Black Antz Technologies

our courses

Get Free Demo

DATA ANALYTICS

Join course

Join course

Unlock your potential with Black Antz Technology. Master tools like Excel, SQL, Python, and Power BI to build real-world data analytics skills. Learn to analyze, visualize, and make data-driven decisions for a thriving career in this high-demand field.

Pro sessions

Join expert-led sessions at Black Antz Technology and gain real-world data skills.

Project Portfolio

Start building a job-ready profile with a dynamic project portfolio

Career Assistance

Prepare for interviews with guidance and opportunities to showcase skills

Peer Network's

Build connections with like-minded learners to exchange ideas and experiences.

Certification

Attain your certificate upon course completion to showcase your capabilities.

Learn Skills

Accelerate your upskilling with industry-relevant skills and personalized support..

Job Assistance

25th April 2025

6 Months

Live + Recorded

English

Syllabus for Data Analytics

EXCEL

ADVANCE EXCEL

DATA BASE

TABLEAU

POWER B I

PYTHON

NUMPY

PANDAS

Excel Enviorment +

Lecture 1: Navigating a worksheet

Lecture 2: Spreadsheet terminology

Lecture 3: Getting help

Entering and Editing data +

Lecture 1: Entering and editing text and values

Lecture 2: Entering and editing formulas

Lecture 3: Saving and updating workbooks

Modifying a worksheet +

Lecture 1: Moving and copying data

Lecture 2: Moving and copying formulas

Lecture 3: Inserting and deleting ranges, rows, and Columns

Lecture 4: Cell comments

Using Function +

Lecture 1: Entering functions

Lecture 2: AutoSum

Lecture 3: Other common functions

Formatting +

Lecture 1: Text formatting

Lecture 2: Row and column formatting

Lecture 3: Number formatting

Lecture 4: Conditional formatting

Lecture 5: Additional formatting options

Printing +

Lecture 1: Preparing to print

Lecture 2: Page Setup options

Lecture 3: Printing worksheets

Charts +

Lecture 1: Chart basics

Lecture 2: Pie Chart

Lecture 3: Bar Chart

Case Study +

Lecture 1: Modifying existing worksheet

Lecture 1: Classes & Objects

Lecture 2: Use shortcut keys

Lecture 3: Create and email worksheet

Review Basics +

Lecture 1: Downloading from Account Reconciliation

Lecture 2: The Excel environment

Lecture 3: The Sparkline

Lecture 4: The Trendline

Subtotal Functions +

Lecture 1: Create an outline and consolidate data

Lecture 2: Create subtotals in a list

Lecture 3: Use multiple subtotal functions – SUBTOTAL, SUMIF

Lecture 4: Create custom views to save different sets of worksheet display and print settings

Range Names and Filter Date +

Lecture 1: Define and apply cell and range names

Lecture 2: Use names in Formulas

Lecture 3: Filter data based on complex criteria

Lecture 4: Use conditional filters

Lecture 5: Copy filtered results to another range

Pivot Tables +

Lecture 1: Prepare data in a table format and name the table

Lecture 2: Create a PivotTable for analyzing

Lecture 3: Use the Download Actuals page in Account Reconciliation as example

Lecture 4: Modify or re-arrange fields

Selected Functions +

Lecture 1: Using IF and SUMIF functions to calculate a value based on specified criteria

Lecture 2: Use ROUND function to round off numbers

Lecture 3: Use VLOOKUP to find values in worksheet data

Lecture 4: Use HLOOKUP

DDL +

Lecture 1: What is database?

Lecture 2: What is database management system?

Lecture 3: What is Relational Model?

Lecture 4: Introduction to RDBMS

Lecture 5: Brief on E.F. Codd

Lecture 6: What are Datatypes?

Lecture 7: Types and Examples (Datatypes)

Lecture 8: How to Use Datatypes

Lecture 9: What are Constraints?

Lecture 10: Types and Examples (Constraints)

Lecture 11: How to Use Constraints

Lecture 12: Data Definition Language (DDL)

Lecture 13: Data Manipulation Language (DML)

Lecture 14: Transaction Control Language (TCL)

Lecture 15: Data Control Language (DCL)

Lecture 16: Data Query Language (DQL)

Lecture 17: SELECT, FROM, WHERE, GROUP BY, HAVING, ORDER BY

Lecture 18: Operators

Lecture 19: Single Row Functions

Lecture 20: Multi Row Functions

Lecture 21: Introduction to Sub Query

Lecture 22: Working of Sub Query

Lecture 23: Query Writing and Execution

Lecture 24: Types of Sub Query

Lecture 25: Single Row Sub Query

Lecture 26: Multi Row Sub Query

Lecture 27: Nested Sub Query

Lecture 28: Introduction on Pseudo Columns

Lecture 29: ROWID

Lecture 30: ROWNUM

Lecture 31: What is Join?

Lecture 32: Types of Joins

Lecture 33: Cartesian Join

Lecture 34: Inner Join

Lecture 35: Outer Join

Lecture 36: Self-Join

Lecture 37: Queries and Examples (Joins)

Lecture 38: Co-related Sub Query

DDL +

Lecture 1: Create

Lecture 2: Rename

Lecture 3: Alter

Lecture 4: Truncate

Lecture 5: Drop

DML +

Lecture 1: Insert

Lecture 2: Update

Lecture 3: Delete

TCL +

Lecture 1: Commit

Lecture 2: Save poi nt

Lecture 3: Rollback

DCL+

Lecture 1: Grant

Lecture 2: Revoke

Lecture 3: Normalization

Lecture 4: E R Diagrams

Tableau Overview +

Lecture 1: Why Tableau? Why Visualization?

Lecture 2: Level Setting – Terminology

Lecture 3: Getting Started – creating some powerful visualizations quickly

Lecture 4: The Tableau Product Line

Lecture 5: Things you should know about Tableau

Lecture 6: Connecting to Data and introduction to data source concept

Lecture 7: Working with data files versus database server

Lecture 8: Understanding the Tableau workspace

Lecture 9: Dimensions and Measures

Lecture 10: Using Show Me!

Lecture 11: Tour of Shelves (How shelves and marks work)

Lecture 12: Building Basic Views

Lecture 13: Help Menu and Samples

Lecture 14: Saving and sharing your work

Tableau Course Content +

Lecture 1: Marks

Lecture 2: Size and Transparency

Lecture 3: Highlighting

Lecture 4: Working with Dates

Lecture 5: Date aggregations and date parts

Lecture 6: Discrete versus Continuous

Lecture 7: Dual Axis / Multiple Measures

Lecture 8: Combo Charts with different mark types

Lecture 9: Geographic Map Page Trails

Lecture 10: Heat Map

Lecture 11: Density Chart

Lecture 12: Scatter Plots

Lecture 13: Pie Charts and Bar Charts

Lecture 14: Small Multiples

Lecture 15: Working with aggregate versus disaggregate data

Lecture 16: Analyzing

Lecture 17: Sorting & Grouping

Lecture 18: Aliases

Lecture 19: Filtering and Quick Filters

Lecture 20: Cross-Tabs (Pivot Tables)

Lecture 21: Totals and Subtotals Drilling and Drill Through

Lecture 22: Aggregation and Disaggregation

Lecture 23: Percent of Total

Lecture 24: Working with Statistics and Trend lines

Getting started with Calculated Fields +

Lecture 1: Working with String Functions

Lecture 2: Basic Arithmetic Calculations

Lecture 3: Date Math

Lecture 4: Working with Totals

Lecture 5: Custom Aggregations

Lecture 6: Logic Statements

Formatting +

Lecture 1: Options in Formatting your Visualization

Lecture 2: Working with Labels and Annotations

Lecture 3: Effective Use of Titles and Captions

Lecture 4: Introduction to Visual Best Practices

Building Interactive Dashboard +

Lecture 1: Combining multiple visualizations into a dashboard

Lecture 2: Making your worksheet interactive by using actions and filters

Lecture 3: An Introduction to Best Practices in Visualization

Sharing Workbooks +

Lecture 1: Publish to Reader

Lecture 2: Packaged Workbooks

Lecture 3: Publish to Office

Lecture 4: Publish to PDF

Lecture 5: Publish to Tableau Server and Share over the Web

Power BI Overview +

Lecture 1: Overview

Lecture 2: Introduction

Lecture 3: Components

Lecture 4: Architecture

Lecture 5: Product Portfolio

Connecting To Power BI Data +

Lecture 1: Introduction to Power BI Desktop tool

Lecture 2: Options from where to connect to data

Lecture 3: Different Views

Lecture 4: Explained Pre-built connectors available

Data Load In Power BI +

Lecture 1: Load data into Power BI

Lecture 2: Data Transformation and do complex calculations

Lecture 3: Introduction to Excel

Lecture 4: Load data

Lecture 5: Data Transformation

Lecture 6: Power Query Editor

Lecture 7: Applied Steps

Lecture 8: Power Query options

Data Transformation +

Lecture 1: Data Transformation

Calculations & Measures +

Lecture 1: Power Query Editor

Lecture 2: Transform Data

Lecture 3: Date Hierarchy

Lecture 4: Create Custom Column in Query Editor

Lecture 5: Replace Value

Lecture 6: Addressing Error

Lecture 7: Reorder Columns

Lecture 8: Merge Columns

Lecture 9: Split Columns

Lecture 10: Creating Graphs

Lecture 11: Bar Chart

Lecture 12: Line chart

Lecture 13: Data Visualization settings

Lecture 14: Drill Down-up

Lecture 15: Practical of DAX

Lecture 16: Measures and Columns

Lecture 17: M-Query Language

Lecture 18: DAX Expression Writing Best Practices

Power BI Data Model & Schema +

Lecture 1: Data Modelling

Lecture 2: Overview

Lecture 3: Why Data Model is used?

Lecture 4: Steps in Dimensional Data Modelling

Lecture 5: Introduction to Schema

Lecture 6: Four types of Schemas

Manage Data Relationship +

Lecture 1: Manage Data Relation in Power BI

Lecture 2: Adding & Removing Relationship

Lecture 3: Manage Relationship

Lecture 4: Cardinality

Lecture 5: Cross Filter Direction

Dax Queries & Measures +

Lecture 1: Introduction of DAX

Lecture 2: Functions in DAX

Lecture 3: Operators

Lecture 4: Precedence Order

Lecture 5: Parameter Naming Convention

Lecture 6: DAX Syntax

Lecture 7: Creating Measures

Lecture 8: Calculate Table

Lecture 9: Interactive Data Visualizations

Interactive Data Visualization +

Lecture 1: Interactive Data Visualizations

Assignment +

Lecture 1: Data Transformation

Case Study +

Lecture 1: Assignment

Bookmarks & Filters +

Lecture 1: Filters & Slicers

Lecture 2: Introduction

Lecture 3: Filter Pane

Lecture 4: Add Filter to report

Lecture 5: Different setting of Filters

Lecture 6: Add Drop Down

Lecture 7: Controlling Page Level Filters

Lecture 8: Top/Bottom N analysis

Lecture 9: Multi Page Filter

Lecture 10: Visual Level Filters

Lecture 11: Sync Slicers

Lecture 12: Bookmarks

Lecture 13: Adding and applying bookmarks

Lecture 14: Triggering bookmarks

Lecture 15: Navigating in multiple page report using bookmarks

Introduction To Python +

Lecture 1: Installation & Environment settings

Lecture 2: Introduction to Shell

Variables, Keywords, Data Types And Identifiers. +

Lecture 1: Variables

Lecture 2: Keywords

Lecture 3: Data types

Lecture 4: Identifiers

String, List, Set, Tuple And Dictionary And Slicing +

Lecture 1: String Data types

Lecture 2: List Data types

Lecture 3: Set Data types

Lecture 4: Tuple Data types

Lecture 5: Dictionary Data types

Lecture 6: Slicing

Operators +

Lecture 1: Arithmetic Operators

Lecture 2: Logical Operators

Lecture 3: Relational Operators

Lecture 4: Bitwise Operators

Lecture 5: Assignment Operators

Lecture 6: Membership Operators

Lecture 7: Identity Operators

Control Statements +

Lecture 1: Decisional Statements

Lecture 2: Looping Statements

Lecture 3: Break, Continue and Pass

Input And Print Statements +

Lecture 1: Input statements

Lecture 2: Print statements

Functions Or Methods +

Lecture 1: Types of Functions

Lecture 2: Recursion

Lecture 3: Arguments

Lecture 4: Packing and unpacking

Oops +

Lecture 1: Class, Objects

Lecture 2: Inheritance

Lecture 3: Method Overriding

Lecture 4: Access Specifies

File Handling And Json +

Lecture 1: Flat File Handling

Lecture 2: Json

Lecture 3: Pickle

Exception Handling +

Lecture 1: Try

Lecture 2: Except and final

Lecture 3: Custom Exceptions

Lecture 4: Raising Exceptions

Lecture 5: Assertions

Comprehension +

Lecture 1: List Comprehension

Map, Filter And Lambda Expressions +

Introduction +

Lecture 1: Introduction to NumPy

Lecture 2: Python NumPy

Lecture 3: NumPy array in Python

Lecture 4: Basics of NumPy Arrays

Lecture 5: Python Lists VS NumPy Arrays

Lecture 6: NumPy – ndarray

Lecture 7: Data type Object (dtype) in NumPy Python

Creating Numpy Array +

Lecture 1: NumPy – Array Creation

Lecture 2: The Arange Method

Lecture 3: The Zero Method

Lecture 4: Create a NumPy array filled with all ones

Lecture 5: The linspace Method

Lecture 6: The eye Method

Lecture 7: NumPy Meshgrid function

Lecture 8: Creating a one-dimensional NumPy array

Lecture 9: How to create an empty and a full NumPy array?

Lecture 10: Create a NumPy array filled with all zeros

Lecture 11: Create a NumPy array filled with all ones

Lecture 12: How to generate 2-D Gaussian array using NumPy?

Lecture 1: How to create a vector in Python using NumPy

Lecture 2: Create the record array from list of individual records

Numpy Array Manipulation +

Lecture 1: Copy and View in NumPy Array

Lecture 2: How to Copy NumPy array into another array?

Lecture 3: Appending values at the end of a NumPy array

Lecture 4: How to swap columns of a given NumPy array?

Lecture 5: Insert a new axis within a NumPy array

Lecture 6: Stack the sequence of NumPy array horizontally

Lecture 7: Stack the sequence of NumPy array vertically

Lecture 8: Joining NumPy Array

Lecture 9: Combining a one and a two-dimensional NumPy Array

Lecture 10: Concatenate two arrays – np.ma.concatenate()

Lecture 11: Combined array index by index

Lecture 12: Splitting Arrays in NumPy

Lecture 13: Compare two NumPy arrays

Lecture 14: Find the union of two NumPy arrays

Matrix In Numpy +

Lecture 1: Matrix manipulation in Python

Lecture 2: NumPy Matrix Operations | Empty() Function

Lecture 3: NumPy Matrix Operations | Zeros() Function

Lecture 4: NumPy Matrix Operations | Ones() Function

Lecture 5: NumPy Matrix Operations | Eye() Function

Lecture 6: NumPy Matrix Operations | Identity() Function

Lecture 7: Adding And Subtracting Matrices In Python

Lecture 8: Matrix Multiplication In NumPy

Lecture 9: Dot product of two arrays

Lecture 10: NumPy | Vector Multiplication

Lecture 11: How to calculate dot product of two vectors in Python?

Lecture 12: Multiplication of two Matrices in Single line using NumPy in Python

Lecture 13: Get the eigen values of a matrix

Lecture 14: Calculate the determinant of a matrix using NumPy

Lecture 15: Find the transpose of the matrix

Lecture 16: Find the variance of a matrix

Reshaping NumPy Array +

Lecture 1: Reshape NumPy Array

Lecture 2: Resize the shape of the given matrix

Lecture 3: Reshape the shape of the given matrix

Lecture 4: Get the Shape of NumPy Array

Lecture 5: Change the dimension of a NumPy array

Lecture 6: Change shape and size of array in-place

Lecture 7: Flatten a Matrix in Python using NumPy

Lecture 8: Flatten a matrix – matrix.ravel()

Lecture 9: Move axes of an array to new positions

Lecture 10: Interchange two axes of an array

Lecture 11: Swap the axes a matrix

Lecture 12: Split an array into multiple sub-arrays vertically

Lecture 13: Split an array into multiple sub-arrays horizontally

Lecture 14: Give a new shape to the masked array without changing its data

Lecture 15: Squeeze the size of a matrix

Indexing Numpy Array +

Lecture 1: Basic Slicing and Advanced Indexing in NumPy Python

Lecture 2: Get selected slices of an array along mentioned axis

Lecture 3: Accessing Data Along Multiple Dimensions Arrays in Python NumPy

Lecture 4: How to access different rows of a multidimensional NumPy array?

Lecture 5: Get the indices for the lower-triangle of an (n, m) array

Arithmetic Operations On Numpy Array +

Lecture 1: Broadcasting with NumPy Arrays

Lecture 2: Estimation of Variable

Lecture 3: Python: Operations on NumPy Arrays

Lecture 4: How to use the NumPy sum function?

Lecture 5: Divide the NumPy array element wise

Lecture 6: Computes the inner product of two arrays

Lecture 7: Absolute Deviation and Absolute Mean Deviation using NumPy

Numpy And Random Data +

Lecture 1: Random sampling in NumPy | rand() function

Lecture 2: Random sampling in NumPy | random() function

Lecture 3: Random sampling in NumPy | random_sample() function

Lecture 4: Random sampling in NumPy | sample() function

Lecture 5: Random sampling in NumPy | random_integers() function

Lecture 6: Random sampling in NumPy | randint() function

Lecture 7: Get random elements from NumPy – random.choice()

Lecture 8: How to choose elements from the list with different probability using NumPy?

Lecture 9: How to get weighted random choice in Python?

Lecture 10: How to get the random positioning of different integer values?

Lecture 11: Get Random Elements from geometric distribution

Sorting And Searching In Numpy Array +

Lecture 1: Searching in a NumPy array

Lecture 2: How to sort a NumPy Array

Lecture 3: NumPy – Sorting, Searching and Counting

Lecture 4: Variations in different Sorting techniques in Python

Lecture 5: Sort a complex array

Lecture 6: Get the minimum value of masked array

Lecture 7: Sort the values in a matrix

Lecture 8: Sort the elements in the given matrix having one or more dimension

Working With Images +

Lecture 1: Create a white image using NumPy in Python

Lecture 2: Convert a NumPy array to an image

Lecture 3: How to Convert images to NumPy array?

Lecture 4: Convert an image to NumPy array and save it to CSV file using Python?

Getting Started With Pandas +

Lecture 1: Introduction to the Course

Lecture 2: Pandas Installation

Dataset Description +

Lecture 1: Loan Prediction

Lecture 2: Big Mart Sales

Read & Write Data Using Pandas +

Lecture 1: Understanding File System & shell commands

Lecture 2: Reading Excel & CSV files

Lecture 3: Writing Data using Pandas

Pandas Dataframes +

Lecture 1: What are Pandas Data frames & its operations?

Lecture 2: Selecting Columns & Rows in Pandas.

Data Exploration Using Pandas +

Lecture 1: Basic Descriptive Statistics using Pandas

Lecture 2: Plotting using Pandas

Data Manipulation Using Pandas +

Lecture 1: Renaming Column using Pandas

Lecture 2: Sorting Data in Pandas Data Frame

Lecture 3: Binning using Pandas

Lecture 4: Handling Missing Values

Lecture 5: Apply Function in Pandas for Element wise Operations +

Aggregating Data Using Pandas +

Lecture 1: Types of Aggregations in Pandas

Lecture 2: Aggregations using Pandas in action

Merging Data Using Pandas +

Lecture 1: Merging Data in Pandas Data frames

E-Commerce Platform +

Project 1: Build a digital marketplace with features like secure payments.
Full-stack development, payment gateways, database management.

Project Management App +

Project 2: Build a task tracker with sections for pending, ongoing, and completed tasks.
Tracking, data management, team collaboration, and project scheduling.

Job Application +

Project 3: Create an app for applying to jobs with filters for vacancies.
Search/filter functions, job data management, and user interface design.

Food Delivery App +

Project 4: Develop an app for ordering food with payment and tracking.
Order management, UI responsiveness, payment integration, and user experience.