PG Diploma in Data Science
Data Science:
It is an interdisciplinary field that combines mathematics, computer science, and business knowledge to process and interpret complex data. Practitioners clean raw data, apply statistical models, and use machine learning to generate predictions or solutions. This process transforms unstructured data into strategic business value.
Key process steps:
- Data collection from sources like databases or sensors.
- Cleaning and preparation to ensure accuracy.
- Analysis using tools like python or r for patterns and modeling.
- Visualization with tableau or power bi for clear communication.
Learning Data Science equips you with skills for high-demand IT roles, enhancing employability in india's booming tech sector. It offers lucrative salaries, versatility across industries, and the ability to solve real-world problems through data-driven insights.
Why you should learn Data Science:
Due to its high job demand, India anticipates over 11 million data science and analytics jobs by 2026, driven by AI and digital transformation. Open positions exceed 200,000 annually, with roles like data scientist and analyst growing 36% globally by 2031. This surge aligns with boosting graduate placements.
Job Market Demand
Data Scientists in India earn competitive pay, with mid-level roles often surpassing ₹10-20 lakhs yearly. Entry-level positions start strong, offering rapid advancement for skilled professionals. High demand ensures job security and financial rewards. Data Science remains relevant amid AI investments, with opportunities expanding beyond metros to smaller cities. Data science roles offer strong employability with average salaries exceeding ₹10-15 lakhs annually for mid-level positions, demand surges in sectors like Fintech and e-commerce, making it ideal for your institute's programs. Training in cloud-integrated data science boosts graduate placement rates.
Course Contents
| R: |
|---|
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Understanding CRAN |
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R Studio the IDE |
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Vectors |
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Basic Operations |
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R Functions |
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Data frame |
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Decision making |
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File Handling |
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Visualisation |
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Basic statistics |
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EDA |
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Linear Regression in R |
| Python |
|---|
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Functions |
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Packages |
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File Handling |
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Oops Concepts |
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Database Access |
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Introduction to RDBMS |
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Working with csv , xml and Json files |
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Data Analytics |
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Introduction to Numpy |
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Introduction to Pandas |
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Operating on data |
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Handling missing data |
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Working with time series |
| SQL: |
|---|
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Introduction to Basic Database Concepts |
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E-R Modelling and Diagram |
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Normalization |
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DDL and DML Statements |
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Working with Queries (DQL) |
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Aggregate Functions |
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Joins and Set Operations |
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Implementation of Data integrity |
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Working with Constraints |
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Data Control language (DCL) |
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Writing Transact-SQL (T-SQL) |
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Working with Stored Procedures and Functions |
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Implementing Triggers |
| Machine Learning: |
|---|
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Matplotlib & EDA |
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Preprocessing |
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Linear Regression |
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Classification |
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Logistics |
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SVM |
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Native Bays |
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KNN |
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Decision Tree |
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Random Forest |
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Bagging & Boosting Model Saving |
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Clustering, K Means |
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Performance measures |
| Statistics: |
|---|
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Mean |
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Mode |
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Median |
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Standard deviation |
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Probability & Distribution |
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Correlation |
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Combination |
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R Studio and R Installation |
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R for Statistics and mathematics |
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Data Modeling |
| Tableau: |
|---|
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Introduction |
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Field Types and Visual Cues |
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Tableau Desktop |
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Opening and Closing Tableau |
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Data Source Page |
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Data Terminology and Definitions |
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Data Connections in the Tableau |
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Organizing and Simplifying Data |
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Formatting and Annotations |
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Tableau Generated Fields |
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Chart Types |
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Mapping |
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Statistics |
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Dashboards |
| PowerBI: |
|---|
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Introduction of Power BI |
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Installation |
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Power Query Editor (ETL), |
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Visualization and Reports |
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Dashboard Basic |
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Filters and Slicers |
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Calculated Columns and Measures |
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Calculated Tables and Data Modelling |
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DAX Function |
| Artificial Intelligence: |
|---|
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Deep Machine Learning |
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Perceptron |
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Artificial Neural Networks |
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Multi Layered ANN |
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Introduction to NLP |
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Introduction to TENSOR Flow |
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KERAS |
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Deep Neural Network |
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Convolutional Neural Networks (CNNs) |
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Recurrent Neural Networks (RNNs) |
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Auto Encoders |
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Reinforcement Learning |
| Advance Excel |
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Advanced formulas and functions |
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Formula referencing |
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Reference functions |
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Pivot table |
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Logical functions |
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Lookup functions |
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Statistical and financial functions |
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Data management analysis |
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Data validation |
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Sorting and filtering |
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Data visualization |
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Automation and security |
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Macro basics |
