Data Science

Data Science Course Syllabus and Subjects Details for 2023


Gurneet Kaur

Data Science Consultant at almaBetter

people15 mins


Published on22 Aug, 2023

Discover the comprehensive Data Science course syllabus 2023, providing a roadmap to mastering the essential Data Science subjects. Delve into mathematical foundations, programming proficiency, advanced algorithms, and more. Suppose you've ever wondered what is Data Science course syllabus. In that case, this detailed curriculum offers a clear insight into the diverse areas you'll explore on your journey to becoming a proficient data scientist.

This syllabus equips you with the skills to analyze and derive insights from complex datasets. Explore the intersection of mathematics, statistics, coding, and Machine Learning, empowering you to transform raw data into valuable business insights.

Prepare to excel in data-driven industries by immersing yourself in this well-structured Data Science course syllabus. This well-crafted Data Science course syllabus sets the stage for a successful journey into data-driven decision-making.

What is a Data Science Course?

A Data Science Course is a transformative journey through a curated foundation of the Data Science syllabus. This educational endeavor introduces learners to fundamental Data Science subjects, encompassing mathematical principles, programming techniques, and advanced algorithms. Tailored for beginners and those pursuing in-depth knowledge, this course covers a range of specializations.

From full stack Data Science course syllabus to the introduction to Data Science course syllabus, it caters to diverse academic backgrounds like Data Science syllabus, MSc Data Science syllabus, BCA Data Science syllabus, and B.Tech artificial intelligence and Data Science subjects list. Dive into this educational voyage to grasp the foundations of Data Science and its intricate applications.

Best Data Science Course Syllabus

Discover the ultimate Data Science course syllabus designed to cultivate comprehensive knowledge in data analytics. This curriculum delves into various Data Science subjects, catering to diverse levels of expertise, including beginners. Explore foundational aspects such as:

  • Introduction to Data Science: Embrace an overview of the field, applications, and ethical considerations.
  • Mathematical & Statistical Skills: Build a strong base in mathematics and statistics for data analysis.
  • Machine Learning: Grasp algorithms and techniques for predictive insights.
  • Coding: Master programming languages like Python for data manipulation.
  • Algorithms used in Machine Learning: Dive deep into regression, decision trees, and more.
  • Statistical Foundations for Data Science: Understand probability, hypothesis testing, and distributions.
  • Data Structures & Algorithms: Learn efficient data handling and processing.
  • Scientific Computing: Apply programming to scientific applications.
  • Optimization Techniques: Enhance algorithm efficiency.
  • Data Visualization: Convey insights through visuals.
  • Matrix Computations: Explore matrix operations.
  • Scholastic Models: Engage with advanced Machine Learning models.
  • Experimentation, Evaluation, and Project Deployment Tools: Assess and deploy models effectively.
  • Predictive Analytics and Segmentation using Clustering: Apply predictive modeling and clustering.
  • Applied Mathematics and Informatics: Apply advanced math in Data Science.
  • Exploratory Data Analysis: Master initial data exploration techniques.
  • Business Acumen & Artificial Intelligence: Bridge Data Science and business objectives.

Full Stack Data Science Course Syllabus by Almabetter

Embark on a comprehensive journey through the meticulously crafted Full Stack Data Science Course Syllabus by Almabetter. This syllabus empowers learners with in-depth knowledge across various subjects, aligning with foundational and advanced levels. Explore a diverse range of Data Science subjects that encompass crucial areas of Data Science, ensuring you're well-equipped to excel in this dynamic field.

Introduction to Computer Programming

  • Discover the essence of Python: Data Types, Indexing, and Slicing.
  • Harness the power of in-built functions, Conditional Statements, and Loops.
  • Unleash advanced concepts like Custom Functions, Lambda, and Map Functions.
  • Navigate OOPs concepts, Coding Best Practices, and Arrays.
  • Traverse complex territories such as Recursion, Sorting, and Competitive Coding.

Numerical Programming in Python

  • Unearth powerful tools: Packages, Libraries, Datetime, and Regex.
  • Command Line and File System manipulation for efficiency.
  • Dive into Git and GitHub for version control.
  • Embrace Data Wrangling using Pandas and Numpy and Data Visualization with Matplotlib and Seaborn.
  • Master Exploratory Data Analysis for insights.

Relational Databases

  • Master SQL from the ground up: Environment, Basic Commands, and Fundamentals.
  • Tackle complex tasks like Advanced SQL Joins and Window Functions.
  • Conquer Database Management, Schema Design, and Query Optimization.
  • Link SQL with Python and deal with intricate queries using CTE and Pivoting.
  • Hone skills in Type Casting and Mathematical Functions.

Data Visualization Tools

  • Grasp the fundamentals of Excel and its data exploration capabilities.
  • Elevate storytelling with Excel's powerful features.
  • Step into the realm of Tableau, Power BI, and Google Analytics.
  • Learn to choose the right visualizations for optimal communication.
  • Connect dashboarding with SQL for insightful displays.

Applied Statistics

  • Embrace key mathematical concepts: Calculus, Vector Algebra, and Matrix Algebra.
  • Dive deep into Probability Theory, Distributions, and Joint Distribution.
  • Understand Sampling and Statistical Inference, Hypothesis Testing, and A/B Testing for real-world insights.

Introduction to Machine Learning

  • Start your Machine Learning journey: Lifecycle and Algorithms.
  • Grasp Supervised Algorithms, Linear and Tree-based models.
  • Implement Unsupervised Algorithms: K-means and Hierarchical Clustering.
  • Prepare data for ML Models, and optimize through Cross-validation and Hyperparameter tuning.
  • Dive into advanced concepts like Bagging, Boosting, and Nonlinear Algorithms.
  • Explore NLP, Image Processing, Recommender Systems, and SQL Feature Engineering.

Product Analytics

  • Discover the essentials of Product Analytics.
  • Delve into Core Visualization Principles and Product Intelligence Platforms.
  • Master Advanced Query Optimization and Business Process Automation.

Choose your path: Distributed Machine Learning with Big Data Fundamentals, Data Warehousing, Apache Spark, or Deep Learning with Neural Networks, NLP, and Computer Vision. This comprehensive syllabus encompasses foundational and advanced knowledge, making you adept in Data Science.

Master In CS: Artificial Intelligence and Data Science Syllabus by Almabetter

Foundation Track

Embark on the solid Master In CS: Artificial Intelligence and Data Science Syllabus offered by Almabetter. Begin your journey with the Foundation Track that lays the groundwork for in-depth knowledge:

Introduction to Computer Programming:

  • Getting Started with Python
  • Python Control Flow
  • Object Oriented Programming
  • Indexing & Slicing
  • Operators in Data Types
  • Statements, Indentation & Conditionals
  • Conditional & Infinite Looping
  • OOPs in Python
  • Errors and Exception Handling
  • Lambda & Map Functions
  • Recursion - I and II
  • Sorting Algorithms
  • In-Built Functions & Methods
  • Advanced Looping Concepts
  • Loops & Iterations

Numerical Programming in Python:

  • Data Structures & Algorithms
  • Data wrangling Libraries
  • Data Visualization Libraries
  • Datetime, Regex & Beautiful Soup
  • Command Line & File System
  • Standard Data Management Libraries
  • Data Wrangling using Pandas and Numpy
  • Data Visualisation Libraries - Matplotlib & Seaborn
  • Data Visualization Tips & Best Practices
  • Efficient String Operations
  • Mastering Recursion Concept
  • Advanced Data Wrangling Concepts
  • Data Management Libraries
  • Skill Mastery Challenge
  • Exploratory Data Analysis - Case Study

Relational Databases:

  • Getting Started with SQL
  • SQL Environment & Basic Commands
  • Fundamentals of SQL Query
  • Dealing with Multiple Tables
  • Advanced SQL Joins
  • Mathematical & Data type conversion Functions
  • DateTime & String Functions
  • Window Functions
  • Miscellaneous Functions
  • Connect & Analyze Data with SQL & Python
  • Database Management & Schema Design
  • Competitive Coding & Query Optimisation
  • Complex queries using CTE & Pivoting
  • Type Casting & Math Functions
  • Advanced SQL Joins
  • Type Casting & Math Functions

Data Visualization Tools:

  • Fundamentals of Excel
  • Data Exploration with In-Built Functions
  • Storytelling with Excel
  • Advanced Dashboarding Concepts
  • Getting Started with Tableau Ecosystem
  • Dashboarding & Storytelling with Tableau
  • Choosing the Right Chart
  • Dashboarding with Power BI
  • Advanced Dashboarding Concepts with PowerBI
  • Customer & Web Analytics
  • Advanced Charts
  • Dashboarding with Business KPIs - E-commerce

Applied Statistics:

  • Foundational Math for DS
  • Advanced Math for DS
  • Probability Theory
  • Data Summarization
  • Discrete Probability Distributions
  • Continuous Probability Distributions
  • Joint Distribution
  • Sampling & Statistical Inference
  • Concept of Confidence
  • Hypothesis Testing
  • Inferential Statistics
  • Descriptive Statistics
  • Coding Assessment
  • Skill Mastery Challenge

Introduction to Machine Learning:

  • Getting Started With ML
  • ML Lifecycle
  • Implementing a simple Supervised Algorithm
  • Linear & Tree-based models
  • Implementing a simple Unsupervised Algorithm
  • Unsupervised Clustering: K-means & Hierarchical
  • Data Preparation for ML Models
  • Cross-validation
  • Hyperparameter tuning
  • TedX Views Prediction - Case Study
  • Customer Segmentation - Case Study
  • Time Series Analysis
  • Bagging & Boosting: Complex Algorithms
  • Nonlinear Algorithms - Polynomial Regression
  • SVM & Neural Networks
  • Natural Language Processing
  • Image processing
  • Recommender Systems
  • SQL Feature Engineering, Prediction, and Analysis

Specialization Track

Dive deeper into specialized subjects with the Specialization Track. Explore a range of domains, from Product Analytics and Business Case Studies to Distributed Machine Learning, Productionization of ML Systems, Introduction to Deep Learning, and Advanced Machine Learning. Gain expertise in various areas and enhance your skills in practical applications and real-world scenarios. This track is designed to provide a comprehensive understanding of advanced concepts and techniques within the realm of Data Science and artificial intelligence.

Product Analytics:

  • Getting Started with Product Analytics
  • Experimental Design & Analysis
  • Web Analytics
  • Introduction to Looker Studio
  • Customer Lifecycle Analysis
  • Marketing Performance Analysis
  • Getting started with Google Analytics
  • Reports in Google Analytics
  • Data Processing with Google Analytics
  • Introduction to Mixpanel
  • Experimental Design & Analysis

Business Case Studies:

  • Fraud Detection
  • Credit Risk Assessment
  • Supply Chain Optimization
  • Disease Diagnosis & Prognosis
  • Personalized Content Delivery
  • Social Network Analysis
  • User Behavior Analysis
  • Process Optimization
  • Healthcare Resource Allocation

Distributed Machine Learning:

  • Data Warehousing with Hive
  • Apache Spark using Python
  • Distributed ML Training
  • Big Data Fundamentals
  • Getting Started with Big Data
  • Communication & Data Consistency
  • Hadoop Commands
  • Hive Functions and Operators
  • Machine Learning with Spark ML
  • Feature Engineering & Prediction with Spark ML
  • Data Parallelism & Model Parallelism
  • Scalability & Fault-tolerance

Productionization of ML Systems:

  • MLOps Fundamentals
  • Model Deployment
  • Apache Airflow Essentials
  • Deploy ML Model on Scale
  • Machine Learning System Architecture
  • Build Classification Model
  • Create Rest API with Flask
  • Introduction to Docker & Kubernetes
  • Packaging the ML Model for Production
  • Getting Started with Deployment Platforms
  • Deploying the ML API with containers
  • Build Web app using Streamlit
  • Getting started with Apache AirFlow

Introduction to Deep Learning:

  • Neural Networks & Deep Learning
  • Improving Deep Neural Networks
  • Structuring ML Projects
  • Getting Started with TensorFlow & Keras
  • Tensorflow & Keras Implementation
  • Shallow Neural Networks
  • Deep Neural Networks
  • Practical Aspects of Deep Learning
  • Optimization Techniques in Deep Learning
  • Structuring Machine Learning Project Foundation

Advanced Machine Learning:

  • Handling Anomalies
  • Advanced Clustering Algorithms
  • Market Basket Analysis
  • Advanced Time Series Analysis
  • Customized Models for User Preferences
  • Handling data for Outlier events
  • Modelling for Outlier Events - Case Study
  • Advanced Clustering Techniques
  • Clustering Analysis - Case Study
  • Introduction to Market Basket Analysis
  • Data Preparation & Association Rule Mining
  • Evaluation Metrics & Visualization
  • Movie Recommendation - Case Study

Industry Immersion Track

Embark on the Industry Immersion Track to gain hands-on experience in a chosen industrial domain. Select from domains like FinTech (PayTM, RazorPay, PhonePe, American Express), E-commerce (Flipkart, Amazon, Myntra), InsurTech (Ditto, Plum, Healthiance), EdTech (AlmaBetter, Unacademy, UpGrad), Healthcare (Novartis, Fortis Health, Dr Lal PathLab), SportsTech (Dream11, MPL, Sportskeeda), or OTT Platforms (Netflix, MX Player, Voot).

Defining Problem Statement:

  • Background & Motivation
  • Problem Statement
  • Objectives
  • Scope & Limitations
  • Thesis Structure

Literature Review:

  • Artificial Intelligence and Machine Learning
  • Deep Learning Architectures
  • Object Detection and Tracking Techniques
  • State-of-the-Art Models
  • Evaluation Metrics
  • Existing Challenges and Future Directions

Data Preparation:

  • Dataset Selection
  • Data Preprocessing
  • Data Augmentation Techniques
  • Dataset Partitioning
  • Ethical Considerations and Data Privacy

Model Development:

  • Model Selection and Rationale
  • Model Architecture & System Design
  • Loss Function and Optimization Strategy
  • Training and Validation
  • Model Interpretability and Explainability
  • Model Compression Techniques

Model Evaluation:

  • Performance Metrics
  • Comparative Analysis with Existing Models
  • Real-World Performance Evaluation
  • Model Robustness and Generalization

Product & Testing:

  • Model Deployment
  • API Design and Integration
  • Testing & Performance
  • User Interface Design
  • System Performance & Scalability

Conclusion & Future Work:

  • Summary of Findings
  • Evaluation of the Project
  • Limitations and Challenges
  • Future Directions and Recommendations

This Data Science course syllabus for AI and Data Science covers foundational and specialized tracks, culminating in hands-on industry experience. Explore real-world applications and gain the skills needed for success in this rapidly evolving field.

Best Data Science Subjects List

Explore a diverse array of essential Data Science subjects to build a strong foundation for your journey in this dynamic field:

  • Introduction and Importance of Data Science: Grasp the significance and scope of Data Science in modern industries.
  • Statistics: Master statistical methods crucial for insightful data analysis.
  • Information Visualization: Convey complex insights through engaging visual representations.
  • Data Mining, Data Structures, and Data Manipulation: Navigate the techniques for extracting valuable information from datasets.
  • Algorithms used in Machine Learning: Dive into algorithms that power predictive models and decision-making.
  • Data Scientist Roles and Responsibilities: Understand the multifaceted responsibilities of a data scientist.
  • Data Acquisition and Data Science Life Cycle: Learn how to collect and manage data throughout its lifecycle.
  • Deploying Recommender Systems on Real-World Data Sets: Apply recommendation algorithms to real-world scenarios.
  • Experimentation, Evaluation, and Project Deployment Tools: Assess and deploy Data Science projects effectively.
  • Predictive Analytics and Segmentation using Clustering: Harness predictive power through clustering techniques.
  • Applied Mathematics and Informatics: Apply advanced mathematics to Data Science applications.
  • Working on Data Mining, Data Structures, and Data Manipulation: Get hands-on with practical data manipulation techniques.
  • Big Data Fundamentals and Hadoop Integration with R: Grasp the basics of big data and its integration with Hadoop and R.

Whether you're a beginner or seeking advanced knowledge, these subjects provide a well-rounded understanding of the Data Science syllabus, fostering expertise in various areas of this multidisciplinary field.

What are the Important Areas in Data Science?

Explore the pivotal realms within the expansive Data Science domain, shaping the Data Science syllabus and enriching your knowledge across varied Data Science subjects. Whether pursuing BSc Data Science subjects, BCA Data Science subjects or CSE Data Science subjects, this curriculum provides you with essential Data Science skills for diverse landscapes.

  • Data Engineering: Master the art of data collection, storage, and processing.
  • Big Data Engineering: Grasp the intricacies of managing and analyzing massive datasets.
  • Data Mining: Uncover hidden patterns and insights from vast data repositories.
  • Database Management: Learn efficient methods to store, retrieve, and manipulate data.
  • Predictive Analytics: Harness predictive modeling techniques to foresee future trends.
  • Data Analytics: Dive deep into data analysis to derive actionable insights.
  • Machine Learning or Cognitive Computing: Delve into Machine Learning techniques, enabling intelligent decision-making.

These critical areas compose the backbone of the Data Science course syllabus, fostering skills needed to excel in the dynamic landscape of data-driven industries.

Components of Data Science Syllabus

The Data Science curriculum is meticulously designed to help beginners and experts gather essential knowledge across various Data Science subjects. Beyond theoretical learning, the syllabus emphasizes practical applications, addressing real-world business challenges using statistical tools. This comprehensive approach ensures that learners grasp the foundational concepts and develop the practical skills necessary to excel in Data Science.

This makes the skills acquired during the trajectory of Data Science and Data Analytic courses truly indispensable in Data Science. Whether you're seeking the Data Science Syllabus for beginners or aiming to delve deeper, here's a thoughtfully curated general syllabus of Data Science.

It encompasses the three most pivotal components that most universities follow, ensuring students are adept in both theory and practice:

  • Big Data: Grasp the intricacies of managing and extracting insights from massive datasets.
  • Machine Learning: Dive into algorithms and models for predictive analysis.
  • Business Acumen & Artificial Intelligence: Understand the synergy between Data Science, business goals, and AI concepts.
  • Modeling in Data Science: Master the art of creating effective models for data analysis and decision-making.

These foundational pillars empower learners to adapt seamlessly to the dynamic world of Data Science.

Is Coding Needed in Data Science?

Coding is an essential skill in the realm of Data Science. While the emphasis might vary based on your specific role and focus, a strong coding foundation is crucial. Coding enables you to manipulate, clean, and preprocess data, build and deploy models, and extract insights. Expertise in Python and R programming languages is necessary to implement algorithms, analyze data, and create visualizations.

Whether you're pursuing a Data Science course syllabus for beginners or seeking advanced knowledge, coding remains a fundamental aspect. It's intertwined with various Data Science subjects such as data manipulation, Machine Learning, etc. Whether you're exploring the BTech Data Science syllabus, CSE Data Science syllabus, or any other specialization, coding proficiency empowers you to excel in the multifaceted landscape of Data Science.

Coding plays a crucial role in Data Science by enabling professionals to extract valuable insights from vast datasets, develop predictive models, and create data-driven solutions. Here's an example that illustrates how coding is essential in Data Science:

Example: Predictive Model for Customer Churn

Imagine a telecom company aiming to reduce customer churn rates. Their massive dataset contains customer information, usage patterns, and churn history. Coding skills are essential to build a predictive model that forecasts which customers are likely to churn.

1. Data Cleaning and Preprocessing: Coding is used to clean and preprocess the dataset. This involves handling missing values, encoding categorical variables, and scaling numerical features.

2. Feature Engineering: Coding helps create new features from existing data, enhancing the model's predictive power. For instance, calculating the average usage over time or creating a churn history variable.

3. Algorithm Implementation: Coding is used to implement Machine Learning algorithms such as decision trees, random forests, or logistic regression. Libraries like scikit-learn in Python provide pre-built functions to implement these algorithms.

4. Training and Validation: With coding, you split the dataset into training and validation sets, train the model on the training data, and validate its performance on the validation set.

5. Hyperparameter Tuning: Coding allows you to fine-tune model hyperparameters for optimal performance. This involves testing various parameter combinations and evaluating their impact on the model's accuracy.

6. Model Evaluation: Coding evaluates the model's performance using accuracy, precision, recall, and F1-score metrics. This helps assess how well the model predicts customer churn.

7. Deployment: Once the model is trained and validated, coding is required to deploy it into a production environment. This might involve creating APIs for real-time predictions.

8. Updating and Maintenance: As new data is collected, coding helps update and retrain the model to ensure it stays accurate over time.

In this example, coding is integral at every stage of the Data Science process, from data cleaning and feature engineering to algorithm implementation, model evaluation, and deployment. It empowers data scientists to transform raw data into actionable insights, allowing businesses to make informed decisions and address real-world challenges effectively.

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