data and Distributed Computing
Learn about the methods and technologies used by Hadoop, Spark, and NoSQL databases to handle, organize, and analyze enormous amounts of data in real-time. You will become familiar with solutions for streaming analytics, cloud computing structures, and additional big data technologies.
Advanced Topics in Data Science
Data science courses will additionally cover more advanced topics such as big data and database management, engaging visualizations, multivariate statistical models, and deep learning.
Capstone Projects and Hands-on Experience
In the capstone project course, you can develop usable/public data products, which they may use to demonstrate their abilities to future businesses. These projects collaborate with businesses, government, or academia focusing on a real-world issue.
Data Science Subjects in Top Courses
Here are some of the top data of data science colleges that you should know about:
IIT Data Science Program
IITs provide MTech and BTech data science and engineering degrees for students looking for careers in this industry in India.
The following are the required core courses for IIT Mandi’s BTech in Data Science and Engineering program:
- Data Management and Visualization
- Information Privacy and Security
- Statistical Foundations of Data Science
- Data Science Optimization for Statistical Foundations
- Data Science Mathematical Foundations
- Overview of Data Structures and Algorithms
- Matrix Computations for Data Science
- Computation for Data Science using Matrix Computations
- An Introduction to Statistical Learning
The following are the mandatory courses included in IIT Guwahati’s MTech Data Science curriculum:
- Foundations of Statistics for Data Science
- Data Models and Algorithms
- Dynamic Models
- Techniques for Machine Learning in Scientific Computing
- Computations with Matrices
- Machine Learning Laboratory
- Optimization Methods
- Python Programming
BSc Data Science Program
The three-year undergraduate BSc Data Science curriculum introduces students to the fundamental ideas behind data algorithmic methods, frameworks, Python coding, statistics fundamentals, machine learning, and more. The BSc Data Science curriculum is as follows:
- Statistical Inference and Probability
- Data Warehousing
- Multidimensional Modeling
- Discrete Mathematics
- Machine Learning
- Operational Research
- Optimization Strategies
- Object-Oriented Programming in Java
- Basics of Artificial Intelligence
- Operating Systems
- Machine Learning
- Cloud Computing
- Designing Programs and Data Structures in C
- Elementary Statistics
Btech Data Science
A 4-year undergraduate program in BTech Data Science introduces students to the fundamental concepts of data science, including corporate analytics, machine learning, data visualization, and computer algorithms. The BTech Data Science curriculum is listed below:
- Electrical and Electronic Engineering Principles
- Fundamentals of Machine Learning and Artificial Intelligence
- Design engineering with CAD
- Engineering Level Physics
- Engineering Level Chemistry
- Python Based Application Programming
- C-based Data Structures
- Application of Statistics
- Networks of Computers
- Software Engineering and Assessment Techniques
- Artificial Intelligence
- Data Mining
MSc Data Science
The postgraduate Master of Science (M.Sc) course runs for two years and is divided into four semesters. The following is a breakdown of the M.Sc. in Data Science program per semester:
- Analytical Statistics
- Spatial sciences
- Database Administration
- Technologies for Computational Mathematics
- Optimization Techniques
- Deep Learning
- Machine Learning
- Artificial intelligence
BlackBelt Program by Analytics Vidhya
The BlackBelt Program, designed by the experts at Analytics Vidhya, covers all the basic and advanced data science concepts. The program covers the following features and syllabus for data science:
- Natural Language Processing
- ML and AI for Business Analysis
- Basics of Deep Learning
- SQL for Data Science
- Microsoft Excel: Basics to Advanced
- Industry-level Hands-on Projects
- 100+ hours of mentorship sessions
- NLP using PyTorch
Data Science Tools
Data science involves a variety of tools that aid in data collection, analysis, visualization, and model building. Here is a list of essential data science tools:
- Programming Languages:
- Python: Widely used for data analysis, machine learning, and visualization with libraries like Pandas, NumPy, Scikit-learn, Matplotlib, and Seaborn.
- R: Popular for statistical analysis, data visualization, and building statistical models.
- Integrated Development Environments (IDEs):
- Jupyter Notebook: Interactive environment for coding, data exploration, and visualization.
- RStudio: IDE specifically designed for R programming.
- Data Collection and Cleaning Tools:
- Web Scraping Libraries (Beautiful Soup, Scrapy): For extracting data from websites.
- OpenRefine: Tool for cleaning and transforming messy data.
- Data Visualization Tools:
- Matplotlib: Library for creating static, interactive, and animated visualizations in Python.
- Seaborn: Built on Matplotlib, focused on statistical visualization.
- Tableau: User-friendly tool for creating interactive and shareable visualizations.
- Machine Learning Libraries:
- Scikit-learn: Machine learning library for classification, regression, clustering, and more.
- TensorFlow: Open-source deep learning framework developed by Google.
- PyTorch: Deep learning framework with dynamic computation graphs.
- Big Data and Distributed Computing:
- Hadoop: Framework for distributed storage and processing of large datasets.
- Apache Spark: Fast and general-purpose cluster computing system for big data.
- Databases and Data Storage:
- SQL (Structured Query Language): For managing and querying relational databases.
- NoSQL Databases (MongoDB, Cassandra): For handling unstructured and semi-structured data.
- Version Control:
- Git: System for tracking changes in code, collaborating with others, and managing projects.
- Cloud Platforms:
- Amazon Web Services (AWS), Google Cloud Platform (GCP), Microsoft Azure: Cloud services for scalable data storage and processing.
- Notebook Extensions and Add-ons:
- Jupyter Notebook Extensions: Enhance the functionality of Jupyter Notebooks.
- Kaggle Kernels: Platform for sharing and running data science projects.
- Text Analytics Tools:
- NLTK (Natural Language Toolkit): Python library for working with human language data.
- spaCy: Library for natural language processing tasks.
- Collaboration and Communication Tools:
- Slack, Microsoft Teams: Communication and collaboration platforms for team projects.
- GitHub: Platform for hosting and collaborating on code repositories.
Data Science Projects
Here are some beginner-friendly data science project ideas to help you get started:
- Exploratory Data Analysis (EDA): Analyze a dataset to gain insights and visualize trends using Python libraries like Pandas, Matplotlib, and Seaborn.
- Predictive Modeling: Build a simple linear regression model to predict a numerical outcome based on features from a dataset.
- Classification Problem: Use a dataset to classify objects into different categories using algorithms like logistic regression or decision trees.
- Sentiment Analysis: Analyze text data to determine the sentiment (positive, negative, neutral) using Natural Language Processing (NLP) tools like NLTK or spaCy.
- Titanic Survival Prediction: Predict whether a passenger on the Titanic survived or not using the classic Titanic dataset.
- Iris Flower Classification: Classify iris flowers into different species based on features like petal length and width using machine learning algorithms.
- Movie Recommender System: Create a basic movie recommender system using collaborative filtering techniques.
- Exploring COVID-19 Data: Analyze COVID-19 data to understand the spread, growth, and impact of the pandemic using real-time datasets.
- Housing Price Prediction: Predict housing prices based on features like location, square footage, and number of bedrooms using regression techniques.
- Customer Segmentation: Cluster customers into different segments based on their purchasing behavior using clustering algorithms like K-Means.
- Time Series Analysis: Analyze and forecast stock prices or weather data using time series analysis techniques.
- Image Classification: Build a simple image classification model to identify common objects using deep learning frameworks like TensorFlow or PyTorch.
- Anomaly Detection: Identify anomalies or outliers in a dataset using statistical methods or machine learning algorithms.
- Social Media Sentiment Analysis: Analyze sentiment on social media platforms for a specific topic using APIs and NLP techniques.
- Customer Churn Prediction: Predict whether customers are likely to churn (leave) a service or product based on historical data.
Best Books to Read for Data Science
Here is a list of some of the best books that an individual can read for learning about data science:
|Name of the Book||Author|
|Python Data Science Handbook||Jake VanderPlas|
|Weapons of Math Destruction||Cath O’Neil|
|Practical Statistics for Data Scientists||Peter Bruce, Andrew Bruce & Peter Gedeck|
|Introducing Data Science||Davy Cielen, Anro DB Meysman, Mohamed Ali|
|Introduction to Probability||William Feller|
|The Art of Statistics Learning from Data||David Spiegelhalter|
|Data Science from Scratch||Joel Grus|
|Pattern Recognition and Machine Learning||Christopher M. Bishop|
|R for Data Science||Hadley Wickham & Garrett Grolemund|
|Think Stats||Allen B Downey|
|Introduction to Machine Learning with Python||Andreas C Muller & Sarah Guido|
|Data Science Job: How to Become a Data Scientist||Przemek Chojecki|
|Naked Statistics||Charles Whelan|
|Hands-on Machine Learning with Scikit-Learn and TensorFlow||Aurelien Geron|
Also Read: Top 30 Data Science Books [2023 Edition]
What Are the Important Areas in Data Science?
There are many opportunities in the vast field of data science for those interested in learning more about it. Additionally, you must comprehend certain additional concepts if you want to work as a data professional, such as the following:
- Data warehousing and data engineering: Data engineering converts data into a usable format for analysis. This usually involves controlling the data’s origin, framework, value, maintenance, and accessibility to ensure other scientists can discover and evaluate it.
- Data mining and statistical analysis: Data mining is using statistics to identify developments and patterns in data from existing sources of information through statistical data analysis and predictive algorithms.
- Database architecture and management: This aspect is at the forefront of developing, installing, and managing databases that enable large volume, complicated data activities for particular services or sets of services.
- Data visualization: The graphical representation of data is data visualization. It makes it possible to employ visualization tools, including charts, tables, graphs, images, maps, and tables. These tools make analyzing trends, competitors and variations, growth, data patterns, and instances much easier.
- Operational data analytics: Operational-related data analytics makes immediate use of tools and data given by different staff and stakeholders of the company. Businesses may simplify their processes and improve the real-time functionality of their positions with this process.
- Marketing data analytics: Marketing data analytics incorporates tools and tactics such as sponsored search marketing, marketing software solutions, search engine optimization, and more for analysis. Data from marketing and sales activities, customer feedback, e-commerce and logistics operation tracking, new business opportunity discovery, and consumer data are the data sources.
These were the top data science subjects and books you must read to ace your career. If you want to know more about recent developments in Data, ML, and AI, follow our blogs and find the best quality content. We also offer a range of data science courses to help learners gain the latest skills and master the best data practices. Explore our courses now!
Frequently Asked Questions
A. A bachelor’s or master’s degree in mathematics, computer science, or engineering is necessary, along with proficiency in statistics and algorithms, if one wants to pursue or begin a career in data science. A background in a relevant discipline and knowledge of the fundamental ideas covered by the field is essential.
A. A prospective student should be familiar with computer programming languages like C++, Java, and Python because subjects in data science rely greatly on coding. You can locate, study, and effectively organize unstructured data with an understanding of coding and computer languages.
A. Understanding data science will be relatively easy, and it involves thoroughly comprehending data methods and principles. There are several resources available that can make it simpler to learn these skills.