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Course Overview:
This course provides a deep dive into data science, covering essential topics such as data wrangling, statistical analysis, machine learning, and data visualization. Designed for learners looking to gain hands-on experience, the course will help students build a strong foundation and apply data science techniques to solve real-world problems.
Course Objectives:
- Understand and apply core data science techniques
- Gain proficiency in Python and SQL for data analysis
- Master advanced machine learning concepts and algorithms
- Develop skills in data visualization and storytelling
- Build hands-on experience with industry-relevant projects
Modules
Data Wrangling and Exploration with SQL and Pandas
Statistics and Probability for Data Science
Introduction to machine learning concepts and types (supervised vs. unsupervised)
Advanced Machine Learning – Classification and Ensemble Techniques
Unsupervised Learning and Clustering
Data Visualization and Storytelling
Capstone Project and Industry Applications
Introduction to Data Science and Python for Data Analysis
Key Topics:
- Overview of Data Science and its applications in different industries
- Introduction to Python for data analysis: Jupyter notebooks, libraries (NumPy, Pandas)
- Data manipulation with Pandas: Importing, cleaning, and transforming data
Virtual Lab:
- Data cleaning and analysis in Python
Project:
- Importing and preparing a dataset for analysis
Data Wrangling and Exploration with SQL and Pandas
Key Topics:
- SQL fundamentals: Data extraction and manipulation
- Data cleaning and preprocessing techniques
- Exploratory data analysis (EDA) to uncover patterns and insights
Virtual Lab:
- Combining Python and SQL for data exploration
Project:
- Conduct an exploratory data analysis on a real-world dataset
Statistics and Probability for Data Science
Topics:
- Descriptive statistics: Mean, median, standard deviation, and distributions
- Probability concepts: Conditional probability, Bayes’ theorem
- Hypothesis testing, confidence intervals, and p-values
Virtual Lab:
- Implementing statistical tests in Python
Project:
- Perform hypothesis testing on a dataset to validate assumptions
Introduction to machine learning concepts and types (supervised vs. unsupervised)
Key Topics:
- Linear regression and logistic regression
- Evaluation metrics: Accuracy, precision, recall, and F1-score
Virtual Lab:
- Building regression models with Python’s Scikit-Learn
Project:
- Build and evaluate a predictive model using linear or logistic regression
Advanced Machine Learning – Classification and Ensemble Techniques
Key Topics:
- Decision trees, random forests, and ensemble methods
- Advanced evaluation metrics (ROC-AUC, confusion matrix)
- Hyperparameter tuning and model optimization
Virtual Lab
- Implementing classification models with Scikit-Learn
Project:
- Develop and tune a classification model on a real-world dataset
Unsupervised Learning and Clustering
Key Topics:
- Clustering techniques: K-means, hierarchical clustering
- Dimensionality reduction with PCA (Principal Component Analysis)
- Applications of clustering in segmentation and anomaly detection
Virtual Lab:
- Applying K-means clustering and PCA with Python
Project:
- Conduct a clustering analysis on a dataset and interpret the results
Data Visualization and Storytelling
Key Topics:
- Principles of effective data visualization
- Tools: Matplotlib, Seaborn, and introductory Tableau
- Crafting a data narrative for stakeholders
Virtual Lab:
- Creating visualizations with Python and Tableau
Project:
- Build a data visualization dashboard to communicate insights from a dataset
Capstone Project and Industry Applications
Key Topics:
- Industry applications of data science: Finance, healthcare, marketing, etc.
- Ethical considerations and responsible AI
- Capstone project presentation and feedback
Virtual Lab:
- Final project development
Capstone Project:
- End-to-end data science project, including data cleaning, analysis, model building, and visualization
Learning Outcomes
- Understand and implement fundamental data science techniques, including data wrangling, statistical analysis, and machine learning.
- Use Python libraries such as Pandas, NumPy, and Scikit-Learn to clean, analyze, and visualize data.
- Develop and evaluate supervised and unsupervised machine learning models, including linear regression, decision trees, and clustering algorithms.
- Explore datasets to uncover patterns, insights, and potential features for predictive modeling.
- Create meaningful visualizations using Python and introductory Tableau, effectively communicating data insights to stakeholders.
- Work on real-world datasets, applying data science skills to build a complete, portfolio-ready project from data cleaning to model building.
- Learn about ethical considerations in data science, including responsible AI practices, privacy concerns, and the importance of transparency in algorithms.
Fees & Enrollment
- Course Fee: INR 49,999
Career Services
- 100% Job Placement Support
- Interview Practice and Preparation
- 1 on 1 Career Mentoring Sessions
- Career Development Workshops
- Resume and LinkedIn Profile Enhancement
Data Science Course
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Course Duration: 8 weeks
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Course Format: Online Interactive Sessions with Virtual Lab Components
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Weekly Schedule: 2-hour sessions, held twice on weekdays or weekends.
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Certification: Globally Recognized Certificate in Data Science
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While prior experience with Python or SQL is helpful, this course is designed to accommodate beginners as well. Foundational topics in Python and SQL are covered, allowing participants to build up their skills gradually.
Yes, in Week 8, students will complete a capstone project that integrates all the skills learned throughout the course. This project is designed to be portfolio-ready and demonstrates a practical application of data science.
You will need access to a computer with Python installed (free to download), as well as Jupyter Notebooks or an IDE like Anaconda. For data visualization, we will use Tableau (free for students) and open-source libraries like Matplotlib and Seaborn.
This course is designed to equip you with in-demand skills in data science, enabling you to pursue roles such as Data Analyst, Data Scientist, Machine Learning Engineer, or similar. The hands-on projects and capstone will provide you with portfolio-ready work to showcase to potential employers.
Our instructors and support team are available to assist you through live Q&A sessions, office hours, and email support. You’ll also have access to a community forum where you can connect with peers and instructors.
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Study at your own pace with accessible online courses and tutorials.
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