Data Science Course Syllabus

Explore the complete Data Science Course Syllabus in Chennai at Asmorix Technologies. Learn Python, Machine Learning, Artificial Intelligence, SQL, Tableau, Power BI, Deep Learning, and real-time projects designed for industry-ready skills.

Why Learn Data Science in 2026

Data Science is one of the fastest-growing career domains in the IT industry. Companies use Data Science for business intelligence, automation, customer analytics, Artificial Intelligence, and predictive analysis. Learning Data Science helps students build careers in Machine Learning, AI, Business Analytics, and Big Data technologies.

The Data Science Course Syllabus at Asmorix Technologies is designed based on current industry requirements and includes practical implementation using Python, Machine Learning, SQL, Tableau, Power BI, and Deep Learning concepts.

Tools Covered in Data Science Course Syllabus

Students will gain hands-on experience with industry-standard tools including:

• Python
• Pandas
• NumPy
• Matplotlib
• Scikit-learn
• TensorFlow
• SQL
• Tableau
• Power BI
• Jupyter Notebook

These tools are widely used in real-world Data Science and AI projects.

Introduction to Data Science

• Overview of Data Science
• Importance of Data Science in Various Industries
• Data Science Life Cycle

What is Data Science?

• Definition and Scope
• Key Components of Data Science
• Real-world Applications

Prerequisites for Learning Data Science

• Basic Mathematics & Statistics
• Programming Fundamentals (Python/R)
• Understanding Databases and SQL

Python for Data Science

• Python Installation & Setup
• Jupyter Notebook & IDEs
• Python Basics (Variables, Data Types, Operators)

Data Structures in Python

• Lists, Tuples, Sets, Dictionaries
• Comprehensions & Lambda Functions

Python for Data Analysis

NumPy for Numerical Computing

• Arrays, Indexing, Broadcasting, and Mathematical Functions

Pandas for Data Manipulation

• DataFrames, Series, Data Cleaning, and Transformation

Data Handling and Preprocessing

• Data Collection & Cleaning
• Handling Missing Values
• Data Formatting and Normalization
• Feature Engineering

Data Visualization

Matplotlib & Seaborn

• Line, Bar, Histogram, Scatter Plots
• Heatmaps, Pairplots, and Advanced Visualizations

Plotly & Dash for Interactive Visualization

• Creating Dynamic Dashboards

Statistics and Probability for Data Science

• Mean, Median, Mode, Variance, Standard Deviation
• Hypothesis Testing
• Confidence Intervals
• p-values and z-scores

Machine Learning with Python

• Introduction to Machine Learning
• Supervised vs Unsupervised Learning
• Applications of Machine Learning

Supervised Learning Algorithms

Regression Techniques

• Linear Regression
• Polynomial Regression
• Ridge & Lasso Regression

Classification Techniques

• Logistic Regression
• Decision Trees
• Random Forest
• Naive Bayes
• Support Vector Machines (SVM)

Unsupervised Learning Algorithms

Clustering

• K-Means Clustering
• Hierarchical Clustering

Dimensionality Reduction

• Principal Component Analysis (PCA)
• t-SNE

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Deep Learning and Neural Networks

• Difference Between Machine Learning and Deep Learning
• Neural Network Architecture

Artificial Neural Networks (ANN)

• Perceptron Model
• Activation Functions

Convolutional Neural Networks (CNN)

• Image Processing Basics
• CNN Architecture (Convolution, Pooling, Fully Connected Layers)

Recurrent Neural Networks (RNN) and LSTMs

• Understanding Sequential Data
• Applications in Time Series & NLP

Natural Language Processing (NLP)

• Text Processing and Tokenization
• Stopwords, Lemmatization, Stemming
• Bag of Words & TF-IDF

Advanced NLP Techniques

• Word Embeddings (Word2Vec, GloVe)
• Named Entity Recognition (NER)

Time Series Analysis

• Forecasting Methods
• ARIMA, SARIMA, Prophet Model
• Handling Seasonality & Trends

Big Data Technologies for Data Science

• Introduction to Big Data
• Hadoop Ecosystem
• Spark for Large-scale Data Processing

Model Deployment & MLOps

• Deploying Machine Learning Models
• Flask & FastAPI
• Streamlit for Web Apps

MLOps & Model Monitoring

• CI/CD for Machine Learning
• Model Tracking and Logging

Data Science Projects & Case Studies

• Predictive Analytics
• Recommender Systems
• Fraud Detection

The Data Science Course Syllabus at Asmorix Technologies is designed to help students and professionals master industry-demand skills through practical learning, real-time projects, and modern AI technologies.

Career Opportunities After Completing Data Science Course Syllabus

After completing the Data Science Course Syllabus, students can apply for roles such as:

• Data Scientist
• Machine Learning Engineer
• Data Analyst
• AI Engineer
• Business Analyst
• Python Developer

Data Science professionals are highly demanded across healthcare, finance, e-commerce, IT, and AI industries.