- May 10, 2020
- 0 Comment
- By ananth4599

**DATA SCIENCE (AI, ML, DL) Course Content**

- Introduction to Data Science

a. What is data science?

How is data science different from BI and Reporting?

b. What is the difference between AI, Data Science, Machine Learning, Deep Learning

c. Job Landscape and Preparation Time

d. Who are data scientists?

What skill sets are required?

e. What is the day to day job of Data Scientist

What kind of projects did they work on?

f. End to End Data Science Project Life Cycle - Business Statistics

a. Data types

Continuous variables

Ordinal Variables

Categorical variables

Time Series

Miscellaneous

Common Data Science Terminology

b. Descriptive statistics

Basics concepts of probability

Frequentist versus Bayesian Probability

Axioms of probability theory,

Permutations and combinations

Conditional and marginal probability

Joint Probability

Bayes Theorem

Probability Mass Function and Probability Density Function

Cumulative Mass Function and Cumulative Density Function

c. Central Tendencies

Mean

Median

Mode

Spread

Variance

Standard Deviation

Effects on central tendencies after transformations

Quartile Analysis

Implementation of central tendencies using python

Box Plots for outlier identification

Drawing Box plots using python

d. Sampling

DATA SCIENCE (AI, ML, DL)

Need for Sampling?

Different types of Sampling

Simple random sampling

Systematic sampling

Stratified Sampling

Implementation of sampling techniques using python

e. Data distributions

Normal Distribution

Binomial Distribution

Binomial Approximated to Normal

Implementation of distributions using python

f. Inferential statistics

Why inferential statistics?

Z score calculation

Defining p value and implementations using python

Inferring from sample to population

Sampling distribution of sample means

g. Hypothesis testing

Confidence Interval

Testing the hypothesis

Type I error

Type II error

Null and alternate hypothesis

Reject or acceptance criterion

- Python for Data Science

a. Understanding the reason of Python’s popularity

b. Basics of Python: Operations, loops, functions, dictionaries

c. Numpy – creating arrays, reading, writing, manipulation techniques

d. Ground-up for Deep-Learning - Exploratory Data Analysis with Python

DATA SCIENCE (AI, ML, DL)

a. Getting to understand structure of Matplotlib

b. Configuring grid, ticks.

c. text, color map, markers, widths with Matplotlib

d. configuring axes, grid,

e. hist, scatterplots

f. bar charts

g. multiple plots

h. 3D plots

i. Correlation matrix plotting

- Data Munging with Python

a. Introduction to pandas

b. Data loading with Pandas

c. Data types with python

d. Descriptive Statistics with Pandas

e. Quartile analysis with Pandas

f. Sort, Merge, join with Pandas

g. Indexing and Slicing with pandas

h. Pivot table, Aggregate and cross tab with pandas

i. Apply function for parallel processing with Python

j. Cleaning Data with python

k. Determining correlation

l. Handling missing values

m. Plotting with Pandas

n. Time series with Pandas - Introduction to Artificial Intelligence

a. Dealing Prediction problem

b. Forecasting for industry

c. Optimization in logistics

d. Segmentation in customer analytics

e. Supervised learning

f. Unsupervised Learning

g. Optimization

h. Types of AI : Statistical Modelling, Machine Learning, Deep Learning,

Optimization, Natural Language Processing, Computer vision, Speech

Processing, Robotics - Artificial Intelligence I – Statistical Modelling

a. Linear Regression

Assumptions

Model development and interpretation

Sum of least squares

DATA SCIENCE (AI, ML, DL)

Model validation – tests to validate assumptions

Multiple linear regression

Disadvantages of linear models

b. Logistic Regression

Need for logistic regression

Logit link function

Maximum likelihood estimation

Model development and interpretation

Confusion Matrix – error measurement

ROC curve

Measuring sensitivity and specificity

Advantages and disadvantages of logistic regression models

c. Time series analysis – Forecasting

- Simple moving averages
- Exponential smoothing
- Time series decomposition
- ARIMA

d. Model validation and deployment

RMSE – Root Mean squared error

MAPE – Mean Average Percentage Error

Confusion matrix and Misclassification rate

Area under the curve (AUC) , ROC curve

- Artificial Intelligence II – Machine Learning

a. Supervised Learning

1.

Decision trees and Random Forest

C5.0 - Classification and Regression trees(CART)
- Process of tree building
- Entropy and Gini Index
- Problem of over fitting
- Pruning a tree back
- Trees for Prediction (Linear) – example
- Tress for classification models – example

9.

1.

Advantages of tree-based models?

Association Rule Mining

Rules generation from decision trees, - Apriori algorithm

3.

1.

Support, confidence and lift measures

Support Vector Machines

Linear learning machines - SVM case for linearly separable data
- Kernel space

Neural Networks

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