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Best Data Analytics with Gen AI & Agentic AITraining Institute in Hyderabad

100% Job-Oriented Training by Industry Experts with Guaranteed Internship and Placement Assistance!

Data Analytics SSSIT

5.0 Created by potrace 1.15, written by Peter Selinger 2001-2017

5.0 Created by potrace 1.15, written by Peter Selinger 2001-2017

4.6 Created by potrace 1.15, written by Peter Selinger 2001-2017

Best Data Analytics with Gen AI & Agentic AI Training Institute in Hyderabad, Kukatpally & KPHB

Best Data Analytics with Gen AI & Agentic AI Training in Hyderabad, Kukatpally and KPHB Data Analytics training in Kukatpally & KPHB, Hyderabad covers topics from scratch to expert level with lots of real time project examples.

SSSIT Computer Education is rated as one of the best Data Analytics with Gen AI & Agentic AI Training Institutes in KPHB, Kukatpally and Hyderabad by trained students. Here Trainers are highly qualified & experienced in delivering Training and Projects delivers the content as per industry expectation from a Data Analyst. The Data Analytics Training class consists of more project oriented scenarios with the Industry Aligned Curriculum

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Industry Aligned Course Curriculum

You will be exposed to the following Data Analytics with Gen AI & Agentic AI training content

  • Introduction about MS Excel Application
  • Basic Parts of MS Excel Window
  • File Menu of MS Excel Application
  • Excel Work Book & Work Sheet
  • Data Clipboard Settings
  • Setting Font Formats
  • Cell Data Alignments
  • Number Format Cells
  • Table Format Cells & Styles
  • Insert Work Sheet Columns & Rows
  • Delete Work Sheet Columns & Rows
  • Adjust Columns Widths & Rows Height
  • Visibility & Organize Work Sheets
  • Data Series Fillings
  • Cells Data Paste Special
  • Insert Hyperlinks
  • Insert Header & Footer
  • Set the Paper Margins, Orientation, Size
  • Set Print Area & Insert Page Breaks
  • Sheet Background & Print Titles
  • Set Scale to Fit of Sheet
  • Print Gridlines & Headings
  • Date ,Time,Mathematical,Financial ,Statistical,Logical ,Text Formulas
  • Name Manager with Define Names
  • Protect Work Sheet
  • Workbook Views
  • Set Sheet Zoom Levels
  • Set Sheet Freeze Panes
  • SUMIF ,COUNTIF , Subtotal ,Database,VLOOKUP,HLOOKUP,Formulas
  • Text To Columns
  • Create Cell Conditional Formatting
  • Create Pivot Tables & Graphical Reports
  • Remove Duplicates
  • Auto Filter Functions
  • Advanced Filter Data Functions
  • Calculating Inputs with Goal Seek
  • Calculating Results with Data Table
  • Subtotal Functions with Category Wise

  • INTRODUCTION to TABLEAU
    • BI Concepts
    • What is TABLEAU? Why Data Visualization?
    • Unique Features compared to Traditional BI Tools
    • TABLEAU Overview & Architecture
    • File Types & Extensions
  • TABLEAU PRODUCTS
    • DESKTOP
    • SERVER
    • PUBLISHER
    • PUBLIC
    • TABLEAU READER
  • DATA CONNECTIONS IN TABLEAU INTERFACE
    • Data Connections in the Tableau Interface
    • Connecting to Tableau Data Server
    • Types of Join
    • When to Use Joining
    • What is Data Blending
    • When to Use Data Blending
    • Joining vs. Blending
    • Creating Data Extracts in Tableau
    • Establishing a Connection and Creating an Extract
    • How does Tableau Optimize Performance
    • Shadow Extracts
    • Prepare your Data for Analysis
    • Key Takeaways
  • ORGANIZING AND SIMPLIFYING DATA
    • Filters, Applying Filters
    • Quick Filters
    • Sorting of Data
    • Creating Combined Fields
    • Creating Groups and Defining Aliases
    • Working with Sets and Combined Sets
    • Drill to Other Levels in a Hierarchy
    • Grand totals and Subtotals
    • Tableau Bins
    • Fixed Sized Bins
    • Variable Sized Bins
    • Creating and using Parameters
    • Exploring Parameter Controls
    • Using parameters for titles, field selections, logic statements, Top X
    • Cross Tabs [Pivot Tables]
    • Page Trials
    • Total and Sub-Total
    • Dual Axis / Multiple Measures
    • Key Takeaways
  • BUILDING CHART TYPES
    • Working with Combined Axis
    • Working with Combination Charts
    • Working with Geocoding and Geographic Mapping
    • Using Scatter Plots
    • Using Text tables and Highlight tables
    • Using Heat Maps
    • Using Histograms
    • Using Pie Charts
    • Using Bullet Charts
  • ADVANCED CHART TYPES
    • Using Pareto Charts
    • Using Waterfall Charts
    • Using Gantt Charts
    • Using Box Plots
    • Using Sparkline Charts
    • Using Density Charts
    • Using KPI Charts
    • Small Multiples Working with aggregate versus disaggregate data
    • What is Market Basket Analysis
    • Performing Market Basket Analysis
  • CALCULATIONS
    • Working with Strings, Date and Arithmetic Calculations
    • Working with Aggregation Options
    • Working with Quick Table Calculations
    • Logic and Conditional Calculations
    • Conditional Filters
    • Advanced Table Calculations
    • Understanding Scope and Direction
    • Calculate on Results of Table Calculations
    • Complex Calculations
    • Addressing and Partitioning
    • Difference From Average
    • Discrete Aggregations
    • Index to Ratios
    • Ranking within higher levels
    • Late Filtering
    • Last Occurrence
    • Working with Dates and Times
    • Continuous versus Discrete Dates
    • Dates and Times
    • Reference Dates
    • Automatic and Custom Split
    • LOD Calculations
  • LOGIC STATEMENTS
    • Formatting
    • Options in Formatting Visualizations
    • Working with Labels and Annotations
    • Effective Use of Titles and Captions
    • Introduction to Visual Best Practices
  • MAPPING
    • Importing and Modifying Custom Geocoding
    • Working with Symbol Map and Filled Map
    • Using Background Image
    • Exploring Geographic Search
    • Perform Pan Zoom Lasso and Radial Selection
    • Working with WMS Server Maps [Web Map Service]
  • STATISTICS
    • Add Reference Lines Bands and Distribution
    • Adding Reference Lines
    • Adding Reference Bands
    • Adding Reference Distribution
    • Working Reference Lines Bands and Forecasting
    • Trend lines and Trend Models
    • Understanding Trend Lines
    • Enabling Trend lines
    • Click Interaction Understanding Trend Models
    • Working with Describe Trend Model Window
    • Working with Trend Lines
    • Statistical Summary Card
    • Perform Drag and Drop Analytics
    • Explore Instant Analysis
    • Summary Stats
    • Cohort Analysis
    • Forecasting
  • DASHBOARD
    • Build Interactive Dashboards
    • Best practices for creating effective dashboards
    • Creating a Dashboard and Importing Sheets
    • Interaction Exploring Dashboard Actions
    • Use of Running Actions
    • Using Dashboard Action
    • How to Share your Reports
    • Exporting your Work
  • FEW MOCK DASHBOARDS PRACTICE

  • Python - Basics
    • Introduction to Python, syntax, data types
    • Operators and expressions
    • Control flow (if-else, nested ifs)
    • Loops – for, while, break, continue
    • Functions and scope
    • Error handling – try, except
    • Strings and string methods
    • Lists and list operations
    • Dictionaries and sets
  • Python for Data
    • Introduction to Numpy – arrays and operations
    • Indexing and slicing arrays
    • Array math and broadcasting
    • Pandas – Series and DataFrames
    • Import/export data (CSV, Excel)
    • Filtering and subsetting
    • GroupBy and aggregation
    • Data cleaning basics (handling NaNs)
    • Merging and joining datasets
  • Data Visualization
    • Introduction to Data Visualization
    • Importance of Data Visualization
    • Types of Data Visualizations
    • Choosing the Right Visualization
    • Best Practices for Effective Visualizations
    • Common Pitfalls in Data Visualization
  • Matplotlib
    • Introduction
    • Pyplot
    • Figure Class
    • Axes Class
    • Setting Limits and Tick Labels
    • Multiple Plots
    • Legend
    • Different Types of Plots
    • Line Graph
    • Bar Chart
    • Histograms
    • Scatter Plot
    • Pie Chart
    • 3D Plots
    • Working with Images
    • Customizing Plots
  • Seaborn
    • catplot() function
    • stripplot() function
    • boxplot() function
    • violinplot() function
    • pointplot() function
    • barplot() function
    • Visualizing statistical relationship with Seaborn relplot() function
    • scatterplot() function
    • regplot() function
    • lmplot() function
    • Seaborn Facetgrid() function
    • Multi-plot grids
    • Statistical Plots
    • Color Palettes
    • Faceting
    • Regression Plots
    • Distribution Plots
    • Categorical Plots
    • Pair Plots

  • MSSQL - SERVER
    • Intro to DBMS & RDBMS
    • Introduction to SQL Server
    • Intoduction to SQL
    • Data Types in SQL
    • Basic of SQL, Types of SQL Statements
    • DDL - create, alter, drop, truncate
    • DML - select, insert, update, delete
    • TCL - commit, rollback
    • DCL - grant, revoke
    • Tables
    • Constraints - NOT NULL, UNIQUE, PRIMARY KEY, FOREIGN KEY, CHECK, DEFAULT, INDEX
    • Identity Column
    • Data Integrity
    • Clauses - selete, top, distinct, from, where, group by, having, order by
    • Joins - inner join, outer join, self join & cross join
    • Sub Queries - Sub queries, corelated subquries
    • Views
    • Functions - scalar, inline & multi valued tabular functions
    • Stored Procedures
    • Triggers - ddl, dml, log on triggers
    • Indexes - clustered, non clustered & unique indexes

  • ML Introduction
    • What is Machine Learning?
    • Types of Machine Learning Methods
    • Classification Problem in General
    • Validation Techniques: CV, OOB
    • Different Types of Metrics for Classification
    • Curse of Dimensionality
    • Feature Transformations
    • Feature Selection
    • Imbalanced Dataset and Its Effect on Classification
    • Bias Variance Tradeoff
  • Important Element of Machine Learning
  • Multiclass Classification
    • One-vs-All
    • Overfitting and Underfitting
    • Error Measures
    • PCA Learning
    • Statistical Learning Approaches
    • Introduction to SKLEARN Framework
  • Data Processing
    • Creating Training and Test Sets, Data Scaling and Normalization
    • Feature Engineering: Adding New Features as Required, Modifying Data
    • Data Cleaning: Treating Missing Values and Outliers
    • Data Wrangling: Encoding, Feature Transformations, Feature Scaling
    • Feature Selection: Filter Methods, Wrapper Methods, Embedded Methods
    • Dimension Reduction: PCA (Sparse PCA, Kernel PCA), Singular Value Decomposition
    • Non-Negative Matrix Factorization
  • Regression
    • Introduction to Regression
    • Mathematics Involved in Regression
    • Regression Algorithms
    • Simple Linear Regression
    • Multiple Linear Regression
    • Polynomial Regression
    • Lasso Regression
    • Ridge Regression
    • Elastic Net Regression
  • Evaluation Metrics for Regression
    • Mean Absolute Error (MAE)
    • Mean Squared Error (MSE)
    • Root Mean Squared Error (RMSE)
    • R2
    • Adjusted R2
  • Classification
    • Introduction
    • K-Nearest Neighbors
    • Logistic Regression
    • Support Vector Machines (Linear SVM)
    • Linear Classification
    • Kernel-Based Classification
    • Non-Linear Examples
    • 2 Features Form Straight Line and 3 Features Form Plane
    • Hyperplane and Support Vectors
    • Controlled Support Vector Machines
    • Support Vector Regression
    • Kernel SVM (Non-Linear SVM)
    • Naive Bayes
    • Decision Trees
    • Random Forest / Bagging
    • AdaBoost
    • Gradient Boost
    • XGBoost
    • Evaluation Metrics for Classification
  • Clustering
    • Introduction
    • K-Means Clustering
    • Finding the Optimal Number of Clusters
    • Optimizing the Inertia
    • Cluster Instability
    • Elbow Method
    • Hierarchical Clustering
    • Agglomerative Clustering
    • DBSCAN Clustering
  • Association Rules
    • Market Basket Analysis
    • Apriori Algorithm
  • Recommendation Engines
    • Collaborative Filtering
    • User-Based Collaborative Filtering
    • Item-Based Collaborative Filtering
  • Time Series and Forecasting
    • What is Time Series Data
    • Different Components of Time Series Data
    • Stationarity of Time Series Data
    • ACF, PACF
    • Time Series Models
    • AR
    • ARMA
    • ARIMA
    • SARIMAX
  • Model Selection and Evaluation
  • Overfitting and Underfitting
    • Bias-Variance Tradeoff
    • Hyperparameter Tuning
    • Joblib and Pickling
  • Others
    • Dummy Variable, One-Hot Encoding
    • GridSearchCV vs RandomizedSearchCV
  • ML Pipeline
  • ML Model Deployment in Flask

  • Artificial Intelligence
    • Introduction to Neural Network
    • Biological and Artificial Neuron
    • Introduction to Perceptron
    • Perceptron Learning Rule and Drawbacks
    • Multilayer Perceptron and Loss Function
  • Artificial Neural Networks (ANN)
    • What is a Neuron
    • ANN Architecture
    • Neural Network Activation Functions
    • Step Function
    • Linear Function
    • Sigmoid Function
    • Tanh Function
    • ReLU Function
    • Backpropagation vs Forward Pass
    • Gradient Descent
    • Fine-Tuning Neural Network Hyperparameters
    • Number of Hidden Layers and Hidden Neurons
    • Optimizer
    • Loss Functions
    • Finding Optimal Hidden Layers and Hidden Neurons in ANN
    • Forward and Backward Propagation, Epoch
    • Training MLP: Backpropagation
    • Cost Function
    • Introduction to PyTorch
    • Regularization
    • Optimizers
    • Hyperparameters and Tuning
  • TensorFlow Framework
    • Introduction to TensorFlow
    • TensorFlow Basic Syntax
    • TensorFlow Graphs
    • Variables and Placeholders
    • TensorFlow Playground
  • Computer Vision
    • Human Vision vs Computer Vision
    • CNN Architecture
    • Convolution, Max Pooling, Flatten Layer, Fully Connected Layer
    • Striding and Padding
    • Max Pooling
    • Data Augmentation
    • Introduction to OpenCV and YOLOv3 Algorithm
  • Image Processing with OpenCV
    • Image Basics with OpenCV
    • Opening Image Files with OpenCV
    • Drawing on Images with OpenCV
    • Face Detection with OpenCV
  • Video Processing with OpenCV
    • Introduction to Video Basics and Object Detection
    • Object Detection with OpenCV
  • RNN (Recurrent Neural Network)
    • Introduction to RNN
    • Backpropagation Through Time
    • Simple RNN Backward Propagation
    • Input and Output Sequences
    • RNN vs ANN
    • Vanishing and Exploding Gradient Problem
    • End-to-End Deep Learning Projects with Simple RNN
    • Different Types of RNN: LSTM, GRU
  • LSTM
    • Why LSTM
    • LSTM Architecture
    • Forget Gate in LSTM
    • Input Gate and Candidate Memory in LSTM
    • Output Gate in LSTM
    • Training Process in LSTM
    • Variants of LSTM
    • GRU RNN In-Depth Intuition
    • LSTM and GRU End-to-End Deep Learning Project

  • Text Processing
    • Introduction, What is a Token, Tokenization
    • Stop Words in spaCy Library
    • Stemming
    • Lemmatization
    • Lemmatization through NLTK
    • Lemmatization using spaCy
    • Word Frequency Analysis
    • Counter
    • Part of Speech and POS Tagging
    • POS using spaCy and NLTK
    • Dependency Parsing
    • Named Entity Recognition (NER)
    • NER with NLTK
    • NER with spaCy
    • Text Cleaning
    • Texts, Tokens
    • Basic Text Classification based on Bag of Words
  • Document Vectorization
    • Bag of Words
    • TF-IDF Vectorizer
    • Topic Modelling using LDA
    • Sentiment Analysis
    • Email Classification
    • Text Clustering

  • Open AI
    • Introduction to Open AI
    • Generative AI
    • ChatGPT (3.5)
    • LLM (Large Language Model)
    • Classification Tasks with Generative AI
    • Content Generation and Summarization with Generative AI
    • Information Retrieval and Synthesis Workflow with Gen AI
  • Time Series and Forecasting
    • Time Series Forecasting using Deep Learning
    • Seasonal-Trend Decomposition using LOESS (STL) Models
    • Bayesian Time Series Analysis
  • Sequence to Sequence Architecture
    • Encoder and Decoder
    • In-Depth Intuition of Encoder and Decoder
    • Sequence to Sequence Architecture
    • Problems with Encoder and Decoder
  • Attention Mechanism
    • Seq2Seq Networks
    • Attention Mechanism Architecture
  • Transformers
    • What and Why to Use Transformers
    • Understanding the Basic Architecture of Encoder
    • Self-Attention Layer Working
    • Multi-Head Attention
    • Feed Forward Neural Network with Multi-Head Attention
    • Positional Encoding
    • Layer Normalization
    • Layer Normalization Examples
    • Complete Encoder Transformer Architecture
    • Decoder Plan of Action
    • Decoder Masked Multi-Head Attention
    • Encoder and Decoder Multi-Head Attention
    • Decoder Final Linear and Softmax Layer
  • Hugging Face Platform and API
    • Introduction to Hugging Face
    • Hands-on with Transformers, HF Pipeline, Datasets, and LLMs
    • Data Processing, Tokenization, and Feature Extraction with Hugging Face
    • Fine-Tuning using Pretrained Models
    • Hugging Face API Key Generation
    • Project: Text Summarization with Hugging Face
    • Project: Text-to-Image Generation with LLMs using Hugging Face
    • Project: Text-to-Speech Generation with LLMs using Hugging Face
    • Hugging Face Platform and its API

  • Getting Started with Gen AI
    • Introduction to Gen AI and LLMs
    • Pretraining and Fine Tuning
    • Real-Time Support Chatbot Use Case
    • RAG, RLHF, LangChain, Few Shot Learning
    • Llama Model
  • Gen AI Practicals
    • Tasks (Sentiment Analysis)
    • Text Summarization, Translation, Question-Answer Tasks
    • Token Classification, Fill Mask, Text Generation
    • Feature Extraction, Zero Shot Classification
    • Selecting the Right Model for the Task, Preprocessing
    • Model Inference, Working of Softmax
    • More about Tokenizer: AutoTokenizer, AutoModel
    • Introduction to Hugging Face, Downloading a Model from Hugging Face, Execute NLP
  • Transformers Internals and Attention Blocks
    • Model Selection
    • Preprocessing: Tokenizer
    • Postprocessing: Softmax
    • Attention Block and Multilayer Perceptron: Decoding Attention Pattern
    • Attention Pattern: Vectors and Matrices
    • Embedding Matrix and Unembedding Matrix
    • Query Matrix and Query Vector, Key Vector and Key Matrix
    • Attention Mechanism: Embedding Contextual Knowledge
    • Multi Layer Perceptron: Feed Forward Layer
    • How MLP Works
  • Getting Started with RAG
    • Introduction to RAG
    • RAG Practicals (Using Google Colab)
    • RAG Practicals Using Databricks CE and Local
    • Accessing Gated Models
    • RAG Internals Explained
    • RAG Pipeline: Text to Embeddings
    • RAG Pipeline: Store Embeddings and Retrieve Answers with LLM
  • RAG End-to-End Production Pipeline
    • End-to-End Production Grade Pipeline
    • Generating Embeddings
    • Building Retriever and RAG LangChain Creation
    • Registering LangChain and Creating Serving Endpoint
  • LangChain Essentials
    • Understanding LangChain Framework
    • Inferring Large Models on Cloud
    • Working with OpenAI
    • Message Structure: System, Human, AI Message
    • Use Case: Few Shot Learning
    • Prompt Template
    • Task and Chain: Runnable Lambda, Runnable Sequence
    • Runnable Parallel
  • RAG Application using LangChain
    • Advanced RAG Application Use Case with LangChain
    • Document Loaders
    • Chunking Strategies and Embedding Model Selection
    • Retriever Configs, Search Types
    • Conversational RAG Solution
    • RAG Challenges

  • Agentic AI
    • Agentic Behavior in AI
    • Introduction to LangGraph
    • LangGraph Use Case: Natural Language to PySpark DataFrame
    • LangGraph Use Case: PySpark DataFrame to Spark SQL
    • Binding Tools to LLMs Using LangGraph
  • Agentic AI Tools
    • Registering Tools with LLMs in LangGraph
    • Tool Binding with ReAct Architecture
    • State Persistence in LangGraph Using Checkpoints
    • Structured Outputs with Pydantic Models
    • Reducers to Retain Full Conversation History
    • Handling Long Conversations with Message Filtering
    • Dynamic Summarization in LangGraph Agents
    • Persisting LangGraph State with SQLite Checkpointers
  • CRAG and Agentic AI Projects
    • Corrective RAG (CRAG)
    • Building a LangGraph-Based CRAG Application
    • Agentic AI Project: SQL Querying Agent with Tools
    • Designing a Multi-Role LLM System with Agents
  • LLM Fine-Tuning
    • Introduction to LLM Fine-Tuning
    • RAG vs Fine-Tuning
    • Types of Fine-Tuning
    • Full Fine-Tuning and Parameter-Efficient Fine-Tuning (PEFT)
    • LoRA and QLoRA (Reducing Memory and Compute Requirements)
    • Practical Fine-Tuning Demo
    • Hyperparameters

  • Introduction to Power BI
    • Power BI Job Roles in Real-time
    • Power BI Data Analyst Job Roles
    • Business Analyst - Job Roles
    • Power BI Developer - Job Roles
    • Power BI for Data Scientists
    • Comparing MSBI and Power BI
    • Comparing Tableau and Power BI
    • MCSA 70-778, MCSA 70-779 Exam
    • Types of Reports in Real-World
    • Interactive & Paginated Reports
    • Analytical & Mobile Reports
    • Data Sources Types in Power BI
    • Power BI Licensing Plans - Types
    • Power BI Training : Lab Plan
    • Power BI Dev & Prod Environments
    • Understanding the Power BI Tools
    • Installing Power BI & Connecting to Data
    • The "Locale" used in the curriculum
    • Working with the query Editor
    • Working with the data model and creating a visualization
  • Basic Report Design
    • Power BI Desktop Installation
    • Data Sources & Visual Types
    • Canvas, Visualizations and Fields
    • Get Data and Memory Tables
    • In-Memory xvelocity Database
    • Table and Tree Map Visuals
    • Format Button and Data Labels
    • Legend, Category and Grid
    • PBIX and PBIT File Formats
    • Visual Interaction, Data Points
    • Disabling Visual Interactions
    • Edit Interactions - Format Options
    • SPOTLIGHT & FOCUSMODE
    • CSV and PDF Exports. Tooltips
    • Power BI EcoSystem, Architecture
  • Visual Sync, Grouping
    • Slicer Visual : Real-time Usage
    • Orientation, Selection Properties
    • Single & Multi Select, CTRL Options
    • Slicer : Number, Text and Date Data
    • Slicer List and Slicer Dropdowns
    • Visual Sync Limitations with Slicer
    • Disabling Slicers,Clear Selections
    • Grouping : Real-time Use, Examples
    • List Grouping and Binning Options
    • Grouping Static / Fixed Data Values
    • Grouping Dynamic / Changing Data
    • Bin Size and Bin Limits (Max, Min)
    • Bin Count and Grouping Options
    • Grouping Binned Data, Classificationx
  • Hierarchies, Filters
    • Creating Hierarchies in Power BI
    • Independent Drill-Down Options
    • Dependant Drill-Down Options
    • Conditional Drilldowns, Data Points
    • Drill Up Buttons and Operations
    • Expand & Show Next Level Options
    • Dynamic Data Drills Limitations
    • Show Data and See Records
    • Filters : Types and Usage in Real-time
    • Visual Filter, Page Filter, Report Filter
    • Basic, Advanced and TOP N Filters
    • Category and Summary Level Filters
    • DrillThru Filters, Drill Thru Reports
    • Keep All Filters" Options in DrillThru
    • CrossReport Filters, Include, Exclude
  • Bookmarks, Azure, Modeling
    • Drill-thru Filters, Page Navigations
    • Bookmarks: Real-time Usage
    • Bookmarks for Visual Filters
    • Bookmarks for Page Navigations
    • Selection Pane with Bookmarks
    • Buttons, Images with Actions
    • Buttons, Actions and Text URLs
    • Bookmarks View & Selection Pane
    • OLTP Databases, Big Data Sourcesx
    • Azure Database Access, Reports
    • Import & Direct Query with Power BI
    • SQL Queries and Enter Data
    • Data Modeling : Currency, Relations
    • Summary, Format, Synonyms
    • Web View & Mobile View in PBI
  • Visualization Properties
    • Stacked Charts and Clustered Charts
    • Line Charts, Area Charts, Bar Charts
    • 100% Stacked Bar & Column Charts
    • Map Visuals: Tree, Filled, Bubble
    • Cards, Funnel, Table, Matrix
    • Scatter Chart : Play Axis, Labels
    • Series Clusters & Selections
    • Waterfall Chart and ArcGIS Maps
    • Info graphics, Icons and Labels
    • Color Saturation, Sentiment Colors
    • Column Series, Column Axis in Lines
    • Join Types : Round, Bevel, Miter
    • Shapes, Markers, Axis, Plot Area
    • Display Units, Data Colors, Shapes
    • Series, Custom Series and Legends
  • Power Query Level 1
    • Power Query M Language Purpose
    • Power Query Architecture and ETL
    • Data Types, Literals and Values
    • Power Query Transformation Types
    • Table & Column Transformations
    • Text & Number Transformations
    • Date, Time and Structured Data
    • List, Record and Table Structures
    • let, source, in statements at the rate M Lang
    • Power Query Functions, Parameters
    • Invoke Functions, Execution Results
    • Get Data, Table Creations and Edit
    • Merge and Append Transformations
    • Join Kinds, Advanced Editor, Apply
    • ETL Operations with Power Query
  • Power Query Level 2
    • Query Duplicate, Query Reference
    • Group By and Advanced Options
    • Aggregations with Power Query
    • Transpose, Header Row Promotion
    • Reverse Rows and Row Count
    • Data Type Changes & Detection
    • Replace Columns: Text, NonText
    • Replace Nulls: Fill Up, Fill Down
    • PIVOT, UNPIVOT Transformations
    • Move Column and Split Column
    • Extract, Format and Numbers
    • Date & Time Transformations
    • Deriving Year, Quarter, Month, Day
    • Add Column : Query Expressions
    • Query Step Inserts and Step Edits
  • Power Query Level 3
    • Creating Parameters in Power Query
    • Parameter Data Types, Default Lists
    • Static/Dynamic Lists For Parameters
    • Removing Columns and Duplicates
    • Convert Tables to List Queries
    • Linking Parameters to Queries
    • Testing Parameters and PBI Canvas
    • Multi-Valued Parameter Lists
    • Creating Lists in Power Query
    • Converting Lists to Table Data
    • Advanced Edits and Parameters
    • Data Type Conversions, Expressions
    • Columns From Examples, Indexes
    • Conditional Columns, Expressions
  • DAX Functions - Level 1
    • DAX : Importance in Real-time
    • Real-world usage of Excel, DAX
    • DAX Architecture, Entity Sets
    • DAX Data Types, Syntax Rules
    • DAX Measures and Calculations
    • ROW Context and Filter Context
    • DAX Operators, Special Characters
    • DAX Functions, Types in Real-time
    • Vertipaq Engine, DAX Cheat Sheet
    • Creating, Using Measures with DAX
    • Creating, Using Columns with DAX
    • Quick Measures and Summaries
    • Validation Errors, Runtime Errors
    • Conditional Columns, Expressions
    • Dynamic Expressions, IF in DAX
  • DAX Functions - Level 2
    • Data Modeling Options in DAX
    • Detecting Relations for DAX
    • Using Calculated Columns in DAX
    • Using Aggregated Measures in DAX
    • Working with Facts & Measures
    • Modeling : Missing Relations
    • Modeling : Relation Management
    • CALCULATE Function Conditions
    • CALCULATE & ALL Member Scope
    • RELATED & COUNTROWS in DAX
    • Entity Sets and Slicing in DAX
    • Dynamic Expressions, RETURN
    • Date, Time and Text Functions
    • Logical, Mathematical Functions
    • Running Total & EARLIER Function
  • DAX Functions - Level 3
    • Connection with CSV, MS Access
    • AVERAGEX and AVERAGE in DAX
    • KEEPFILTERS and CALCUALTE
    • COUNTROWS, RELATED, DIVIDE
    • PARALLELPERIOD, DATEDADD
    • CALCULATE & PREVIOUSMONTH
    • USERELATIONSHIP, DAX Variables
    • TOTALYTD , TOTALQTD
    • DIVIDE, CALCULATE, Conditions
    • IF..ELSE..THEN Statement
    • SELECTEDVALUE, FORMAT
    • SUM, DATEDIFF Examples in DAX
    • TODAY, DATE, DAY with DAX
    • Time Intelligence Functions - DAX
  • Power BI Cloud - 1
    • Power BI Service Architecture
    • Power BI Cloud Components, Use
    • App Workspaces, Report Publish
    • Reports & Related Datasets Cloud
    • Creating New Reports in Cloud
    • Report Publish and Report Uploads
    • Dashboards Creation and Usage
    • Adding Tiles to Dashboards
    • Pining Visuals and Report Pages
    • Visual Pin Actions in Dashboards
    • LIVE Page Interaction in Dashboard
    • Adding Media: Images, Custom Links
    • Adding Chs and Embed Links
    • API Data Sources, Streaming Data
    • Streaming Dataset Tiles (REST API)
  • Power BI Cloud - 2
    • Dashboards Actions, Report Actions
    • DataSet Actions: Create Report
    • Share, Metrics and Exports
    • Mobile View & Dashboard Themes
    • Q & A [Cortana] and Pin Visuals
    • Export, Subscribe, Subscribe
    • Favorite, Insights, Embed Code
    • Featured Dashboards and Refresh
    • Gateways Configuration, PBI Service
    • Gateway Types, Cloud Connections
    • Gateway Clusters, Add Data Sources
    • Data Refresh : Manual, Automatic
    • PBIEngw Service, ODG Logs, Audits
    • DataFlows, Power Query Expressions
    • Adding Entities and JSON Files
  • Excel & RLS
    • Import and Upload Options in Excel
    • Excel Workbooks and Dashboards
    • Datasets in Excel and Dashboards
    • Using Excel Analyzer in Power BI
    • Using Excel Publisher in PBI Cloud
    • Excel Workbooks, PINS in Power BI
    • Excel ODC Connections, Power Pivot
    • Row Level Security (RLS) with DAX
    • Need for RLS in Power BI Cloud
    • Data Modeling in Power BI Desktop
    • DAX Roles Creation and Testing
    • Adding Power BI Users to Roles
    • Custom Visualizations in Cloud
    • Histogram, Gantt Chart, Infographics
  • Report Server, RDL
    • Need for Report Server in PROD
    • Install, Configure Report Server
    • Report Server DB, Temp Database
    • Web service URL, Webportal URL
    • Creating Hybrid Cloud with Power BI
    • Using Power BI DesktopRS
    • Uploading Interactive Reports
    • Report Builder For Report Server
    • Report Builder For Power BI Cloud
    • Designing Paginated Reports (RDL)
    • Deploy to Power BI Report Server
    • Data Source Connections, Report
    • Power BI Report Server to Cloud
    • Tenant IDs Generation and Use
    • Mobile Report Publisher, Usage
  • PowerApp
    • Overview
    • Basic Power App Concept
    • Canvas Apps | Navigation | Customization
    • Contents (Galleries, Data Cards, Forms, Triggers, Functions & Formulas, Edit Forms, Text Boxes)
  • Power BI Service & Power BI Mobile
    • Why Power Bi Service?
    • Comparison Power BI Free & Premium
    • Logging into Power Bi Service
    • Interface overview
    • Importing data from Desktop to Service
    • Dataset menu
    • Working on reports
    • Dashboard overview
    • Workspace & Gateways
    • Installing Gateways - Personal & On-premise
    • Working alone or collaborating with colleagues
    • Collaborating in App Workspace
    • Sharing the results
    • Publishing the app
    • Content packs from online services
    • Power Bi Mobile Overview
    • Excluding dataset from sharing
  • Power BI and Excel Together
    • Options for Publishing from Excel
    • Pin Excel Elements to Power BI
    • Analyze in Excel (Power BI Pro or Premium)
    • Excel Publish: Upload and Export to Power BI
    • Sharing Published Excel Dashboards (Power BI Pro or Premium)

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Career Opportunities After the Course

  1. Data Analyst
  2. Business Intelligence Analyst
  3. Data Visualization Specialist
  4. Reporting Analyst
  5. Marketing Analyst
  6. Operations Analyst

Industries Hiring Data Analysts

  1. 📈 Information Technology
  2. 💰 Banking & Finance
  3. 🏥 Healthcare & Pharma
  4. 🛒 E-Commerce & Retail
  5. 📊 Consulting & Research
  6. 🚚 Logistics & Supply Chain

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