Data Science Course Syllabus in Hyderabad
The demand for data science professionals is growing rapidly as companies increasingly rely on data-driven decision-making. If you’re considering a Data Science Course in Hyderabad, understanding the course syllabus is crucial to ensure you acquire the right skills. A well-structured data science syllabus covers essential topics like Python, machine learning, deep learning, big data, and AI to prepare students for real-world industry challenges.
Overview of Data Science Course Structure
Most data science courses in Hyderabad are designed for freshers, working professionals, and IT experts who want to switch to a data-driven career. The syllabus typically includes:
Fundamentals of Data Science
Programming for Data Science (Python/R)
Data Analysis & Visualization
Machine Learning & Deep Learning
Big Data & Cloud Technologies
Capstone Projects & Real-world Case Studies
Detailed Data Science Syllabus
1. Introduction to Data Science
Understanding what data science is and its significance.
Applications of data science in industries like healthcare, finance, and e-commerce.
Overview of different data science tools and technologies.
2. Programming for Data Science (Python/R)
Basics of Python and R programming for data science.
Libraries used in data science: NumPy, Pandas, Matplotlib, Seaborn, Scikit-Learn.
Writing data processing scripts and automating workflows.
3. Statistics & Mathematics for Data Science
Probability and Statistics: Mean, Median, Mode, Variance, Standard Deviation.
Hypothesis testing, Regression Analysis, and Probability Distributions.
Linear Algebra and Matrices for Machine Learning.
4. Data Wrangling & Data Visualization
Cleaning and Preprocessing Data using Pandas and NumPy.
Handling missing values, outliers, and inconsistencies in datasets.
Data Visualization using Matplotlib, Seaborn, and Power BI/Tableau.
5. Exploratory Data Analysis (EDA)
Understanding data patterns and correlations.
Feature selection and feature engineering techniques.
Hands-on EDA using Python/R.
6. Machine Learning & Artificial Intelligence
Supervised vs Unsupervised Learning.
Algorithms like Linear Regression, Logistic Regression, Decision Trees, Random Forests, SVM, KNN.
Clustering techniques: K-Means, Hierarchical Clustering, DBSCAN.
Model Evaluation using Confusion Matrix, ROC Curve, Precision-Recall.
7. Deep Learning & Neural Networks
Basics of Deep Learning and Neural Networks.
Introduction to TensorFlow & Keras for deep learning.
CNNs (Convolutional Neural Networks) for image recognition.
Recurrent Neural Networks (RNN) for sequential data.
8. Big Data Technologies & Cloud Computing
Introduction to Big Data and Hadoop.
Working with Apache Spark & PySpark for large-scale data processing.
Cloud computing with AWS, Azure, or Google Cloud for data science projects.
9. Natural Language Processing (NLP) & AI Applications
Text preprocessing: Tokenization, Lemmatization, Stopwords.
Sentiment Analysis and Chatbot Development.
Real-world applications like Speech Recognition & Text Summarization.
10. Capstone Projects & Industry Case Studies
End-to-end data science projects using real datasets.
Working on healthcare, banking, retail, or social media analytics projects.
Resume building and interview preparation for data science roles.
Conclusion
A Data Science Course in Hyderabad covers a comprehensive syllabus to help learners develop essential skills in data analytics, machine learning, and AI. With hands-on projects, real-world applications, and expert guidance, this course equips students with job-ready skills to thrive in the fast-growing field of data science.
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