Curriculam

• What is Data Science?
• What is Machine Learning?
• What is Deep Learning?
• What is AI?
• Data Analytics & it’s types

• What is Python?
• Why Python?
• Installing Python
• Python IDEs
• Jupyter Notebook Overview

• Python Basic Data types
• Lists
• Slicing
• IF statements
• Loops
• Dictionaries
• Tuples
• Functions
• Array
• Selection by position & Labels

• Pandas
• Numpy
• Sci-kit Learn
• Mat-plot library

• Reading CSV files
• Saving in Python data
• Loading Python data objects
• Writing data to csv file

• Selecting rows/observations
• Rounding Number
• Selecting columns/fields
• Merging data
• Data aggregation
• Data munging techniques

• Central Tendency

  Mean

  Median

  Mode

  Skewness

  Normal Distribution

• Probability Basics

  What does mean by probability?

  Types of Probability

  ODDS Ratio?

• Standard Deviation

  Data deviation & distribution

  Variance

• Bias variance Trade off

  Underfitting

  Overfitting

• Distance metrics

  Euclidean Distance

  Manhattan Distance

• Outlier analysis

  What is an Outlier?

  Inter Quartile Range

  Box & whisker plot

  Upper Whisker

  Lower Whisker

  Scatter plot

  Cook’s Distance

• Missing Value treatment

  What is a NA?

  Central Imputation

  KNN imputation

  Dummification

• Correlation

  Pearson correlation

  Positive & Negative correlation

• Classification
• Confusion Matrix
• Precision
• Recall
• Specificity
• F1 Score
• Regression
• MSE
• RMSE
• MAPE

• Supervised Learning

  Linear Regression

  Linear Equation

  Slope

  Intercept

  R square value

  Logistic regression

  ODDS ratio

  Probability of success

  Probability of failure Bias Variance Tradeoff

  ROC curve

  Bias Variance Tradeoff

• Unsupervised Learning

  K-Means

  K-Means ++

  Hierarchical Clustering

• SVM

  Support Vectors

  Hyperplanes

  2-D Case

  Linear Hyperplane

• SVM Kernal

  Linear

  Radial

  polynomial

• Other Machine Learning algorithms

  K – Nearest Neighbour

  Naïve Bayes Classifier

  Decision Tree – CART

  Decision Tree – C50

  Random Forest

• AI Introduction

  Perceptron

  Multi-Layer perceptron

  Markov Decision Process

  Logical Agent & First Order Logic

  AL Applications

• CNN – Convolutional Neural Network
• RNN – Recurrent Neural Network
• ANN – Artificial Neural Network
• Introduction to NLP

  Text Pre-processing

  Noise Removal

  Lexicon Normalization

  Lemmatization

  Stemming

  Object Standardization

• Text to Features (Feature Engineering)

  Syntactical Parsing

  Dependency Grammar

  Part of Speech Tagging

  Entity Parsing

  Named Entity Recognition

  Topic Modelling

  N-Grams

  TF – IDF

  Frequency / Density Features

  Word Embedding’s

• Tasks of NLP

  Text Classification

  Text Matching

  Levenshtein Distance

  Phonetic Matching

  Flexible String Matching

• Enhance report design
• Add report objects to enhance design
• Format data and report objects
• Add a background image to a report
• Add row numbers to a report

• Create multi-lingual reports
• Highlight exceptional data
• Show and hide data
• Conditionally render objects in reports

• Analysis Studio Fundamentals
• Nest Data in Crosstabs in Analysis Studio
• Create Analysis with Multiple filter
• Reusable analysis
• Build Advanced Crosstabs in Analysis Studio
• Focus with Filters in Analysis Studio
• Creating reports from cubes
• Drill down and drill up

• Introduction to Event Studio
• Create an agent
• Add tasks to an agent
• Run an agent through its lifecycle
• Schedule an agent

• Introdcution to Dashboards
• Create Dashboard
• Types of Filter-Value, Slider and advanced filter
• Overview of RSS Feed and web Page
• Content Pane
• Create Widgets
• Sort, Filter and Calculate data
• Hands on

• Overview of Business Intelligence Advance level
• Create Different types of Reports
• Reporting Styles and filters
• Create dashboard objects
• Summarize data and Create Calculations
• Dispatcher and Services

• All Services
• Properties of Services