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Getting Started
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01 - What is analytics
2:00
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Preview
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02 - Application of Analytics in HR
5:00
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03 - Analytics in the Employee life cycle
6:00
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04 - Introduction to Big Data
5:00
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05 - Analytics Value Escalator
6:00
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06 - Data types in a Structured dataset
9:00
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07 - An Example
4:00
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08 - What is Artificial Intelligence
4:00
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09 - Machine Learning and its Applications
6:00
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10 - Way to Dashboarding
9:00
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11 - Sample metrics for Dashboarding
10:00
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12 - Sample issue use case Attrition Analysis
4:00
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13 - Heads up!
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14 - Installation of Tableau Public
2:00
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15 - Business Scenario
2:00
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16 - Dataset description
5:00
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17 - Loading the dataset into Tableau Public
4:00
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18 - Building a Geo Map
7:00
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19 - Saving the Tableau Project
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20 - Stacked bar chart using hierarchies
7:00
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21 - Creating a Bubble plot
4:00
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22 - Creating a Pie Chart
4:00
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23 - Creating an Area chart
4:00
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24 - Creating a Treemap
3:00
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25 - Creating a side-by-side circle chart
6:00
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26 - Building the Dashboard - Part 1
8:00
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27 - Building the Dashboard - Part 2
9:00
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28 - Interactions between the two Dashboards
5:00
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29 - Way to Predictive Modeling
5:00
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30 - What do we mean by a model
4:00
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31 - Sample issue use case Candidate success profile
10:00
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32 - Translating Business model to Statistical Model
5:00
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33 - Heads up!
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34 - Dataset description
4:00
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35 - Creating an account on Microsoft Azure
2:00
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36 - Loading the dataset into MS Azure
4:00
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37 - Data Preparation using MS Azure Edit Metadata
9:00
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38 - Outlier TREATMENT
17:00
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39 - missing values and feature selection
7:00
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40 - Linear Regression Intuition
4:00
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41 - Linear Regression using MS Azure
5:00
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42 - Linear Regression using MS Azure - Part 2
5:00
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43 - How to Predict for new observation
3:00
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44 - Decision Tree Intuition
3:00
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45 - Decision tree using MS Azure
8:00
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46 - Neural Network Intuition
6:00
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47 - Neural Network using MS Azure
3:00
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48 - Classification using Decision Tree MS Azure
6:00
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49 - Classification using Neural Network MS Azure
3:00
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50 - Logistic Regression Intuition
5:00
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51 - Evaluation Metrics
5:00
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52 - Classification using Logistic Regression MS Azure
3:00
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53 - Clustering using K-means Algorithm Intuition
3:00
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54 - Clustering using MS Azure - Part 1
12:00
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55 - Clustering using MS Azure - Part 2
6:00
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