1. Business Problems and Data Science Solutions: From Business Problems to Data
Mining Tasks, The Data Mining Process, Other Analytics Techniques and
Technologies; CRISP – DM
2. Data Warehousing and Online Analytical Processing: Basic concepts, Data
Warehouse Modeling: Data Cube and OLAP, Data Warehouse Design and Usage,
3. Introduction to Big Data: Characteristics of Big Data, Analytics Flow for Big Data,
intro to Setting up Big Data Stack
4. Demo of an ETL Tool: Singer – How it can be used
5. Introduction to data mining: Basic concepts of data mining, Different types of data
repositories, Data mining functionalities, Concept of interesting patterns, Data mining
tasks, Current trends, Major issues and ethics in data mining
6. Introduction to mining using Python: Introduction to python, Introduction to various
packages as tool, features of related packages
7. Introduction to Statistical Programming Methods with Python: Basic Probability
and Statistics with python and hands on.
8. Data Preprocessing using python and packages: Data cleaning, Data integration and
transformation, Data reduction, Discretization and concept hierarchy generation
9. Association and Correlation Analysis: Basic concepts of frequent pattern and
association rule, frequent itemset generation with Apriori algorithm and FP Growth
algorithm, Rule generation, Applications of Association rules
10. Machine Learning -: Overview of machine learning (ML), Supervised learning, -
Classification, Regression; Unsupervised learning – Clustering, Introduction to Neural
Networks.
11. Deep Learning – Deep Neural Networks, Convolutional neural networks, Recurrent
Neural Network, LSTM and NLP applications
12. Modeling: The Machine Learning Modeling Process, feature selection; feature
engineering, Implementation of Modeling and example, Metrics and Baseline Results,
Aligning the Model with the Business Requirement, Model Validation, Validating
Business Value, Making Predictions and Deployment
13. Classification: Introduction to classification, Introduction to Classification methods,
Basic concepts of binary classification, Bayes theorem and Naive Bayes classifier,
Association based classification, Rule based classifiers, Nearest neighbor classifiers,
Decision Trees, Random Forest,
14. Prediction and Classification using Python: Applying model for prediction,
Bayesian Classification on new imported data, Bayesian Classification on existed
dummy data set, Decision Tree classification on dummy data sets, Practice problems
on classification methods, Applications of classification for web mining
15. Clustering Algorithms and Cluster Analysis: Measures of similarity, K means
partitioning method, k medoids method, CLARANS method, Agglomerative and
divisive clustering hierarchical method, etc.
16. Clustering methods using python: Introduction to clustering, Introduction to
Clustering algorithms, differentiate clustering and classification, K-means clustering,
Hierarchical clustering algorithm,
17. Deep learning Demo/Hands on: Demo/Hands-on with Python package for DL –
Keras/TensorFlow
18. Analysis & Visualizations using python: Data Visualization - Basic Charts,
Multidimensional Visualization, Specialized Visualizations, Intro to PowerBI or Tableau
(any one shall be explained)
19. Web Dashboards with python: Spyre -Web Application Framework for providing a
simple user interface for Python dataprojects.
20. Introduction – Cloud deployment - Flask python microwebserver