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 TendencyMean Median Mode Skewness Normal Distribution
• Probability BasicsWhat does mean by probability? Types of Probability ODDS Ratio?
• Standard DeviationData deviation & distribution Variance
• Bias variance Trade offUnderfitting Overfitting
• Distance metricsEuclidean Distance Manhattan Distance
• Outlier analysisWhat is an Outlier? Inter Quartile Range Box & whisker plot Upper Whisker Lower Whisker Scatter plot Cook’s Distance
• Missing Value treatmentWhat is a NA? Central Imputation KNN imputation Dummification
• CorrelationPearson correlation Positive & Negative correlation

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

• Supervised LearningLinear 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 LearningK-Means K-Means ++ Hierarchical Clustering
• SVMSupport Vectors Hyperplanes 2-D Case Linear Hyperplane
• SVM KernalLinear Radial polynomial
• Other Machine Learning algorithmsK – Nearest Neighbour Naïve Bayes Classifier Decision Tree – CART Decision Tree – C50 Random Forest

• AI IntroductionPerceptron 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 NLPText 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 NLPText 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