Paper 12

(E1) BIG DATA ANALYTICS

This paper is intended to equip the candidate with the knowledge, skills and attitude that will enable him/her to apply relevant Big Data analytical tools and techniques to generate insight from large volumes of data for informed decision making.

On completion, a candidate should be able to

Explore the Big Data analytics and machine learning approaches
Utilize exploratory and predictive data analysis techniques for massive datasets
Apply Big Data analytics and related technologies and present visual outcomes for decision making
Deploy applications that leverage Big Data analytics for sustainable impact.
Evaluate ethical or public policy concerns and emerging issues in Big Data analytics

Content

1
Introduction to Big Data Analytics
1. 1.1
  Concepts of Big Data analytics
2. 1.2
  Artificial intelligence
3. 1.3
  Challenges and opportunities
4. 1.4
  Applications of Big Data analytics
5. 1.5
  Introduction to Python programming language
2
Mathematics for Big Data Analytics
1. 2.1
  Linear algebra
2. 2.2
  Tensor and matrix implementation
3. 2.3
  Transposition and matrix multiplication
4. 2.4
  Eigenvalue and eigenvector
5. 2.5
  Determinant and singular value decomposition
6. 2.6
  Probability theory
7. 2.7
  Least squares
8. 2.8
  Gradient descent implementation
9. 2.9
  Implementation with python
3
Exploration of massive datasets in Python
1. 3.1
  Working with Pandas and Dask
2. 3.2
  Predictor and target feature identification
3. 3.3
  Techniques for handling missing values, noise and outliers
4. 3.4
  Feature engineering (Feature transformation and creation)
5. 3.5
  Case study in Data Exploration
4
Social Networks Analysis in Python
1. 4.1
  Introduction to Graph Theory
2. 4.2
  Modelling networks
3. 4.3
  Network metrics
4. 4.4
  Network library (34)
5. 4.5
  Graph databases
6. 4.6
  Neo4j
7. 4.7
  Case studies in Social Networks Analysis
5
Machine Learning Pipeline
1. 5.1
  Big Data collection
2. 5.2
  Big Data pre-processing
3. 5.3
  Feature extraction and selection (labelling and dimensionality reduction)
4. 5.4
  Model validation
5. 5.5
  Data visualization
6. 5.6
  Using Python's NumPy, SciPy, Matplotlib and Scikit-learn
6
Unsupervised (Clustering) Machine Learning in Python
1. 6.1
  Dimension reduction techniques
2. 6.2
  K-means clustering
3. 6.3
  K-Nearest Neighbour
4. 6.4
  Hierarchical clustering (Divisive and Agglomerative)
5. 6.5
  Case studies with Python
7
Supervised (Classification) Machine Learning in Python
1. 7.1
  Linear regression
2. 7.2
  Logistic regression
3. 7.3
  Decision trees, rules and random forests
4. 7.4
  K-nearest neighbour algorithm
5. 7.5
  Support vector Machines
6. 7.6
  Naive Bayes
7. 7.7
  Linear discriminant analysis
8. 7.8
  Case studies with Python
8
Associative Rule Mining in Python
1. 8.1
  Overview of association rule mining
2. 8.2
  A Priori algorithm
3. 8.3
  Evaluation of candidate rules
4. 8.4
  Applications of association rules
5. 8.5
  Validation and testing
9
Deep learning implementation in Python
1. 9.1
  Concepts of deep learning
2. 9.2
  Introductions to biological neurons
3. 9.3
  Artificial neural network
4. 9.4
  Network topology
5. 9.5
  Convolutional neural networks and architecture
6. 9.6
  Activation functions
7. 9.7
  Recurrent neural networks
8. 9.8
  Case studies with Python (35)
10
Natural language processing in Python
1. 10.1
  Sentiment analysis
2. 10.2
  Topic modelling
3. 10.3
  Text analytics
4. 10.4
  Social media analytics
5. 10.5
  Recommender systems
6. 10.6
  Case studies with Python ]
11
Big Data visualization in Python
1. 11.1
  Line plots, bar, pie and donut charts
2. 11.2
  Scatter plots and biplots
3. 11.3
  Word clouds
4. 11.4
  Kernel density estimation and Histogram plots
5. 11.5
  Box and whisker plots
6. 11.6
  Correlation matrix and heatmaps
7. 11.7
  Clustering visualization
12
The law and ethics in Big Data analytics
1. 12.1
  Principles of data processing
2. 12.2
  Professional ethics and legal frameworks for the data profession