Data Science Master Program

Statistics Essentials for Analytics

All the topics in the following section will explain the basis of what it is, which scenario you want to use, What math behind it, How to implement with an analytic tool, what inferences you are getting from the final result.

• Understanding the Data
• Probability and its Uses
• Statistical Inference
• Data Clustering
• Testing the Data
• Regression Modelling

Data Science With R

Module 1: Introduction to R (Duration: 2Hrs)

• What is R?
• Why R?
• Installing R
• R environment
• How to get help in R
• R Studio Overview

Module 2: R Basics (Duration: 5Hrs)

• Environment setup
• Data Types
• Variables
• Vectors
• Lists
• Matrix
• Array
• Factors
• Data Frames
• Loops
• Packages
• Functions
• In-Built Data sets

Module 3: R Packages (Duration: 3Hrs)

• DMwR
• Dplyr/plyr
• Caret
• Lubridate
• E1071
• Cluster/fpc
• table
• Stats/utils
• Ggplot/ggplot2
• Glmnet

Module 4: Machine Learning using R (Duration: 10Hrs)

• Linear Regression
• Logistic Regression
• K-Means
• K-Means++
• Hierarchical Clustering – Agglomerative
• CART
• c5.0
• Random forest
• Naïve Bayes

Data Science With Python

Module 1: Introduction to Data Science (Duration: 1Hr)

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

Module 2: Introduction to Python (Duration: 1Hr)

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

Module 3: Python Basics (Duration: 5Hrs)

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

Module 4: Python Packages (Duration: 2Hrs)

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

Module 5: Importing data (Duration: 1Hr)

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

Module 6: Manipulating Data (Duration: 1Hr)

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

Module 7: Statistics Basics (Duration: 11Hrs)

• Central Tendency
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• 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 treatments
  • What is a NA?
  • Central Imputation
  • KNN imputation
  • Dummification
• Correlation
  • Pearson correlation
  • Positive & Negative correlation

Module 8: Error Metrics (Duration: 3Hrs)

• Classification
  • Confusion Matrix
  • Precision
  • Recall
  • Specificity
  • F1 Score

• Regression
  • MSE
  • RMSE
  • MAPE

Machine Learning

Module 9: Supervised Learning (Duration: 6Hrs)

• Linear Regression
  • Linear Equation
  • Slope
  • Intercept
  • R square value

• Logistic regression
  • ODDS ratio
  • Probability of success
  • Probability of failure
  • ROC curve
  • Bias Variance Tradeoff

Module 10: Unsupervised Learning (Duration: 4Hrs)

• K-Means
• K-Means ++
• Hierarchical Clustering

Module 11: Other Machine Learning algorithms (Duration: 10Hrs)

• K – Nearest Neighbour
• Naïve Bayes Classifier
• Decision Tree – CART
• Decision Tree – C50
• Random Forest

Tableau

Module 1: Tableau Course Material (Duration: 5Hrs)

• Start Page
• Show Me
• Connecting to Excel Files
• Connecting to Text Files
• Connect to Microsoft SQL Server
• Connecting to Microsoft Analysis Services
• Creating and Removing Hierarchies
• Bins
• Joining Tables
• Data Blending

Module 2: Learn Tableau Basic Reports (Duration: 5Hrs)

• Parameters
• Grouping Example 1
• Grouping Example 2
• Edit Groups
• Set
• Combined Sets
• Creating a First Report
• Data Labels
• Create Folders
• Sorting Data
• Add Totals, Sub Totals and Grand Totals to Report

Module 3: Learn Tableau Charts (Duration: 4Hrs)

• Area Chart
• Bar Chart
• Box Plot
• Bubble Chart
• Bump Chart
• Bullet Graph
• Circle Views
• Dual Combination Chart
• Dual Lines Chart
• Funnel Chart
• Traditional Funnel Charts
• Gantt Chart
• Grouped Bar or Side by Side Bars Chart
• Heatmap
• Highlight Table
• Histogram
• Cumulative Histogram
• Line Chart
• Lollipop Chart
• Pareto Chart
• Pie Chart
• Scatter Plot
• Stacked Bar Chart
• Text Label
• Tree Map
• Word Cloud
• Waterfall Chart

Module 4: Learn Tableau Advanced Reports (Duration: 6Hrs)

• Dual Axis Reports
• Blended Axis
• Individual Axis
• Add Reference Lines
• Reference Bands
• Reference Distributions
• Basic Maps
• Symbol Map
• Use Google Maps
• Mapbox Maps as a Background Map
• WMS Server Map as a Background Map

Module 5: Learn Tableau Calculations & Filters (Duration: 6Hrs)

• Calculated Fields
• Basic Approach to Calculate Rank
• Advanced Approach to Calculate Rank
• Calculating Running Total
• Filters Introduction
• Quick Filters
• Filters on Dimensions
• Conditional Filters
• Top and Bottom Filters
• Filters on Measures
• Context Filters
• Slicing Fliters
• Data Source Filters
• Extract Filters

Module 6: Learn Tableau Dashboards (Duration :4Hrs)

• Create a Dashboard
• Format Dashboard Layout
• Create a Device Preview of a Dashboard
• Create Filters on Dashboard
• Dashboard Objects
• Create a Story

Module 7: Server (Duration: 5Hrs)

• Tableau online.
• Overview of Tableau Server.
• Publishing Tableau objects and scheduling/subscription.