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Data Science
Data Science Program Summary
The Accelerated Data Science Program is a 3-month intensive course designed to equip students with essential data science skills. Covering a comprehensive range of topics, the curriculum includes data collection and cleaning, exploratory data analysis (EDA), statistical analysis, machine learning, deep learning, natural language processing (NLP), and big data. Students will gain hands-on experience with tools such as Python, Jupyter Notebooks, and SQL, and will work on real-world projects to apply their knowledge. The course culminates in a capstone project, allowing students to demonstrate their skills in data collection, cleaning, EDA, and model building. Assignments, a final exam, and a certificate of completion ensure a thorough understanding and practical application of data science concepts.
Who Should Attend
The Data Science Program is ideal for individuals who are looking to quickly transition into a data science career or enhance their existing skill set with intensive, hands-on training. This course is perfect for:
Aspiring Data Scientists: Individuals with a strong interest in data science who want to gain practical skills and knowledge to start a career in this field.
Working Professionals: Employees in roles related to data analysis, business intelligence, or IT who want to expand their expertise and advance their careers by acquiring data science competencies.
Recent Graduates: Graduates with degrees in STEM fields (science, technology, engineering, and mathematics) who are seeking to specialize in data science and improve their employability.
Career Switchers: Professionals from different backgrounds looking to transition into the data science industry, leveraging their analytical and problem-solving skills in a new domain.
Technical Managers and Executives: Leaders who need a solid understanding of data science to make informed decisions, manage data science teams, or integrate data-driven strategies into their business operations.
No prior experience in data science is required, but a basic understanding of programming and statistics will be beneficial. The course provides a thorough foundation and practical experience to ensure all participants are well-prepared to succeed in the data science field.
Program Curriculum
Introduction to Data Science
Overview of Data Science
Definition and lifecycle
Applications in various industries
Tools and Technologies
Introduction to Python and Jupyter Notebooks
Overview of SQL databases
Data Collection and Cleaning
Data Collection
Data sources: APIs and Web Scraping
Basics of using APIs and web scraping with BeautifulSoup
Data Cleaning
Handling missing values and duplicates
Data normalization and standardization with pandas
Exploratory Data Analysis (EDA)
Data Exploration
Descriptive statistics and data visualization
Using matplotlib and seaborn for visualizations
Advanced EDA
Correlation and causation
Basic feature engineering
Statistical Analysis
Probability and Statistics
Basic probability theory
Descriptive and inferential statistics
Statistical Tests
T-tests and Chi-square tests
Confidence intervals and p-values
Machine Learning Fundamentals
Introduction to Machine Learning
Supervised vs. Unsupervised Learning
Introduction to scikit-learn
Linear regression and logistic regression
Classification Algorithms
K-Nearest Neighbors
Decision Trees
Advanced Machine Learning
Ensemble Methods
Random Forests
Gradient Boosting Machines
Unsupervised Learning
K-Means Clustering
Principal Component Analysis (PCA)
Introduction to Deep Learning
Neural Networks Basics
Basics of neural networks and activation functions
Introduction to TensorFlow and Keras
Deep Learning Models
– Convolutional Neural Networks (CNNs)
Natural Language Processing (NLP)
Introduction to NLP
Text preprocessing and tokenization
Bag of Words and TF-IDF
Advanced NLP Techniques
Word Embeddings (Word2Vec)
Sentiment Analysis
Data Visualization and Reporting
Data Visualization Techniques
Advanced plotting with matplotlib and seaborn
Interactive visualizations with plotly
Reporting and Communication
Creating effective reports and presentations
Storytelling with data
Introduction to Big Data
Understanding Big Data
Big Data challenges and applications
Overview of Hadoop and Spark
Big Data Tools
Introduction to PySpark
Capstone Project
Project Planning and Execution
Scoping and planning the project
Data collection and cleaning
Exploratory data analysis and model building
Final evaluation and presentation
Course Includes:
- Course Fee :
-
₹
45,00035,000
- Duration:
- 15 weeks
- Language:
- English
- Certifications:
- Yes