DATA SCIENCE TRAINING

Lesson 01 – Data Science & Data Analytics Overview

• Introduction to Artificial Intelligence, Machine Learning, Deep Learning
• Relationship between AI, ML,DL, and Data Science
• Introduction to Data Science

                  What is Data Science

                   Why Data Science

• Components of Data Science
• Applications of data science
• Why Python for Data Science

Lesson 02 – Python Overview & Environment Setup

• Installation of Anaconda Python
• Variables, Standard Data Types, Basic Operators
• Functions, Modules,
• String,
• List, Tuple,
• Dictionary, Set
• List comprehension, Dictionary comprehension. Set Comprehension
• Regular expressions
• Python File I/O
• Database Operations using Sqlite
• Generators, Iterators, Decorators.
• Collections Module
• Object Oriented Python,
  • Class, Object
  • Abstraction, Encapsulation, Inheritance, Polymorphism
• CSS, Web Scraping, Flask web application Framework
• Project on Python  data structures

Part – 02

Lesson 03 – Descriptive Statistics, Distributions and Visualization

• Central Tendency (mean, median, and mode)
• Measures of Variation(Interquartile Range, Variance, Standard Deviation)
• Bar Chart, Histogram, Box whisker plot, Scatter Plot
• Co-variance, Correlation
• Binomial Distribution, Normal Distribution
• Skewness and Kurtosis

Lesson 04 – Inferential Statistics

• Central Limit Theorem
• Z Test, T-Test, P-value
• Hypothesis testing
• Chi-Square, F- Statistic
• Sampling Techniques

Part – 03

Lesson 05 – Mathematical Computing with Python (NumPy)

• Introduction to NumPy, N-D array
• Mathematical Functions of NumPy
• Data types and attributes of Arrays
• Array Indexing and Slicing
• 1-D arrays and N-D arrays
• Array broadcasting

Lesson 06 – Data Manipulation with Pandas

• Introduction to Pandas,
• Understanding Data Frame
• Transforming List, Tuples, and Dictionaries into Data Frame
• Accessing rows and columns, Iteration over Data Frames
•  Pandas joining and merging,
• Pandas Group by, Pivot Table, Binning,
•  Pandas Visualization
•  Data Generation
•  Real-time Case Studies on Data Analysis based on Kaggle

Lesson 07 – Exploratory Data Analysis and Data Visualization

• Univariant analysis
• Bivariant analysis
• MatplotLib

Lesson 08 – Data Preprocessing/Wrangling/Munging Techniques

• Data Imputation (Missing values)
• Data Encoding Techniques (Label Encoding, One hot encoding.)
• Finding Outliers
• Data Normalization, Transformation, Scaling
• Dimensionality Reduction Techniques
• PCA- Principle Component Analysis
• SVD- Singular Value Decomposition
• LDA- Linear Discriminant analysis
• Feature Selection (Importance) and Engendering techniques
• Case Studies on Data Preprocessing techniques

 

Part -04

Lesson 9 – Regression Analysis

• What is regression, Linear regression
• Linear regression, Multiple Regression
• Ridge Regression, Lasso Regression
• Explanation of Statistics
• Evaluation metrics (R-Squre, Adj R-Square, MSE, RMSE)
• Train/Test Split, Hypothesis testing formal way
• Case Studies
• Project on Regression Analysis from Kaggle

Lesson 10 – Classification

• Introduction to Machine Learning
• Naïve Bayes classifier
• Decision Tree classifier
• Support Vector Machines
• KNN classifier
• Logistic Regression
• Support Vector Machines (SVM)
• Ensemble models (Random Forest, Bagging, Boosting)
• Xg boost in depth with Industry cases

• Backpropagation using Neural Networks
• SK Learn ML library using Python
• Case Studies
• Project on Classification Algorithms from Kaggle

Lesson 11 – Model Selection and Evaluation

• Accuracy measurements
• Precision, Recall, Precision-Recall Tread-off
• AUC Score, ROC Curve
• Train/Validation/Test split, K-Fold Cross Validation
• The Problem of Over-fitting (Bias-Variance tread-off)

Lesson 12- Learning Best Practices for Model Evaluation

• Bias, Variance, Overfitting, Underfitting methods
• Pipelining
• Parameter Tuning mechanisms (Grid Search, Random Search)
• Debugging algorithms with learning and validation curves
• Deployment of models into Flask

Part -05

Lesson 13– Association Analysis

• Association Rules & Interesting measures
• Apriori Algorithm
• FP-Growth algorithm
• Case Studies

Lesson 14 – Clustering

• Similarity distance measures
• K-means Clustering
• Hierarchical Clustering
• DB Scan Clustering
• Case Studies

Part -06

Lesson 15 – Natural Language Processing

•  NLP Overview, Applications using NLTK, Text Blob
• Tokenizing, Stop Word Removal, Stemming, Lemmatization, POS Tagging,
• Similarity measures over Text,
•  Vector Space Model, Bag of words,
• transforming text to Numeric using Count Vectorizer
• Text Classification,
• Text Clustering,
• Topic Modelling,
• Model Deployment using NLP
• Word Embeddings, Sentiment Analysis
• Case Studies
• Project on NLP from Kaggle

Part -07

Lesson 16 – Image Processing using CV2

• Image Processing Basics and Computer Vision Library
• Images Operations using Numpy
• Blending and Pasting
• Image Threshold
• Morphological Operations
• Edge detection
• Contour detection
• Feature Marching
• Face detection

Lesson 17- Deep Learning using TensorFlow and Kera’s.

• Introduction to Deep Learning, Neurons, Perceptron, Multilayer Perceptron,
• Forward Propagation, Backward Propagation, Activation Functions,
• Artificial Neural Networks, Case Studies
• Convolution Neural Networks, Case Studies
• Recurrence Neural Networks, Case Studies
• Model Deployment using Deep Learning
• Deep Learning with Text
• Word Embedding
• Transformers using Hugging Faces
  • Encoders and Decoders
  • Attention mechanism

Part -08

Lesson 18- Applications

• Time Series Analysis
• Recommendation Systems (Filter based RS and Collaborative based RS)
• Saving Models, Deployment of models into Flask
• Industrial Case Studies

 

Part -09

Lesson 19- Explainable AI

• Need for Explainable AI in the Industry
• SHAP
• LIME
• Industrial Case Studies

Part -10

 

Lesson 20- Generative AI  using Gemini AI

• Gen AI text
• Gen AI images
• Retrieval-Augmented Generation
• Case Studies on Gen AI and Other

 

Lesson 21- Git hub, Azure/AWS/GCP deployment of various domain projects

 

Features of the course

• Case Study based Teaching
• Industrial persons involve in Industrial case studies
• Interview-based training