Introduction to Data Science:
- What is DS?
- Why is it needed? , Worth the hype?
- What are major fields under DS?
- A few industry application examples, Future Demand & requirement of DS, Different categories of job options in DS.
An overview of different stages under Data Analysis
- Data Summarization/Descriptive Analytics, Data Interpretation, Extraction of Pattern
- Data Preprocessing: Why? How? What?
- Data Modeling
- Cross Validation, Improve performance accuracy
- Introduction to Machine Learning:
- Few examples of Practical Applications,Future Demand & requirement of ML
- Supervised Learning
- Decision Trees
- Random Forest
- Navie Bayes, Bayesian networks, Generative vs Discriminative
- KNN algorithm
- Support Vector Machine
- Ensemble Modeling
- Morkov Models
- Unsupervised Learning
- Arules, FP Trees
- Recommendation Systems: Collaborative Filtering, SVD
- Clustering Techniques:
- K-means, k-medoids,
- EM clustering etc
- PCA, Error Metrics: Confusion Matrix, Cross Validation etc
- Curse of Dimensionality, Bias vs Variance, Improving Accuracy
- Practical real world examples and solving problems
- Introduction to R programming and working on project(will provide solution and explain)
- Linear Regression: Assumptions, Gradient Descent, model analysis, tweaking parameters. Logistic Regression
- Hypothesis Testing: Null, Alternate Hypothesis, Normal distribution, Type 1,2 errors
- Sampling Techniques: Random, System, Stratified, Quota etc
- Distributions: Poisson, Bernoulli, Binomial, Exponential, Normal etc
- ANOVA, Chisquare, t-test etc.