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Understanding Relational Databases, Tables, Rows & Columns Introduction to SQL as the language used to manage and query relational databases Relational Database Management Systems (RDBMS): Overview of popular RDBMS like MySQL, PostgreSQL, SQL Server, and SQLit Basic Database Terminology: Database, table, column, row, primary key, foreign key Setting Up SQL Environment: Installing and setting up MySQL, PostgreSQL, or using SQL clients like DBeaver SELECT Statement: Basic syntax of the SELECT query Selecting Specific Columns: Retrieving data from specific columns using SELECT column_name Selecting All Columns: Using SELECT * to retrieve all columns from a table Filtering Data: Using the WHERE clause to filter records based on conditions (e.g., equality, inequalities, text matching) Operators: Using comparison operators (Equal, Less than, Greater than, Less than Equal, Greater than Equal) and logical operators (AND, OR, NOT) Wildcard Characters: Using LIKE for pattern matching and % or _ as wildcards in queries. Sorting Data: Using ORDER BY to sort results by one or more columns in ascending (ASC) or descending (DESC) order Limiting Results: Using LIMIT (or TOP in some RDBMS) to restrict the number of rows returned by a query Filtering with BETWEEN, IN, and IS NULL: Using BETWEEN to filter a range of values, IN for matching specific values, and IS NULL for handling null values. Basic Aggregate Functions: 
COUNT(): Counting rowsSUM(): Calculating the sum of a columnAVG(): Calculating the average of a columnMIN() and MAX(): Finding the minimum and maximum values in a column. GROUP BY Clause: Grouping data by one or more columns to perform aggregation on subsets of data HAVING Clause: Filtering groups with conditions (e.g., filtering results after GROUP BY) Introduction to Joins: What are joins, and why do we use them in relational databases? INNER JOIN: Retrieving matching rows from two or more tables. LEFT JOIN (OUTER JOIN): Retrieving all rows from the left table and matching rows from the right table RIGHT JOIN (OUTER JOIN): Retrieving all rows from the right table and matching rows from the left table FULL OUTER JOIN: Retrieving all rows from both tables SELF JOIN: Joining a table with itself to compare rows Using Aliases: Giving temporary names (aliases) to tables and columns for easier reference What is a Subquery?: Introduction to subqueries and how they can be used in SELECT, INSERT, UPDATE, or DELETE statements Subqueries in WHERE Clause: Using subqueries to filter results based on another query Subqueries in FROM Clause: Using subqueries to create a virtual table in the FROM clause Correlated Subqueries: Subqueries that reference columns from the outer query INSERT INTO: Adding new records to a table UPDATE: Modifying existing records in a table. DELETE: Removing rows from a table Using WHERE with Update/Delete: Applying conditions to modify or delete specific rows. Transactions: Introduction to COMMIT, ROLLBACK, and SAVEPOINT to ensure data integrity What are NULL Values?: Definition and significance of NULL in databases IS NULL and IS NOT NULL: Identifying NULL values in queries COALESCE Function: Replacing NULL values with a default value Handling NULL in Aggregate Functions: How SQL handles NULL in COUNT(), SUM(), AVG(), etc. Window Functions: Using ROW_NUMBER(), RANK(), DENSE_RANK(), NTILE() for advanced analytics tasks. CASE Statement: Conditional logic in SQL for dynamic calculations String Functions: Using functions like CONCAT(), LENGTH(), SUBSTRING(), TRIM() to manipulate text data Date Functions: Working with date and time values using functions like CURRENT_DATE(), DATE_ADD(), DATEDIFF(), and DATE_FORMAT() Indexing: Understanding indexes and how they speed up queries Query Execution Plan: Using the EXPLAIN command to understand and optimize query performance Joins vs Subqueries: When to use joins instead of subqueries for better performance Avoiding Common Pitfalls: Best practices for writing efficient SQL queries (e.g., avoiding unnecessary DISTINCT, and reducing complexity) Understand the Excel interface and basic functions.Overview of Excel and its components (Workbooks, Worksheets, Cells)Basic Navigation and Interface: Rows, Columns, and RibbonsIntroduction to Excel Data Types: Numbers, Text, DatesBasic Formatting: Font, Alignment, Colors, Cell StylesData Entry, Editing, and Basic Navigation (shortcuts)Introduction to Formula Bar and Simple CalculationsBasic Cell Referencing: Relative vs. Absolute referencesSaving, Exporting, and Sharing Workbook Learn to clean and organize data for analysis.Removing DuplicatesSorting and Filtering DataUsing Text Functions: TRIM(), UPPER(), LOWER(), LEFT(), RIGHT(), MID(), CONCATENATE()Handling Missing Data: IFERROR(), ISBLANK()Text to Columns (delimited and fixed width)Date and Time Functions: DATE(), DAY(), MONTH(), YEAR()Data Validation: Drop-down Lists, Error MessagesIntroduction to Power Query (for basic transformations like merging, appending, filtering) Master essential Excel functions for summarizing and analyzing dataAggregation Functions: SUM(), AVERAGE(), COUNT(), MIN(), MAX()Logical Functions: IF(), AND(), OR()Lookup Functions: VLOOKUP(), HLOOKUP(), INDEX(), MATCH()Conditional Aggregation: SUMIF(), COUNTIF(), AVERAGEIF()Text Analysis Functions: LEN(), FIND(), SEARCH()Statistical Functions: STDEV(), VAR(), MEDIAN()Error Handling: IFERROR(), ISERROR() Learn how to use pivot tables and pivot charts for summarizing large datasetsIntroduction to Pivot Tables: Creating and manipulating Pivot TablesGrouping Data by Categories: Using rows, columns, values, and filtersApplying Filters and Sorting in Pivot TablesCalculating with Pivot Tables: Sums, Averages, CountsUsing Calculated Fields in Pivot TablesPivot Charts: Creating and formatting pivot chartsSlicers and Timeline Filters for interactive analysis Gain proficiency in creating meaningful charts and graphs to present insights.Creating Basic Charts: Bar, Line, Column, Pie, and Scatter PlotsCustomizing Charts: Titles, Axes, Legends, Data Labels, ColorsAdvanced Charts: Waterfall, Funnel, Histogram, Box and WhiskerConditional Formatting: Using color scales, data bars, and icon setsSparklines for visualizing trends in small data rangesCreating Dynamic Dashboards with Multiple ChartsAdvanced Chart Features: Secondary Axes, Trendlines Learn advanced Excel functions for complex data analysis tasks.Array Formulas: Introduction to array functions and using CTRL + SHIFT + ENTERUsing SUMPRODUCT() for complex calculationsAdvanced Data Filtering: Using Advanced Filter, Criteria Range Learn how to use Power Query for advanced data transformation tasksIntroduction to Power Query: Navigating the Power Query EditorImporting Data from Various Sources: Files, Databases, Web, APIsData Transformation: Remove, Sort, Merge, Split, Unpivot, PivotApplying Multiple Queries and Combining DataAutomating Data Preparation: Save and load transformed data to Excel or Power BIManaging and Cleaning Data: Removing blanks, duplicates, and errors Build data models and perform advanced calculations using Power PivotIntroduction to Power Pivot: Adding and managing data modelsCreating Relationships Between Tables in Power PivotUsing DAX (Data Analysis Expressions) for Calculations:
Measures and Calculated Columns Time Intelligence Functions: YTD(), QTD(), MTD() Advanced DAX Functions: CALCULATE(), FILTER(), ALL()Managing Large Datasets: Importing and analyzing millions of rowsUsing Power Pivot with Pivot Tables Learn how to collaborate with others and share insights efficientlyExcel Collaboration Features: Share and co-author workbooksProtecting Data: Using password protection, hiding sheets, and cellsData Versioning and Change Tracking: Use Track Changes and CommentsSharing Dashboards: Publish Excel files to SharePoint or Microsoft TeamsExporting Data and Reports: Convert to PDF, CSV, or integrate with Power BICreating Interactive Dashboards: Using Form Controls and Slicers for dynamic reports Overview of TableauIntroduction to Tableau and its componentsTableau vs other BI tools (Excel, Power BI, etc.)Applications of Tableau in data analytics Installing TableauDownloading and installing Tableau DesktopOverview of Tableau Desktop interface (data pane, shelves, report view) Understanding Tableau WorkflowConnecting to different data sources (Excel, CSV, SQL)Overview of Tableau's data import processUnderstanding dimensions and measures Connecting to DataConnecting to common data sources (Excel, Google Sheets, SQL, and web data connectors)Difference between live and extract connectionsImporting data into Tableau Cleaning and Transforming DataData transformation using basic techniques in TableauRenaming fields, changing data types, handling null valuesFiltering and aggregating data for better insights Using Tableau Prep (Optional for Advanced Users)Introduction to Tableau Prep for data shapingSimple data cleaning tasks: filtering, pivoting, and aggregating Introduction to Visualization TypesOverview of Tableau's visualization options: bar charts, line charts, pie charts, scatter plots, etcSelecting the right visualization for the dataCreating a simple bar chart and line chart Customizing VisualizationsFormatting charts (color, font, size)Modifying axes, labels, and tooltipsAdding titles and legends Introduction to Data AggregationUnderstanding aggregation in Tableau (sum, average, count, etc.)Using basic filters to refine data visualizationsFiltering data using dimensions and measures Using Dual-Axis and Combo ChartsCreating dual-axis charts (combining two different chart types)Creating combo charts for complex analysis (e.g., line and bar chart) Geospatial Analysis (Maps)Visualizing geographic data (latitude and longitude)Creating maps using Tableau’s built-in map optionsCustomizing map types and map layers Working with Dates and Time SeriesHandling time-based data: continuous vs discrete datesCreating time series visualizations and trend linesUsing date functions in Tableau for trend analysis Calculations in TableauIntroduction to calculated fields in TableauBasic calculations (SUM, AVG, COUNT)Using calculated fields to derive new insights Trend Analysis and ForecastingCreating trend lines and analyzing trendsUsing Tableau’s built-in forecasting models for predicting data patternsImplementing reference lines and bands for trend analysis Segmentation and GroupingCreating groups and sets for data segmentationUsing bins to group continuous data (e.g., creating age ranges)Working with manual and automatic grouping Introduction to DashboardsCombining multiple sheets into a dashboardOrganizing a dashboard using containers (tiled vs. floating)Adding text, images, and web content to dashboards Adding Interactivity to DashboardsUsing filters, actions (highlight, URL), and drill-throughsCreating dashboard filters and drop-down lists for interactivityAdding parameters for user-driven changes in the dashboard Designing Dashboards for Data StorytellingEffective design principles for dashboardsUsing color, layout, and typography to tell a clear storyBest practices for dashboard design (simplicity, clarity) Publishing Reports to Tableau Server or Tableau OnlineHow to publish workbooks and dashboards to Tableau Server or Tableau OnlineSetting permissions and access control for viewersSharing interactive dashboards online Exporting ReportsExporting visualizations as images or PDFCreating static reports for offline sharingSending reports by email or printing directly from Tableau Data Refresh and SchedulingSetting up automated data refresh for live connections and extractsUnderstanding Tableau's data refresh schedulesManaging and troubleshooting data refresh issues Data Preparation Best PracticesImportance of clean and well-structured data for analysisTips for optimizing data connections and handling large datasetsUsing data extracts to improve performance Visualization Best PracticesSelecting the right chart for the dataAvoiding misleading visualizations and ensuring clarityUsing color effectively and avoiding clutter Performance OptimizationTips for improving Tableau dashboard performanceWorking with large datasets and optimizing queriesUsing aggregations and extracts to enhance speed Overview of Power BI Why Power BI is popular for data analytics Power BI vs other BI tools (Excel, Tableau, etc.) Power BI ComponentsPower BI DesktopPower BI ServicePower BI MobilePower BI Gateway Power BI Architecture and WorkflowConnecting to Data SourcesTransforming data (ETL)Visualizing Data Sharing and collaborating with reports/dashboards Installing and Setting up Power BI Desktop Overview of the Power BI Desktop interface (Ribbon, Data Pane, Report View, etc.) Importing Data into Power BIConnecting to different data sources (Excel, CSV, SQL Server, etc.)Understanding different file types supported by Power BILoading data into Power BI Data Transformation with Power QueryIntroduction to Power Query EditorCleaning and transforming data (Remove Columns, Filter Rows, Change Data Types, etc.)Using M code for advanced transformations Creating Data RelationshipsUnderstanding relationships (One-to-One, One-to-Many, Many-to-Many)Building relationships between tablesRelationship types and cardinality Introduction to DAXWhat is DAX and why it’s importantBasic DAX functions (SUM, AVERAGE, COUNT, etc.)Creating calculated columns and measures Time Intelligence in Power BIWorking with date tablesUsing built-in DAX time intelligence functions (YTD, QTD, MTD, etc.) Introduction to Power BI Visuals Overview of available visualizations (Bar chart, Line chart, Pie chart, etc.) Choosing the right visual for your data Formatting and Customizing VisualizationsFormatting charts (colors, labels, titles, axes, etc.)Using slicers and filters for interactivityCustom visuals and how to import them Building Interactive DashboardsCombining multiple visuals into a dashboardUsing drill-through and drill-down featuresAdding bookmarks and buttons for navigation Advanced DAX FunctionsUnderstanding CALCULATE and FILTER functionsUsing SUMX, AVERAGEX, and other row context functionsUsing variables in DAX for better performance and readability Optimizing Data ModelsStar schema vs Snowflake schema in data modellingHandling large datasets and performance tuningManaging many-to-many relationships Working with Hierarchies and GroupsCreating hierarchies in Power BI (Date hierarchies, custom hierarchies)Grouping data dynamically (bins, custom grouping) Introduction to Power BI ServiceOverview of PowerBI Service(Cloud-based)Setting up workspaces and appsCollaboration and sharing options Publishing Reports from Power BI DesktopPublishing to Power BI ServiceUnderstanding datasets and reports in the Service Creating Dashboards and SharingPinning reports to dashboards Sharing dashboards with othersData refresh and scheduling Power BI Mobile AppInstalling and setting up the Power BI Mobile appAccessing reports and dashboards on mobile devicesInteracting with visuals on mobile Power BI Gateway for On-Premises DataUnderstanding the Power BI GatewaySetting up the gateway for connecting to on-premises data sourcesManaging data refresh schedules Best Practices for Data VisualizationUnderstanding how to tell a story with dataChoosing the right chart types for different scenariosKeeping your reports simple and focused Optimizing Performance Optimization in Power BIdata models for performanceReducing report load timesUsing query folding and DirectQuery mode Securing Power BI ReportsRow-level security (RLS)Setting permissions in Power BI ServiceProtecting sensitive data in reports Overview and importance of Python in Data Analytics Setting up Python via Anaconda or directly from python.org. Python IDEs: Jupyter Notebooks, VS Code, and PyCharm Basic Syntax: Writing and running Python scripts Python Data Types: Integers, floats, strings, and Booleans Variables and Constants: Assigning values, naming conventions Basic Operators: Arithmetic, comparison, logical operators Conditional Statements: if, else, elif for decision-making Loops: for and while loops for repeated execution List Comprehensions: Efficient looping and creating lists. Functions: Defining and calling functions, passing arguments, and returning values Lambda Functions: Anonymous functions for simple tasks Error Handling: Using try and except to handle errors in code Lists: Creating, accessing, modifying, and removing elements Tuples: Immutable data structure, indexing, and unpacking Dictionaries: Key-value pairs, accessing, adding, and deleting items Sets: Unordered collection, adding/removing elements, set operations String Manipulation: Basic string operations like concatenation, slicing, and formatting Reading and Writing CSV Files: Using Python’s csv module for simple I/O Reading JSON Data: Using json module to parse JSON files Handling Excel Files: Using openpyxl or pandas for reading and writing Excel files Basic File Handling: Using open(), read(), write(), and close() methods Introduction to Pandas: Installation and basic usage. DataFrames: Creating, accessing, and modifying DataFrames Series: Understanding and working with Series objects Loading Data: Importing CSV, Excel, and JSON files into Pandas DataFrames Data Inspection: Exploring data with head(), tail(), info(), describe() Filtering Data: Using conditions to filter rows from DataFrames Data Selection: Selecting columns, rows, and subsets using indexing and .loc[] or .iloc[]. Handling Missing Data: Identifying and handling missing values with isna(), dropna(), and fillna() Removing Duplicates: Using drop_duplicates() to remove redundant rows Data Type Conversion: Converting data types (e.g., astype() to change column types) Renaming Columns: Renaming columns for consistency using rename() String Manipulation: Using string functions like str.replace(), str.split(), and regular expressions GroupBy: Using groupby() to group data based on columns Aggregation Functions: Applying functions like sum(), mean(), count(), min(), max() to grouped data Pivot Tables: Creating pivot tables with pivot_table() for summarizing data Multi-level Indexing: Working with hierarchical indexing in DataFrames Applying Functions: Using apply() and agg() for custom aggregation Introduction to Matplotlib: Installing and using matplotlib.pyplot for plotting Line and Bar Charts: Creating basic line plots and bar charts Histograms and Pie Charts: Visualizing distribution and proportions Scatter Plots: Plotting relationships between two variables Customizing Plots: Adding titles, labels, legends, and grid lines Subplots: Creating multiple plots in a single figure Descriptive Statistics: Calculating mean, median, mode, variance, and standard deviation Correlation: Understanding and calculating correlation between variables using pandas and numpy Probability Distributions: Working with normal and binomial distributions using scipy.stats Random Sampling: Generating random numbers and creating random samples using numpy Introduction to Power Apps and its role in app development Types of Power Apps: Canvas Apps, Model-driven Apps, and Portals Overview of Power Platform (Power BI, Power Automate, Power Virtual Agents) Power Apps Interface and Setup Overview of Power Apps StudioUnderstanding the design environment: Screens, controls, and propertiesCreating your first Power App (Canvas App) Power Apps and Data IntegrationConnecting Power Apps to data sources (SharePoint, Excel, SQL Server, Dynamics 365, Common Data Service)Understanding data connections and tablesImporting and managing data in Power Apps Power Apps Data Sources and ConnectionsConnecting to common data sources (Excel, SharePoint, SQL Server, Dataverse)Exploring data connectors and using built-in connectorsIntroduction to Dataverse (Common Data Service) and its role in data management Creating and Managing Data TablesCreating custom tables in DataverseImporting and managing data in Dataverse and external sourcesUnderstanding and utilizing relationships in data (one-to-many, many-to-many) Introduction to Data Models and EntitiesUnderstanding entities, fields, and data typesWorking with structured data models in Power AppsUsing Power Apps to define business rules for data validation and transformation Introduction to Canvas AppsCanvas Apps: What they are and how they differ from Model-driven appsUsing the Power Apps Studio to build a Canvas AppDesigning the app layout and user interface (UI) for data input and analysis Adding Data to Canvas AppsConnecting Canvas Apps to data sources (SharePoint, Excel, SQL Server)Using Galleries, Forms, and Data Tables to display and interact with dataManaging records and implementing CRUD (Create, Read, Update, Delete) operations Interactive Elements for Data AnalysisUsing controls (Buttons, Dropdowns, Sliders, and Radio buttons) for interactionImplementing filters, searches, and sorting to analyze data dynamicallyCreating simple calculations, aggregations, and totals Adding Data VisualizationsIntroduction to data visualization controls (Charts, Pie charts, Bar charts, etc.)Connecting charts and visualizations to data sourcesCustomizing visualizations for better clarity and insight Using Power BI with Power AppsEmbedding Power BI reports and dashboards within Power AppsIntegrating Power BI tiles into Power Apps for interactive analyticsSynchronizing data between Power Apps and Power BI for real-time analytics Dynamic Reporting in Power AppsImplementing reporting features (e.g., sales reports, performance dashboards)Using filtering and parameterization to create dynamic reportsExporting data from Power Apps to Excel or other formats for detailed analysis Introduction to Model-driven AppsDifferences between Canvas Apps and Model-driven AppsUsing Dataverse to create a structured model for data-centric appsDesigning model-driven apps with views, forms, and charts Creating and Managing Forms and ViewsSetting up forms for data entry and managementCreating and customizing views for data analysis and reportingWorking with business rules and workflows in Model-driven Apps Using Charts and Dashboards in Model-driven AppsCreating and using charts for data visualizationSetting up interactive dashboards to display data insightsIntegrating data analytics features into Model-driven apps for decision-making Introduction to Power AutomateOverview of Power Automate and its role in data automationSetting up automated workflows (flows) between apps and servicesTriggering actions based on specific data events or conditions Automating Data Collection and ProcessingCreating automated data flows to gather data from various sourcesUsing Power Automate to clean, aggregate, and transform dataSending notifications, approvals, and alerts based on data events Scheduling Reports and AlertsAutomating the scheduling of reports and dashboards in Power AppsCreating alerts and notifications based on data changesIntegrating Power Automate with Power Apps for real-time analytics Understanding Power Apps SecurityUser roles, permissions, and access control in Power AppsConfiguring security roles in Dataverse (Common Data Service)Implementing app-level security (who can view, edit, or delete data) Data Governance in Power AppsBest practices for data governance in Power AppsMonitoring data access and usageData compliance and privacy considerations Managing Data Loss Prevention (DLP) PoliciesSetting up DLP policies in Power PlatformEnsuring secure data sharing between Power Apps, Power Automate, and other toolsAuditing and reporting for compliance Sharing and Collaborating on Power AppsSharing apps with users and configuring permissionsCollaborating with team members on app developmentBest practices for sharing apps within your organization Publishing Power Apps to the CloudDeploying Power Apps for cloud-based access (Power Apps mobile app, web access)Managing app versions and updatesTroubleshooting common publishing and access issues Managing and Monitoring Power AppsMonitoring app usage and performanceAnalyzing app performance metrics (user engagement, load times)Using analytics and insights to improve app performance Best Practices for Data Integration and ConnectivityOptimizing data sources and managing connectionsBest practices for large data sets and performance optimizationEnsuring smooth data flow and updates in real-time applications Design and Usability Best PracticesEnsuring a user-friendly and intuitive app interfaceDesign tips for making apps visually appealing and easy to navigateEnhancing data presentation through visuals and interactive elements Performance Optimization for Data-Driven AppsOptimizing app performance for large data volumesReducing load times and improving app responsivenessReducing load times and improving app responsiveness Overview of Power Automate and its role in data automation Understanding how Power Automate integrates with the Microsoft Power Platform (Power Apps, Power BI, Power Virtual Agents) Introduction to automation scenarios in data analytics Power Automate Interface and SetupNavigating the Power Automate interfaceSetting up a Power Automate account and environmentIntroduction to flows (automated, instant, scheduled, and business process flows) Power Automate Use Cases in Data AnalyticsReal-world examples of how Power Automate enhances data analysispAutomating data collection, integration, and reportingIntegrating Power Automate with external data sources (Excel, SharePoint, SQL, etc.) Types of Flows in Power AutomateAutomated flows: Automatically triggered based on conditions (e.g., new data added, or scheduled time)Instant flows: Manually triggered by users (e.g., on button click)Scheduled flows: Runs at a specified time or frequency (e.g., hourly, daily)Business process flows: Structured process flows based on business requirements Using Triggers for Data Analysis AutomationDefining triggers for flows (e.g., when a new file is uploaded to SharePoint, new row added in Excel)uConnecting triggers to data sources (e.g., SharePoint, SQL Server, Outlook, Dynamics 365)Using condition-based triggers for specific data events Working with Conditional Logic and VariablesCreating conditions to check data (e.g., if a value in a spreadsheet exceeds a threshold)Using expressions and variables in flowsImplementing decision trees and branching logic in workflows Connecting Power Automate to Data SourcesOverview of connectors in Power Automate (SharePoint, Excel, SQL Server, Dynamics 365, and more)Setting up data connectors to external systems and databasesAuthenticating data sources securely in Power Automate Data Integration: Moving and Managing DataUsing connectors to integrate data between systems (e.g., moving data from Excel to SharePoint)Data mapping and transforming data in Power AutomateUsing the "Apply to each" action for iterating over data Data Operations and ActionsUsing built-in actions for data manipulation (e.g., filtering, sorting, updating, and deleting data)Creating custom actions and data operations for complex workflowsHandling complex data with loops and arrays Automating Data CollectionSetting up flows to automatically collect data from multiple sources (Excel, SharePoint, SQL)Importing and aggregating data from external APIs or web servicesUsing Power Automate to send data from external forms or surveys to a central location Data Transformation and AggregationUsing Power Automate to clean and process raw dataApplying transformations (e.g., calculating totals, averages, aggregating sales data)Automating data summarization and categorization for analysis Automating Reporting and NotificationsSetting up automated reports (e.g., daily, weekly, monthly reports)Automatically sending email summaries or alerts based on data analysis (e.g., when data exceeds thresholds)sCreating notifications for stakeholders based on data insights Power Automate and Power BI OverviewOverview of Power BI and its role in data analyticsUsing Power Automate to trigger Power BI actions (e.g., refresh datasets, update dashboards)Integrating Power Automate with Power BI to trigger workflows based on report data Automating Power BI Report GenerationCreating flows to automate the generation and distribution of Power BI reportsAutomating report generation for scheduled or on-demand updatesSending automated notifications and alerts based on Power BI data insights Using Power Automate for Real-time Data AnalysisSetting up flows to trigger real-time data updates in Power BI dashboardsAutomating data refresh in Power BI datasets with Power AutomateUsing Power Automate to integrate Power BI with other applications for enhanced analytics Sharing flows with others for collaboration Managing permissions for shared flows Organizing and maintaining flows in environments Monitoring flow performance and activity logs Debugging and troubleshooting errors in flows Setting up notifications for flow failures and errors Best practices for optimizing flow performance (e.g., minimizing calls to external services) Reducing unnecessary flow triggers and actions Handling large data sets efficiently in Power Automate Securing data within Power Automate workflows Understanding the security roles and permissions for flows Authenticating and securing data connectors Implementing data loss prevention (DLP) policies Ensuring compliance with organizational policies when automating data workflows Auditing and monitoring Power Automate usage Setting up guidelines for flow creation and management Managing flow lifecycle and updates Ensuring that flows adhere to organizational standards and practices Best Practices for Building Efficient FlowsStreamlining flow design for better performanceUsing conditional logic and error handling effectivelyAvoiding common flow pitfalls and inefficiencies Optimizing Data FlowsMinimizing API calls and reducing flow runtimeHandling large datasets efficientlyLeveraging Power Automate’s built-in connectors for performance optimization Data Integration Best PracticesCreating robust data integration workflows across systemsUsing flow templates for faster automation deploymentEnsuring data accuracy and consistency during automation
There are no prerequisites for taking the online Data Analytics course. Basic knowledge of data analysis, statistics, and probability are beneficial to take Data Analytics online courses.
Absolutely, Greens technologies provide you with the 100% placement assistance. We teach our students by conducting group discussions and mock interviews. Apart from it, we also provide soft skill support to boost the confidence level of the students.
Unquestionably, online training programs are available as well as accessible. You as a candidate have the entire liberty to choose the timings that suit you. Thus, it is highly convenient and flexible.
We have hired experts who use an extensive set of online techniques and tools for making online training experience satisfying. Our instructors will set a time that is convenient for the students. In an online setting, the students can engage, view, analyze queries related to presentations, and engage with learning resources. You have to submit all assignments online within a specified time period.
The DATA ANALYTICS professionals earn an average salary between $60000 to $330000 per annum in the Australia.
Skilled data analysts are some of the most important professionals in the world. Because the demand is so large, and the supply of people who can truly do this job well is so finite, data analysts get huge salaries and excellent perks, even at beginner-level.
*Defining data, analyzing results using statistical techniques.
*Establishing and implementing data analysis, data gathering systems, and other methods that develop analytical efficiency and quality.
*Getting data from primary or secondary data sources and managing databases.
It entirely depends upon each individual's efforts, preparation strategy, and time invested. However, it will take nearly 1 month if an aspirant has an elementary knowledge and background of SQL. It will take around 2 to 3 months if an aspirant is a fresher.
Green's Technology offered an outstanding Data Science course. The instructors were highly knowledgeable, simplifying complex concepts effectively. Engaging in hands-on projects greatly enhanced my confidence. I received exceptional support from the mentor, Godwin. I wholeheartedly recommend it to anyone eager to explore Data Science!.....
Enrolling in Greens technology web development course was a decision I don't regret. The curriculum was comprehensive, covering essential topics like HTML, CSS, JavaScript, and GitHub. Building my own website was a satisfying conclusion to the course. This experience has equipped me with the skills and confidence for a future in web development.
I usually don't write reviews. I chose Greens Technologies through a friend. I Attended DevOps and AWS courses at Greens Technology.The trainer was exceptionally helpful in clarifying concepts, making it the best training center. The emphasis on practical, live projects rather than just theory was commendable. Highly recommended.
I took courses in Networking and Azure SQL Database. My instructor, Dhivakar, was really knowledgeable and the best. He kept the class interested and made sure we understood everything in each module. I want to thank Dhivakar for his great support during the learning process.
