Course Content
- Recap of Demo
- Introduction to Types of Analytics
- Project life cycle
- An introduction to our E learning platform
- Data Types
- Measure Of central tendency
- Measures of Dispersion
- Graphical Techniques
- Skewness & Kurtosis
- Box Plot
- R Studio
- Descriptive Stats in R
- Python (Installation and basic commands) and Libraries
- Jupyter note book
- Set up Github
- Descriptive Stats in Python
- Random Variable
- Probability
- Probility Distribution
- Normal Distribution
- SND
- Expected Value
- Sampling Funnel
- Sampling Variation
- CLT
- Confidence interval
- Visualization
- Data Cleaningr
- Imputation Techniques
- Scatter Plot
- Correlation analysis
- Transformations
- Principles of Regression
- Multiple Linear Regression
- Multiple Logistic Regression
- Confusion matrix
- Receiver operating characteristics curve (ROC curve)
- Supervised vs Unsupervised learning
- Data Mining Process
- Hierarchical Clustering / Agglomerative Clustering
- Visualization of clustering algorithm using Dendrogram
- PCA and tSNE
- Why dimension reduction
- Advantages of PCA
- Calculation of PCA weights
- 2D Visualization using Principal components
- Basics of Matrix algebra
- What is Market Basket / Affinity Analysis
- Measure of association
- Support
- Confidence
- Lift Ratio
- Apriori Algorithm
- User-based collaborative filtering
- Measure of distance / similarity between users
- Driver for recommendation
- Computation reduction techniques
- Search based methods / Item to item collaborative filtering
- Vulnerability of recommender systems
- Workflow from data to deployment
- Data nuances
- Mindsets of modelling
- Elements of Classification Tree – Root node, Child Node, Leaf Node, etc.
- Greedy algorithm
- Measure of Entropy
- Attribute selection using Information Gain
- Implementation of Decision tree using C5.0 and Sklearn libraries
- Data types Identification and probability
- Expected values, Measures of central tendencies
- Skewness and Kurtosis & Boxplot
- Practice Mean, Median, Varience, Standard Deviation and Graphical representations in R
- Creating Python Objects
- Resume Preparation
- Interview Support