Curriculum
The curriculum has been designed by faculty at Sri Vidya Tech and leading industry leaders. The teaching, content and projects in the course are by world-renowned faculty and other practicing management professionals from leading companies.
Module 1: Introduction to Data Science (Duration-1hr)
- What is Data Science?
- What is Machine Learning?
- What is Deep Learning?
- What is AI?
- Data Analytics & it’s types
Module 2: Introduction to Python (Duration-1hr)
- What is Python?
- Why Python?
- Installing Python
- Python IDEs
- Jupyter Notebook Overview
Module 3: Python Basics (Duration-5hrs)
- Python Basic Data types
- Lists
- Slicing
- IF statements
- Loops
- Dictionaries
- Tuples
- Functions
- Array
- Selection by position & Labels
Module 4: Python Packages (Duration-2hrs)
- Pandas
- Numpy
- Sci-kit Learn
- Mat-plot library
Module 5: Importing Data (Duration-1hr)
- Reading CSV files
- Saving in Python data
- Loading Python data objects
- Writing data to csv file
Module 6: Manipulating Data (Duration-1hr)
- Selecting rows/observations
- Rounding Number
- Selecting columns/fields
- Merging data
- Data aggregation
- Data munging techniques
Module 7: Statistics Basics (Duration-11hrs)
- Central Tendency
- Mean
- Median
- Mode
- Skewness
- Normal Distribution
- Probability Basics
- What does mean by probability?
- Types of Probability
- ODDS Ratio?
- Standard Deviation
- Data deviation & distribution
- Variance
- Bias variance Trade off
- Underfitting
- Overfitting
- Distance metrics
- Euclidean Distance
- Manhattan Distance
- Outlier analysis
- What is an Outlier?
- Inter Quartile Range
- Box & whisker plot
- Upper Whisker
- Lower Whisker
- Scatter plot
- Cook’s Distance
- Missing Value treatment
- What is a NA?
- Central Imputation
- KNN imputation
- Dummification
- Correlation
- Pearson correlation
- ositive & Negative correlation
Module 8: Error Metrics (Duration-3hrs)
- Classification
- Confusion Matrix
- Precision
- Recall
- Specificity
- F1 Score
- Regression
- MSE
- RMSE
- MAPE
Module 9: Machine Learning
Supervised Learning (Duration-6hrs)
- Linear Regression
- Linear Equation
- Slope
- Intercept
- R square value
- Logistic regression
- ODDS ratio
- Probability of success
- Probability of failure Bias Variance Tradeoff
- ROC curve
- Bias Variance Tradeoff
Unsupervised Learning (Duration-4hrs)
- K-Means
- K-Means ++
- Hierarchical Clustering
SVM (Duration-2hrs)
- Support Vectors
- Hyperplanes
- 2-D Case
- Linear Hyperplane
SVM Kernal (Duration-2hrs)
- Linear
- Radial
- polynomial
Other Machine Learning algorithms (Duration-10hrs)
- K – Nearest Neighbour
- Naïve Bayes Classifier
- Decision Tree – CART
- Decision Tree – C50
- Random Forest
Module 10: ARTIFICIAL INTELLIGENCE
AI Introduction (Duration-9hrs)
- Perceptron
- Multi-Layer perceptron
- Markov Decision Process
- Logical Agent & First Order Logic
- AL Applications
Module 11: Deep Learning Algorithms (Duration-10hrs)
CNN – Convolutional Neural Network
RNN – Recurrent Neural Network
ANN – Artificial Neural Network
Introduction to NLP (Duration-5hrs)
- Text Pre-processing
- Noise Removal
- Lexicon Normalization
- Lemmatization
- Stemming
- Object Standardization
Text to Features (Feature Engineering) (Duration-5hrs)
- Syntactical Parsing
- Dependency Grammar
- Part of Speech Tagging
- Entity Parsing
- Named Entity Recognition
- Topic Modelling
- N-Grams
- TF – IDF
- Frequency / Density Features
- Word Embedding’s
Tasks of NLP (Duration-2hrs)
- Text Classification
- Text Matching
- Levenshtein Distance
- Phonetic Matching
- Flexible String Matching