Data Analytics Training

Data Analytics:

Data analytics refers to the process of examining large data sets to uncover hidden patterns, correlations, trends, and other insights. It involves applying various techniques and algorithms to understand and make sense of the data, which can then be used to make informed business decisions, solve problems, optimize processes, and more.

Key components of data analytics include data collection, data cleansing, data transformation, data modeling, and data visualization. These activities are often supported by specialized software tools and systems that facilitate the analysis of data from diverse sources such as databases, spreadsheets, sensors, social media platforms, and more.

In essence, data analytics enables organizations and individuals to derive valuable information from data, leading to improved decision-making and strategic planning.

Data analytics is used for several important reasons across various fields and industries:

1. Decision Making: Data analytics provides valuable insights that can support decision-making processes. By analyzing historical data and current trends, organizations can make informed decisions that are based on evidence rather than intuition alone.
2. Business Optimization: Analyzing data allows businesses to optimize their operations and processes. This can lead to cost reductions, improved efficiency, and better resource allocation.
3. Understanding Customers: Data analytics helps organizations understand their customers better by analyzing their behaviors, preferences, and needs. This information can be used to tailor products, services, and marketing strategies to better meet customer expectations.
4. Predictive Capabilities: By analyzing historical data, organizations can develop predictive models that forecast future trends and outcomes. This enables proactive planning and risk management.
5. Improving Performance: Data analytics can be used to monitor and evaluate performance metrics across various aspects of an organization, such as sales performance, production efficiency, and employee productivity. This allows for continuous improvement efforts.
6. Innovation and Research: Data analytics plays a crucial role in research and development by identifying new opportunities, trends, and areas for innovation. It helps researchers and innovators stay ahead of the curve.
7. Risk Management: Analyzing data can help organizations identify potential risks and vulnerabilities. This allows for proactive measures to be taken to mitigate risks and enhance security.
8. Competitive Advantage: Organizations that effectively use data analytics can gain a competitive advantage in their industry. By leveraging data-driven insights, they can innovate faster, respond to market changes more effectively, and meet customer demands more precisely.

Where to use Data Analytics?

Data analytics can be applied across various domains and industries to derive insights and drive decision-making. Here are some specific areas where data analytics is commonly used:

1. Business and Operations: Data analytics helps businesses optimize their operations, improve efficiency, and reduce costs. It can be used for supply chain management, inventory optimization, logistics planning, and resource allocation.
2. Marketing and Customer Analytics: Organizations use data analytics to understand customer behaviors, preferences, and trends. This information helps in customer segmentation, personalized marketing campaigns, customer acquisition, and retention strategies.
3. Finance and Banking: In the financial sector, data analytics is used for fraud detection, risk management, credit scoring, investment analysis, and compliance monitoring.
4. Healthcare and Pharmaceuticals: Data analytics plays a crucial role in healthcare for patient monitoring, personalized medicine, disease prediction, clinical trials optimization, and healthcare resource management.
5. Telecommunications and Media: Companies in telecommunications and media use data analytics for customer churn prediction, content recommendation systems, audience segmentation, and advertising effectiveness analysis.
6. Government and Public Sector: Governments use data analytics for policy planning, public health monitoring, crime analysis, traffic management, and resource allocation.
7. Manufacturing and Industrial Processes: Data analytics is used in manufacturing for predictive maintenance of equipment, quality control, production optimization, and supply chain management.
8. Education: Educational institutions use data analytics for student performance analysis, personalized learning paths, predictive analytics for student outcomes, and resource allocation.
9. Sports and Entertainment: Data analytics is increasingly used in sports for performance analysis, player recruitment, fan engagement strategies, and game strategy optimization.
10. Research and Development: In various fields such as scientific research, engineering, and technology development, data analytics helps in data-driven discovery, innovation, and process optimization.

Business Statistics

Module 1: Introduction to Statistical Analysis

• Counting, Probability, and Probability Distributions
• Sampling Distributions
• Estimation and Hypothesis Testing
• Scatter Diagram
• Anova and Chi-square
• Imputation Techniques
• Data Cleaning
• Correlation and Regression

Module 2: Introduction to Data Analytics

• Data Analytics Overview
• Importance of Data Analytics
• Types of Data Analytics
• Descriptive Analytics
• Diagnostic Analytics
• Predictive Analytics
• Prescriptive Analytics
• Benefits of Data Analytics
• Data Visualization for Decision Making
• Data Types, Measure Of central tendency, Measures of Dispersion
• Graphical Techniques, Skewness & Kurtosis, Box Plot
• Descriptive Stats
• Sampling Funnel, Sampling Variation, Central Limit Theorem, Confidence interval

Excel

Module 1: Basic Excel

• Excel tutorial
• Text to Columns
• Concatenate
• The Concatenate Function
• The Right Function with Concatenation
• Absolute Cell References
• Data Validation
• Time and Date Calculations
• Conditional Formatting
• Exploring Styles and Clearing Formatting
• Using Conditional Formatting to Hide Cells
• Using the IF Function
• Changing the “Value if false” Condition to Text
• Pivot Tables
• Creating a Pivot Table
• Specifying PivotTable Data
• Changing a PivotTables Calculation
• Filtering and Sorting a PivotTable
• Creating a PivotChart
• Grouping Items
• Updating a PivotTable
• Formatting a PivotTable
• Using Slicers
• Charts
• Creating a Simple Chart
• Charting Non-Adjacent Cells
• Creating a Chart Using the Chart Wizard
• Modifying Charts
• Moving an Embedded Chart
• Sizing an Embedded Chart
• Changing the Chart Type
• Chart Types
• Changing the Way Data is Displayed
• Moving the Legend
• Formatting Charts
• Adding Chart Items
• Formatting All Text
• Formatting and Aligning Numbers
• Formatting the Plot Area
• Formatting Data Markers
• Pie Charts
• Creating a Pie Chart
• Moving the Pie Chart to its Own Sheet
• Adding Data Labels
• Exploding a Slice of a Pie Chart
• Data Analysis − Overview
• types of Data Analysis
• Data Analysis Process
• Working with Range Names
• Copying Name using Formula Autocomplete
• Range Name Syntax Rules
• Creating Range Names
• Creating Names for Constants
• Managing Names
• Scope of a Name
• Editing Names
• Applying Names
• Using Names in a Formula
• Viewing Names in a Workbook
• Copying Formulas with Names
• Difference between Tables and Ranges
• Create Table
• Table Name
• Managing Names in a Table
• Table Headers replacing Column Letters
• Propagation of a Formula in a Table
• Resize Table
• Remove Duplicates
• Convert to Range
• Table Style Options
• Table Styles
• Cleaning Data with Text Functions
• Removing Unwanted Characters from Text
• Extracting Data Values from Text
• Formatting Data with Text Functions

Advance Excel

• Date Formats
• Conditional Formatting
• Sorting
• Filtering
• Lookup Functions
• Pivoting

SQL

• Introduction to Oracle Database
• Retrieve Data using the SQL SELECT Statement
• Learn to Restrict and Sort Data
• Usage of Single-Row Functions to Customize Output
• Invoke Conversion Functions and Conditional Expressions
• Aggregate Data Using the Group Functions
• Display Data from Multiple Tables Using Joins
• Use Sub-Queries to Solve Queries
• The SET Operators
• Data Manipulation Statements
• Use of DDL Statements to Create and Manage Tables
• Other Schema Objects
• Control User Access
• Management of Schema Objects
• Manage Objects with Data Dictionary Views
• Manipulate Large Data Sets
• Data Management in Different Time Zones
• Retrieve Data Using Sub-queries
• Regular Expression Support

Tableau

Module 1: Tableau Course Material

• 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

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

Module 4: Learn Tableau Advanced Reports

• 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

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

Module 6: Learn Tableau Dashboards

• 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

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

Power BI

Module 1: Introduction to Power BI

• Get Started with Power BI
• Overview: Power BI concepts
• Sign up for Power BI
• Overview: Power BI data sources
• Connect to a SaaS solution
• Upload a local CSV file
• Connect to Excel data that can be refreshed
• Connect to a sample
• Create a Report with Visualizations
• Explore the Power BI portal

Module 2: Viz and Tiles

• Overview: Visualizations
• Using visualizations
• Create a new report
• Create and arrange visualizations
• Format a visualization
• Create chart visualizations
• Use text, map, and gauge visualizations and save a report
• Use a slicer to filter visualizations
• Sort, copy, and paste visualizations
• Download and use a custom visual from the gallery

Module 3: Reports and Dashboards 

• Modify and Print a Report
• Rename and delete report pages
• Add a filter to a page or report
• Set visualization interactions
• Print a report page
• Send a report to PowerPoint
• Create a Dashboard
• Create and manage dashboards
• Pin a report tile to a dashboard
• Pin a live report page to a dashboard
• Pin a tile from another dashboard
• Pin an Excel element to a dashboard
• Manage pinned elements in Excel
• Add a tile to a dashboard
• Build a dashboard with Quick Insights
• Set a Featured (default) dashboard
• Ask Questions about Your Data
• Ask a question with Power BI Q&A
• Tweak your dataset for Q&A
• Enable Cortana for Power BI

Module 4: Publishing Workbooks and Workspace

• Share Data with Colleagues and Others
• Publish a report to the web
• Manage published reports
• Share a dashboard
• Create an app workspace and add users
• Use an app workspace
• Publish an app
• Create a QR code to share a tile
• Embed a report in SharePoint Online

Module 5: Other Power BI Components and Table Relationship

• Use Power BI Mobile Apps
• Get Power BI for mobile
• View reports and dashboards in the iPad app
• Use workspaces in the mobile app
• Sharing from Power BI Mobile
• Use Power BI Desktop
• Install and launch Power BI Desktop
• Get data
• Reduce data
• Transform data
• Get Power BI Desktop data with the Power BI service
• Export a report from Power BI service to Desktop

Module 6: DAX functions

• New Dax functions
• Date and time functions
• Time intelligence functions
• Filter functions
• Information functions
• Logical functions
• Math & trig functions
• Parent and child functions
• Text functions

Python Basics

To understand basic concepts of Python because they are used in programming to provide comments.

• Print the values.
• The print statement
• Comments
• Python Data Structures & Data Types
• String Operations in Python
• Simple Input & Output
• Simple Output Formatting
• Deep copy
• Shallow copy
• Operators in python