Advanced Diploma In Data Science
Subjects
- Introduction to Data Science and Analytics
- Programming for Data Science (Python & R)
- Data Wrangling and Preprocessing
- Exploratory Data Analysis (EDA) and Data Visualization
- Statistical Analysis and Probability
- Machine Learning Fundamentals
Details
Description
Course Name: Advanced Diploma in Data Science
Course Id: ADDS
Eligibility: 10+2 (Higher Secondary) or Equivalent.
Objective: To equip learners with advanced tools, techniques, and methodologies for working with big data and complex datasets. To prepare students for careers in data analytics, data engineering, machine learning, and artificial intelligence.
Duration: ONE YEAR
Syllabus:-
Introduction to Data Science and Analytics: Overview of Data Science, Importance and Applications of Data Science, Data Science Lifecycle, Data Science vs. Business Intelligence vs. Big Data, Role of a Data Scientist, Structured vs. Unstructured Data, Basics of Data Cleaning and Preprocessing, Introduction to Data Visualization, Fundamentals of Exploratory Data Analysis (EDA), Ethical Considerations in Data Science.
Programming for Data Science (Python & R): Introduction to Python for Data Science, Introduction to R for Data Science, Data Types and Structures, File Handling and Data Importing, Data Manipulation using Pandas and dplyr, Loops, Functions, and Conditional Statements, NumPy and Matplotlib for Scientific Computing, Introduction to Jupyter Notebook and RStudio, Debugging and Error Handling, Best Practices in Coding for Data Science.
Data Wrangling and Preprocessing: Handling Missing Data, Data Cleaning Techniques, Feature Engineering and Feature Selection, Data Transformation (Scaling and Normalization), Handling Categorical Data (One-Hot Encoding, Label Encoding), Outlier Detection and Treatment, Data Imbalance Handling (SMOTE, Undersampling), Data Integration and Merging, Working with Large Datasets, Introduction to SQL for Data Science.
Exploratory Data Analysis (EDA) and Data Visualization: Introduction to EDA, Summary Statistics and Data Distribution, Data Visualization using Matplotlib & Seaborn, Interactive Visualization with Plotly, Correlation and Relationship Analysis, Dimensionality Reduction Techniques (PCA, t-SNE), Heatmaps and Clustered Data Visualization, Time Series Data Visualization, Customizing Graphs for Business Insights, Storytelling with Data.
Statistical Analysis and Probability: Basics of Descriptive and Inferential Statistics, Measures of Central Tendency and Variability, Probability Distributions (Normal, Binomial, Poisson), Hypothesis Testing (T-test, Chi-square test, ANOVA), Confidence Intervals and Significance Levels, Bayesian Statistics and Its Applications, Correlation vs. Causation, Statistical Power and Sample Size, A/B Testing and Experimentation, Monte Carlo Simulations.
Machine Learning Fundamentals: Supervised vs. Unsupervised Learning, Linear Regression and Logistic Regression, Decision Trees and Random Forest, Support Vector Machines (SVM), Naïve Bayes Classifier, K-Nearest Neighbors (KNN), Clustering Techniques (K-Means, Hierarchical), Evaluation Metrics for Model Performance (Accuracy, Precision, Recall, F1-Score), Cross-Validation Techniques, Bias-Variance Tradeoff.