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  • About Our Advanced Data Analytics with M.L. Training Program with 100% Job Placement -

    Learn database, visualization, statistics and data analyis with machine learning and projects under our experts to become an Advanced Data Analytics Professional in short duration with 100% Job Placement.

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    Get hired quickly with early placement initiation around 70% training completion (criteria-based) or bydefault on completion, full placement support and continuous expert guidance until you get hired. Enroll Today.  

    Fees / Details

  • Jobs Roles Targeted -

    Data Analyst, Data Analytics Developer, Power BI Developer, Data Expert etc.

  • Pre-requisites -

    None. Any one from IT & non IT can learn.

  • Duration -

    7.5 months
    with option to complete in 5/6 months

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Syllabus

  • Learn both MySQL & Adv. MySQL
  • MySQL & Database Basics
  • Installation
    • Server
    • Workbench (client)
  • DDL - Data Definition Language
    • Tables - Create, Alter, Drop, Auto Increment
    • Constraints - Not Null, Primary Key, Unique Key, Foreign Key
    • Index - Why required, Create, Drop
  • DML - Data Manipulation Language
    • Insert, Update, Delete & Truncate
  • Transactions (T SQL)
    • Commit
    • Rollback
  • DQL - Data Query Language (Select)
  • Filters
    • Operators - <, <=, >, >=, =, !=
    • BETWEEN
    • IN
    • LIKE
    • NOT
    • NULL
    • AND, OR, NOT
    • Multi conditions filtering
    • DISTINCT
    • Limit, offset
  • Sorting
    • Ascending Order
    • Descending Order
    • Multi Column Sorting
  • Functions
    • Data, number, characters, null values etc.
    • Case
  • Groups
    • Basics
    • Grouping functions - AVG, MIN, MAX, COUNT, DISTINCT COUNT
    • Grouping Filters - HAVING
  • Joins
    • Cartesian Product
    • Equi and Non-Equi Joins
    • Left Outer Join
    • Right Outer Join
    • Full Outer Join
    • Self Join
  • SET Operators
    • Union
    • Union All
    • Intersect
    • Minus
  • Subquery
    • Single Valued
    • Multi Valued
  • Views & Inline Views
  • Procedures
    • Programming basics - if..else, for loop
    • Procedures - create, drop
  • Functions
    • create, drop
  • Triggers
    • create, drop
  • Analytic (Window) Functions
    • Top N Analysis
    • Over() with summary functions
    • Partition By Queries
    • ROW_NUMBER()
    • RANK()
    • DENSE_RANK()
  • Project
  • Introduction to Python
    • What is Python? Features & uses
    • Python installation & IDE setup (VS Code, PyCharm, Jupyter, Anaconda)
    • Running Python programs (script mode vs interactive mode)
    • First program: print("Hello, World!")
  • Python Basics
    • Variables & Data Types (int, float, str, bool)
    • Input/Output functions (print(), input())
    • Comments & indentation
    • Type conversion & type() function
  • Operators
    • Arithmetic operators (+, -, *, /, //, %, **)
    • Comparison operators (==, !=, <, >, <=, >=)
    • Logical operators (and, or, not)
    • Assignment operators (+=, -=, etc.)
    • Identity operators (is, is not)
    • Membership operators (in, not in)
  • Control Flow
    • if, elif, else statements
    • Nested conditions
    • for and while loops
    • break, continue, pass
  • Data Structures
    • Strings → indexing, slicing, methods
    • Lists → create, access, update, iterate, list methods
    • Tuples → immutable collections
    • Sets → unique values, set operations (union, intersection)
    • Dictionaries → key-value pairs, methods
  • Functions
    • Defining & calling functions
    • Function arguments (positional, keyword, default, variable-length *args, **kwargs)
    • Return values
    • Lambda (anonymous functions)
    • Scope (local vs global variables)
  • Modules & Packages
    • Importing built-in modules (math, random, datetime, os)
    • Creating your own modules
    • Installing external libraries with pip
  • File Handling
    • Opening & closing files
    • Reading & writing (read(), write(), with open)
    • Working with CSV and JSON files
  • Exception Handling
    • try, except blocks
    • finally, else in exceptions
    • Raising exceptions (raise)
    • Custom exceptions
  • Object-Oriented Programming (OOP)
    • Classes & objects
    • Constructors (__init__)
    • Instance & class variables
    • Methods (instance, class, static)
    • Inheritance (single, multiple, multilevel)
    • Method overriding
    • Encapsulation & Abstraction
    • Polymorphism
    • Special methods (__str__, __len__, etc.)
  • Advanced Python Concepts
    • Iterators & Generators (iter(), next(), yield)
    • Decorators (function decorators, @staticmethod, @classmethod)
    • Regular Expressions (re module)
  • Working with APIs
    • Sending HTTP requests with requests
    • JSON handling
  • Database Connectivity
    • Installation
    • Basic CRUD Operations (create, read, insert, update, delete)
    • MySQL with connectors
  • Python for Automation
    • Automating tasks (file renaming, web scraping with BeautifulSoup)
    • Sending emails with Python (smtplib)
    • Working with Excel (openpyxl, pandas)
  • Python for Data Science
    • NumPy → arrays, vectorized operations
    • Pandas → DataFrames, Series, CSV/Excel handling
  • Python for Data Visualization
    • Installing Matplotlib (pip install matplotlib)
    • Basic Plots: Line chart, Bar chart, Pie chart
    • Statistical Plots: Histogram, Scatter plot, Box plot
  • Understand the Excel interface.
  • Learn basic navigation and data entry.
  • Excel Interface Overview
    • Workbook vs. Worksheet
    • Ribbon, Tabs, and Menus
    • Cells, Rows, Columns
    • Name Box and Formula Bar
  • Basic Navigation
    • Moving around the worksheet
    • Selecting cells, rows, and columns
    • Using the Go To command
  • Data Entry and Basic Formatting
    • Entering text, numbers, and dates
    • Editing cell contents
    • Basic formatting (font, size, color, cell alignment)
  • Saving and Managing Workbooks
    • Saving, opening, and closing workbookss
    • Save As options (different formats)
  • Summary Functions: SUM, AVERAGE, MIN, MAX, SUMIF, COUNTIF, AVERAGEIF, SUMIFS, COUNTIFS, AVERAGEIFS
  • Using AutoSum
  • Cell Referencing
    • Relative vs. Absolute References
    • Mixed References
  • Organize and manage data efficiently
  • Sorting and Filtering Data
    • Sorting by one or multiple columns
    • Applying filters to display specific data
    • Using Filters using effectively
  • Using Tables
    • Creating and formatting tables
    • Table styles and features (Total Row, Header Row)
  • Data Validation:
  • Creating drop-down lists
  • Setting data entry rules
  • Conditional formatting:
    • Conditional formatting using ifs function, greater than, lower than and equal between
    • Conditional formatting using colours
    • Conditional formatting using colour scales, data bars & Icon sets
  • Use advanced formulas and functions
  • Logical Functions
    • IF, AND, OR, NOT
  • Loopkup Functions
    • VLOOKUP, VLOOKUP WITH MATCH FUNCTION, VLOOKUP WITH COLUMN & ROW FUNCTION, VLOOKUP WITH SEQUENCE FUNCTION, VLOOKUP WITH CURLY PARANTHESIS FUNCTION, VLOOKUP WITH SUMIF, SUMIFS, COUNTIF & COUNTIFS FUNCTIONS, VLOOKUP WITH MATCH FUNCTION, INDEX & MATCH, XLOOKUP, XLOOKUP WITH CONCATENATE FUNCTIONS
  • Text Functions
    • CONCATENATE, LEFT, RIGHT, MID, LEN
  • Date and Time Functions
    • TODAY, NOW, DATE, TIME, YEAR, MONTH, DAY, EOMONTH
  • Office 365 function
    • Transpose
    • Textjoin
    • Sequence
    • Filter
    • Xlookup
    • Vstack
    • Hstack
    • Aggregate and many more
  • Analyze data using built-in Excel tools
  • Introduction to PivotTables
    • What is a PivotTable?
    • Benefits and uses of PivotTables
  • Creating a PivotTable
    • Preparing your data
    • Inserting a PivotTable from various data sources
  • Understanding PivotTable Components
    • Rows, Columns, Values, and Filters areas
    • Field List and Field Settings
  • Basic PivotTable Operations
    • Adding and arranging fields
    • Sorting and filtering data within PivotTables
    • Grouping data (by dates, numbers, etc.)
  • Customizing PivotTables
    • Changing the layout and style
    • Using different PivotTable layouts (Compact, Outline, Tabular)
    • Formatting numbers and cells
  • Calculations in PivotTables
    • Adding calculated fields and items
    • Using summary functions (SUM, COUNT, AVERAGE, etc.)
    • Showing values as percentages, differences, rankings
  • PivotTable Options and Settings
    • Understanding PivotTable options
    • Refreshing data in PivotTables
    • Changing data source
  • PivotCharts
    • Creating PivotCharts from PivotTables
    • Customizing and formatting PivotCharts
    • Understanding the relationship between PivotTables and PivotCharts
  • Advanced PivotTable Techniques
    • Creating dynamic PivotTables with named ranges
    • Using Power Pivot to manage large data sets
    • Connecting PivotTables to external data sources
  • Create and customize advanced charts and graphs
  • Creating Charts
    • Column, Bar, Line, Pie, and Combo charts
    • Customizing chart elements (titles, axes, legends)
  • Using Sparklines
    • Creating line, column, and win/loss sparklines
  • Advanced Chart Techniques
    • Dynamic charts with named ranges
    • Using secondary axes
  • Automate tasks using Macros and VBA
  • Introduction to Macros
    • Recording and running macros
    • Macro security settings
  • Basics of VBA (Recording):
    • Starting with the recording Macros
    • Recording of Macros
    • Saving and running of Macros
  • Creating Custom Functions
    • Writing and using custom functions in VBA
  • Use Excel’s collaborative and security features
  • Sharing and Collaboration
    • Sharing workbooks
    • Co-authoring and tracking changes
    • Comments and notes
  • Protecting Data
    • Worksheet and workbook protection
    • Setting permissions
  • Excel Online and Mobile
    • Using Excel in Office 365
  • Power Query
  • Introduction to Power Query
    • What is Power Query?
    • Importance and benefits of using Power Query
  • Getting Started with Power Query
    • Installing and accessing Power Query
    • Power Query interface and key components
  • Connecting to Data Sources
    • Types of data sources (Excel, databases, web, etc.)
    • How to connect to different data sources
  • Data Import and Shaping
    • Loading data into Power Query
    • Basic data transformation (filtering, sorting, removing duplicates)
  • Transforming Data
    • Using the Query Editor
    • Applying common transformations (merging, appending, splitting columns)
    • Pivoting and unpivoting columns
    • Grouping data
  • Data Cleaning
    • Handling missing values
    • Data type conversion
    • Text transformations (trim, clean, replace, etc.)
  • Working with Functions
    • Using built-in functions
    • Creating custom functions
    • Parameters in Power Query
  • Advanced Data Transformations
    • Merging queries
    • Append queries
    • Using conditional columns
    • Calculating and adding new columns
  • Scenario manager
  • Macro (record functionality)
  • Dashboards (Project)
    • What are dashboards
    • Getting data from differnt worksheets
    • Display data in various charts on same sheet etc.
  • Introduction to Power BI
    • What is Power BI and Why Power BI
    • Installing Power BI Desktop
    • Exploring the Power BI Workflow
    • Adjusting the settings of the Power BI Desktop
    • Comparison of Power BI vs Other Reporting Tools
  • Getting and Transforming Data with Power BI Desktop
    • Connecting to different sources
    • Connecting to multiple sources
    • Different connecting Options(DirectQuery vs Import Data Vs Live Connection)
    • Shaping and transforming data with Power Query
    • Editing,Merging,Appending queries etc
  • Advance Importing techniques without coding
  • Data cleaning & Advance data cleaning
  • Modeling with Power BI
    • Introduction to Modeling
    • Building Relational Models(Setup and Manage Relationships)
    • Creating table relationships
    • Understanding the filter flow
    • Cardinality and Cross filtering
  • DAX
    • Understanding Dax Syntax
    • Calculated Columns vs Measures
    • Common Dax functions and formulae
    • Understanding the evaluation context in DAX
  • Visualising Data with Reports
    • Creating Visualisations
    • Color & Conditional Formatting
    • Setting Sort Order
    • Scatter & Bubble Charts & Play Axis
    • Tooltips
    • Slicers, Timeline Slicers & Sync Slicers
    • Cross Filtering and Highlighting
    • Visual, Page and Report Level Filters
    • Drill Down/Up
    • Hierarchies
    • Constant Lines
    • Tables, Matrices & Table Conditional Formatting
    • KPI's, Cards & Gauges
    • Map Visualisations
    • Custom Visuals
    • Managing and Arranging
    • Drillthrough
    • Custom Report Themes
    • Grouping and Binning
    • Bookmarks & Buttons
  • Introduction to Power BI Service
    • Introduction to the Power BI Service
    • Quick Tour of Power BI Service
    • Connecting to Data from Power BI service
    • Building Blocks of Power BI Service
  • Sharing and Collaboration Tools in Power BI Service
    • Sharing and Collaboration Options Overview
    • Publish from Power BI Desktop
    • Publish Reports to Web
    • Printing and Exporting from Power BI Service
    • Sharing Reports & Dashboards
  • Power BI Refreshing Datasets
    • What is refreshing?
    • Implementation
  • A.I. in Power BI
  • Advance Report techniques
  • Power Automate (Latest)
    • Introduction
    • installation
    • Types of flows
    • Actions
    • Copy multiple excel, pdf files from one folder
    • Merge PDF files together, Rename etc.
    • Extract phones name and price from amazon
  • Best Practices
  • Mean, Median, Mode, Variance, Standard Deviation
  • Skewness, Kurtosis, Correlation, Covariance
  • Probability Distributions: Normal, Binomial, Poisson
  • Sampling, Central Limit Theorem
  • Hypothesis Testing: t-test, z-test, ANOVA, chi-square
  • Confidence Intervals, p-values, Type I & II errors
  • Note – Statistics part will be taken along with ML & AI as and when required
  • Data Preprocessing & EDA Overview
  • Handling missing values, outliers, duplicates
  • Encoding (One-hot, Label), Scaling (Standard, MinMax)
  • Feature engineering, selection techniques
  • EDA: Univariate, Bivariate, Multivariate analysis
  • Visuals: Heatmaps, Pairplots, Boxplots, Violin plots
  • Supervised Learning
    • Linear & Logistic Regression
    • Decision Tree, Random Forest, KNN
    • SVM with kernel functions
    • Naive Bayes, AdaBoost, XGBoost
    • Project 1: House Price Prediction
    • Project 2: Email Spam Detection
  • Unsupervised Learning
    • K-Means, DBSCAN, Hierarchical Clustering
    • Dimensionality Reduction: PCA
    • Project 3: Employee Role Segmentation
  • Model Evaluation & Optimization
    • Metrics: Accuracy, Precision, Recall, F1, AUC, R2, RMSE
    • Cross Validation, Stratified K-Fold
    • Hyperparameter tuning: GridSearchCV, RandomizedSearchCV
    • Imbalanced Data Handling: SMOTE, NearMiss
  • Time Series Analysis Basics
  • Trend, Seasonality, Noise, Stationarity
  • ADF Test, ARIMA, SARIMA
  • Facebook Prophet, Exponential Smoothing
  • Project 4: Sales Forecasting
  • Weekdays (MTTF, Wed Off) :
    Option 1 - 7.5 months - 1.5 to 2hrs/day
    Option 2 - 4.5 to 5 months - 2.5hrs/day (including 10 mins break)

    Weekends(Sat & Sun):
    Option 1 - 7.5 to 8 months - 2 to 2.5hrs/day
    Option 2 - 5.5 to 6 months - 2.5 to 3hrs/day (including 10 mins break)

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