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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Data Science Subjects and Syllabus [Latest Topics Included]

A
Analytics Vidhya Last Updated : 07 Nov, 2023
8 min read

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?

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

With the help of various methods and platforms, you can achieve effective data visualization. You will learn more about integrating R packages, Tableau, and Power BI to visualize data.

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. 

Do you know our AI/ML Blackbelt plus program covers all the essential Data Science subjects? Explore the course curriculum here to know more!

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 
  • Mathematics
  • 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.
  • Cloud Platforms:
    • Amazon Web Services (AWS), Google Cloud Platform (GCP), Microsoft Azure: Cloud services for scalable data storage and processing.
  • 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.
  • 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.

Also Read: 10 Data Science Projects Every Beginner should add to their Portfolio

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 BookAuthor
Python Data Science HandbookJake VanderPlas
Weapons of Math DestructionCath O’Neil
Practical Statistics for Data ScientistsPeter Bruce, Andrew Bruce & Peter Gedeck
Introducing Data ScienceDavy Cielen, Anro DB Meysman, Mohamed Ali
Introduction to ProbabilityWilliam Feller
The Art of Statistics Learning from DataDavid Spiegelhalter
Data Science from ScratchJoel Grus
Pattern Recognition and Machine LearningChristopher M. Bishop
R for Data ScienceHadley Wickham & Garrett Grolemund
Think StatsAllen B Downey
Introduction to Machine Learning with PythonAndreas C Muller & Sarah Guido
Data Science Job: How to Become a Data ScientistPrzemek Chojecki
Naked Statistics Charles Whelan
Hands-on Machine Learning with Scikit-Learn and TensorFlowAurelien 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.

Conclusion

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

Q1. What is the eligibility to start or pursue a career in Data Science?

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.

Q2. Does Data Science require coding?

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. 

Q3. Is Data Science difficult?

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.

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