Certificate Program in Data Science
Course Overview
Course Contents
The Basic Certificate Program in Data Science provides a comprehensive introduction to the essential concepts and techniques in data science. Covering data collection, cleaning, visualization, statistics and machine learning. This course equips students with practical skills in Python and data analysis, preparing them for real-world data science projects and further studies.
| Module | Details |
|---|---|
| Module 1 Introduction to Data Science |
Objective: Gain a solid understanding of the fundamentals of data science, its various applications and the stages involved in a data science project. Learn about the key roles, responsibilities and skills required in the field of data science. |
| Module 2 Data Collection and Acquisition |
Objective: Explore different methods of data collection, including retrieving data from APIs, web scraping and databases. Learn how to acquire both structured and unstructured data for analysis. |
| Module 3 Data Cleaning and Preprocessing |
Objective: Learn techniques for cleaning and transforming raw data into a usable format. This includes handling missing values, dealing with outliers and applying normalization to prepare data for analysis. |
| Module 4 Exploratory Data Analysis (EDA) |
Objective: Conduct exploratory data analysis to understand the distribution of data, detect patterns and generate insights using statistical methods and visualizations. |
| Module 5 Data Visualization |
Objective: Master the creation of effective visualizations using tools such as Matplotlib and Seaborn. Learn how to communicate key insights and trends through charts, graphs and plots. |
| Module 6 Introduction to Statistics for Data Science |
Objective: Acquire foundational knowledge in statistics, including concepts like probability, distributions, hypothesis testing and correlation analysis, which are essential for interpreting data. |
| Module 7 Introduction to Python for Data Science |
Objective: Learn to use Python for data manipulation, focusing on libraries like Pandas and NumPy to process, clean and analyze data effectively. |
| Module 8 Machine Learning Fundamentals |
Objective: Understand the basics of machine learning, including the distinction between supervised and unsupervised learning, key algorithms and how to evaluate machine learning models. |
| Module 9 Supervised Learning Techniques |
Objective: Learn about supervised learning algorithms, including linear regression, logistic regression and decision trees. Understand how to build, train and evaluate these models. |
| Module 10 Unsupervised Learning and Clustering |
Objective: Explore unsupervised learning techniques, particularly clustering methods such as K-means and hierarchical clustering, to identify patterns in unlabeled data. |
| Module 11 Model Evaluation and Performance Metrics |
Objective: Learn how to evaluate machine learning models using various performance metrics, including accuracy, precision, recall, F1 score and cross-validation techniques to ensure robust model performance. |
| Module 12 Real-World Data Science Project |
Objective: Apply all the skills learned in the course to a hands-on data science project. Students will analyze a real-world dataset, perform necessary preprocessing, apply machine learning models and present their findings. |
Course Fee
Your Investment
Rs 60,000/=
Admission Requirements
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Core Prerequisites
General computer literacy and basic operational comfort with standard software.
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Beginner-Friendly Entry
Zero technical background required. Built to guide learners from ground-zero concepts into foundational Python, data analysis, and machine learning principles.
The Basic Certificate Program in Data Science is designed for:
- Beginners interested in learning data science from scratch, with no prior experience required.
- Career Changers seeking to transition into the growing field of data science and analytics.
- University Students wanting to enhance their academic studies with practical skills in data science.
- Professionals aiming to upskill in Python, machine learning and data analysis for career advancement in IT, finance, marketing or other data-driven industries.
This program is ideal for anyone looking to gain a solid foundation in data science and prepare for further studies or career opportunities in the field.
Please review the entry requirements before starting your application.
