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Course Details

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