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Description

# DATA SCIENCE WITH PYTHON COURSE CONTENT

Introduction to Data Science

Data science introduction

Data science toolkit

Job outlook

Prerequisite, Target audience

Data science project lifecycle – CRISP-DM

Model

Basics of Statistics

Statistics concepts

Random variable

Type of Random variables

Central Tendencies – Mean, Mode, Median

Probability, Probability Distribution of

Random variables, PMF, PDF, CDF

Type of RV – Nominal, Ordinal, Interval, Ratio:

Variance, Standard Deviation

Normal Distribution, Standard Normal

Distribution

Binomial Distribution

Poisson Distribution

Sampling

Inferential Statistics

Sampling Distribution

Central Limit Theorem

Simulation

Null and Alternative Hypothesis

Hypothesis Testing

1 tail test and 2 tail test, type 1 and type error

z test & t-test

Python Programming for Data Science (Lab)

Introduction to Python, Anaconda & Spyder

Installation & Configuration

Data Structures in Python

-List

-Tuples

-Array in NumPy

-Matrices

-Data frame in Pandas

Control Structure & Functions – If- Else, For, Loop, While loop

Slicing, dicing & filter operations

Applied Statistics in Python (Lab)

Normal distribution

Simulation

Hypothesis testing

Other statistical concepts using Python

Graphics and Data Visualization, Exploratory Data Analysis in Python(Lab)

Graphics and Data Visualization libraries in python

-Plotly

-Matplotlib

-Seaborn

-Other useful packages/functions in Python

Exploratory Data Analysis Exercise in Python

Machine Learning Concepts

Introduction to machine learning

Supervised and Unsupervised ML

Parametric/Non-parametric Machine Learning

Algorithms

Machine Learning Models

– Linear Regression

– Logistic Regression

– Classification & KNN

– Decision trees

– Random Forest

– Clustering – K Means & hierarchical clustering

– Time Series Analysis

– ARMA Models

– Support vector Machine

Model Validation/Cross-validation techniques

parameter tuning

Model evaluation metrics, MSE, RMSE, R

Confusion Matrix

Bias and Variance

Underfitting, Over Fitting

Real-World Data Science & Machine Case Studies in Python (Lab)

ML Case Studies on

– Regression

– Classification

– Decision Tree

– Random Forest

– Clustering

– Time Series Analysis