Steinbeis University - Data Science Certification Course Training 

offered by ExcelR

Module 1 - Statistical Analysis

Data Types

Measure Of central tendency

Measures of Dispersion

Graphical Techniques

Skewness & Kurtosis

Box Plot.

Random Variable

Probability

Probability Distribution

Normal Distribution

SND

Expected Value

Sampling Funnel

Sampling Variation

Central Limit Theorem

Confidence interval

 

Module 2 - Hypothesis Testing

Introduction to Hypothesis Testing

Hypothesis Testing ( 2 proportion test, 2 t sample t test)

Anova and Chi Square

 

Module 3 - Linear and Logistic Regression

Principles of Regression

Intro to Simple Linear Regression

Multiple Linear Regression

Logistic Regression

 

Module 4 -EDA

Data Cleaning

Imputation Techniques

Data analysis and Visualization

Scatter Diagram

Correlation Analysis

Transformations

Encoding Methods - OHE, Label Encoders,Outlier detection-Isolation Forest and Calculating the Predictive Power Score (PPS)

 

Module 5 - Unsupervised ML Algorithms

Clustering introduction

Hierarchical clustering

K Means

DBSCAN

PCA

Association Rules

Recommender System

Python Model Deployment

 

Module 6 - Machine Learning Models

Regression Tasks / Classification Tasks

Decision Tree

KNN

Support Vector Machines

Feature Engineering (Tree based methods, RFE,PCA)

Model Validation Methods (train-test,CV,Shuffle CV, and Accuracy methods)

Lasso and Ridge Regressions

 

Module 7 - Neural Network

ANN

Optimization Algorithm(Gradient descent)

Stochastic gradient descent(intro)

Back Propagation method

Introduction to CNN

 

Module 8 - Bagging and Boosting

Bagging and Random Forest

Boosting

XGBM

LGBM

 

Module 9 - Text Mining

Introduction to Text Mining

VSM

Intro to word embeddings

Word clouds and Document Similarity using cosine similarity

Named Entity Recognition

Text classification using Naive Bayes

Emotion Mining

 

Module 10 - Forecasting

Introduction to Time Series

Level

Trend and Seasonality

Strategy

Scatter plot

Lag plot

ACF

Principles of Visualization

Naive forecasts

Forecasting Error and it metrics

Model Based Approaches

AR Model for errors

Data driven approaches

MA

Exponential Smoothing

ARIMA