Course outline for Data Analytics

Goal:

The goal of Data Analytics course is to examine large amounts of data to uncover hidden patterns, correlations and other insights.

Audience:

This course is designed for any one willing to make career in Data Analytics .

Pre-requisites:

Any Graduate or Post-Graduate having affinity with Data, Information, Knowledge and Wisdom

Duration:

45 hours

Course Structure

Data Analytics using Excel

INTRODUCTION TO EXCEL

• Introduction to Excel Environment
• Explanation about data and calculation in Excel
• Basics of Formulas
• Formatting and Conditional Formatting for Data Visualization
• Understanding about Data Validation, Data Filter, Advance Filter and Data Sorting

ADVANCED FORMULAS IN EXCEL FOR DATA ANALYSIS

• Vlookup and Hlookup
• Sumif, Sumifs, Averageif and Averageifs
• If and Nestedif
• Text Functions
• Pivot Table
• Data Analysis with Pivot Table

ADVANCED TOOLS IN EXCEL FOR DATA ANALYSIS

• Understanding of Name Ranges and Usage in Data Modelling
• Statistical and Mathematical Functions
• Subtotal Analysis
• What if Analysis
• Understanding of Table concept for Excel Power User
• Basics of Macro (Macro Recording and Execution)

CHARTS AND DASHBOARD

• Basics of Charts creation and interpretation
• Trends and Scenarios using Charts
• Advanced Charting Techniques for Data Visualization
• Pareto Analysis, Thermometer, Panel and Step Chart
• Overview and Decision making on Dashboards
• Designing Dashboard using Charts and Form Controls
• Dynamic Chart and Dynamic Dashboard

Data Analytics using Tableau

GETTING STARTED WITH TABLEAU

• What is Tableau
• Architecture of Tableau
• Introduction of Data Source and Connecting to Data
• Understanding of Tableau Interface
• Dimensions and Measures in Tableau
• Data Types in Tableau
• Tour of Shelves & Marks Card
• Building Basic Views

BUILDING VIEWS OR REPORTS IN TABLEAU

• Understanding and Creating Cross tab & Tabular charts
• Creating Bar Charts and Stacked Bars
• Understanding and Creating Scatter Plots
• Creating Line Graphs with Date & Without Date
• Individual Axis, Dual Axis and Combination Chart
• Trend Lines, Reference Lines and Forecasting
• Filters and Context Filters
• Understanding Sets (In/Out Sets, Combined Sets)
• Creating Bins/Histograms

CALCULATED FIELDS AND TABLE CALCULATIONS

• Working with Disaggregate data and Aggregate data
• Basic Functions like String, Date and Numbers etc.
• Working with Logical Conditions
• Scope and Directions in Table Calculation
• Calculation of Percent of Total (Running and Cumulative Calculations)

ADVANCED DASHBOARDS and DATA ANALYSIS IN TABLEAU

• Create What-if-Analysis
• Dynamic Dimension and Measure Selection (Display Options)
• Usage of Parameters in Calculated Fields, Reference Lines and Filters
• Combining Multiple Reports\Visualizations to Create Dashboard
• Customizing Dashboards
• Interactive Reports by using Actions for Filters, URL, Highlight etc.
• Working with Data Extracts
• Working with Data Extracts
• Data Blending

Data Analytics and Stats using System R

GETTING STARTED With R

• Overview of R, R data types and objects, reading and writing data
• Control structures, functions, scoping rules, dates and times
• Loop functions, debugging tools
• Simulation, code profiling
• Graphics and Charts

Stats and Data Analysis with R

• Introduction to big data analytics: big data overview, data pre-processing
• Concepts of supervised and unsupervised learning
• Basic statistics: mean, median, standard deviation, variance, correlation, covariance
• Linear regression: simple linear regression, introduction to multiple linear regression
• Classification: logistic regression and decision trees
• Clustering: K-means, K-medoids, Hierarchical clustering, X-means
• Evaluation and validation: cross-validation, assessing the statistical significance of data mining results