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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.

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25th April 2025

6 Months

Live + Recorded

English

Syllabus for Data Analytics

Python Basic

Statistics

Machine Learning

Deep learning

NLP

Genrative Ai

Power BI [Optional]

Python Basics+

Lecture 1 : Introduction to Python

Lecture 2 : Python Objects, Number & Booleans, Strings.

Lecture 3 : Container Objects, Mutability Of Objects

Lecture 4 : Operators

Lecture 5 : Python Type Conversion

Lecture 6 : Conditions (If Else, If-Elif-Else)

Lecture 7 : Loops (While, For)

Lecture 8 : Break And Continue Statement And Range Function

Lecture 9 : Python Namespace

Data Types & Functions+

Lecture 1 : Basic Data Structure In Python

Lecture 2 : String Object Basics

Lecture 3 : String Inbuilt Methods

Lecture 4 : Splitting And Joining Strings

Lecture 5 : String Format Functions

Lecture 6 : List Methods

Lecture 7 : List As Stack And Queues

Lecture 8 : List Comprehensions

Lecture 9 : Tuples, Sets & Dictionary Object Methods

Lecture 10 : Dictionary Comprehensions

Lecture 11 : Dictionary View Objects

Lecture 12 : Functions Basics, Parameter Passing, Iterators.

Lecture 13 : Generator Functions

Lecture 14 : Lambda Functions

Lecture 15 : Map, Reduce, Filter Functions.

OOPS+

Lecture 1 : OOPS Basic Concepts.

Lecture 2 : Creating Classes

Lecture 3 : Pillars Of OOPS

Lecture 4 : Inheritance

Lecture 5 : Polymorphism

Lecture 6 : Encapsulation

Lecture 7 : Abstraction

Lecture 8 : Decorator

Lecture 9 : Class Methods And Static Methods

Lecture 10 : Special (Magic/Dunder) Methods

Lecture 11 : Property Decorators - Getters, Setters, And Deletes

Files & Exceptional Handling+

Lecture 1 : Working With Files

Lecture 2 : Reading And Writing Files

Lecture 3 : Buffered Read And Write

Lecture 4 : Other File Methods.

Lecture 5 : Logging, Debugger

Lecture 6 : Modules And Import Statements

Lecture 7 : Exceptions Handling With Try-Except

Lecture 8 : Custom Exception Handling

Lecture 9 : List Of General Use Exception

Lecture 10 : Best Practice Exception Handling

Database & Web API+

Lecture 1 : Multithreading

Lecture 2 : Multiprocessing

Lecture 3 : Mysql

Lecture 4 : Mongo Db

Lecture 5 : What Is Web Api

Lecture 6 : Difference B/W Api And Web Api

Lecture 7 : Rest And Soap Architecture

Lecture 8 : Restful Services

Python Libraries+

Lecture 1 : Numpy

Lecture 2 : Pandas

Lecture 3 : Matplotlib

Lecture 4 : Seaborn

Lecture 5 : Plotly

Lecture 6 : Bokeh

Statistics Basics +

Lecture 1 : Introduction To Basic Statistics Terms

Lecture 2 : Types Of Statistics

Lecture 3 : Types Of Data

Lecture 4 : Levels Of Measurement

Lecture 5 : Measures Of Central Tendency

Lecture 6 : Measures Of Dispersion

Lecture 7 : Random Variables

Lecture 8 : Set

Lecture 9 : Skewness

Lecture 10 : Covariance And Correlation

Statistics Advance +

Lecture 1 : Probability Density/Distribution Function

Lecture 2 : Types Of The Probability Distribution

Lecture 3 : Binomial Distribution

Lecture 4 : Poisson Distribution

Lecture 5 : Normal Distribution (Gaussian Distribution)

Lecture 6 : Probability Density Function And Mass Function

Lecture 7 : Cumulative Density Function

Lecture 8 : Examples Of Normal Distribution

Lecture 9 : Bernoulli Distribution

Lecture 10 : Uniform Distribution

Lecture 11 : Z Stats

Lecture 12 : Central Limit Theorem

Lecture 13 : Estimation

Lecture 14 : A Hypothesis

Lecture 15 : Hypothesis Testing’S Mechanism

Lecture 16 : P-Value

Lecture 17 : T-Stats

Lecture 18 : Student T Distribution

Lecture 19 : T-Stats Vs. Z-Stats: Overview

Lecture 20 : When To Use A T-Tests Vs. Z-Tests

Lecture 21 : Type 1 & Type 2 Error

Lecture 22 : Confidence Interval(Ci)

Lecture 23 : Confidence Intervals And The Margin Of Error

Lecture 24 : Interpreting Confidence Levels And Confidence Intervals

Lecture 25 : Chi-Square Test

Lecture 26 : Chi-Square Distribution Using Python

Lecture 27 : Chi-Square For Goodness Of Fit Test

Lecture 28 : When To Use Which Statistical Distribution?

Lecture 29 : Analysis Of Variance (Anova)

Lecture 30 : Anova Three Type

Lecture 31 : Partitioning Of Variance In The Anova

Lecture 32 : Calculating Using Python

Lecture 33 : F-Distribution

Lecture 34 : F-Test (Variance Ratio Test)

Lecture 35 : Determining The Values Of F

Lecture 36 : F Distribution Using Python

Introduction to ML +

Lecture 1 : AI vs ML vs DL vs DS

Lecture 2 : Supervised, Unsupervised

Lecture 3 : Semi-Supervised, Reinforcement Learning

Lecture 4 : Train, Test, Validation Split

Lecture 5 : Overfitting, Under Fitting

Lecture 6 : Bias Vs Variance

Feature Engineering +

Lecture 1 : Handling Missing Data

Lecture 2 : Handling Imbalanced Data

Lecture 3 : Up-Sampling

Lecture 4 : Down-Sampling

Lecture 5 : Smote

Lecture 6 : Data Interpolation

Lecture 7 : Handling Outliers

Lecture 8 : Filter Method

Lecture 9 : Wrapper Method

Lecture 10 : Wrapper Method

Lecture 11 : Embedded Methods

Lecture 12 : Feature Scaling

Lecture 13 : Standardization

Lecture 14 : Mean Normalization

Lecture 15 : Min-Max Scaling

Lecture 16 : Unit Vector

Lecture 17 : Feature Extraction

Lecture 18 : Pca (Principle Component Analysis)

Lecture 19 : Data Encoding

Lecture 20 : Nominal Encoding

Lecture 21 : One Hot Encoding

Lecture 22 : One Hot Encoding With Multiple Categories

Lecture 23 : Mean Encoding

Lecture 24 : Ordinal Encoding

Lecture 25 : Label Encoding

Lecture 26 : Target Guided Ordinal Encoding

Lecture 27 : Covariance

Lecture 28 : Correlation Check

Lecture 29 : Pearson Correlation Coefficient

Lecture 30 : Spearman’S Rank Correlation

Lecture 31 : Vif

Lecture 32 : Feature Selection

Lecture 33 : Recursive Feature Elimination

Lecture 34 : Backward Elimination

Lecture 35 : Forward Elimination

Exploratory Data Analysis +

Lecture 1 : Feature Engineering And Selection.

Lecture 2 : Analyzing Movie Reviews Sentiment.

Lecture 3 : Customer Segmentation And Cross Selling Suggestions.

Lecture 4 : Forecasting Stock And Commodity Prices

Regression +

Lecture 1 : Linear Regression

Lecture 2 : Gradient Descent

Lecture 3 : Multiple Linear Regression

Lecture 4 : Polynomial Regression

Lecture 5 : R Square And Adjusted R Square

Lecture 6 : Rmse , Mse, Mae Comparison

Lecture 7 : Regularized Linear Models

Lecture 8 : Regularized Linear Models

Lecture 9 : Ridge Regression

Lecture 10 : Lasso Regression

Lecture 11 : Lasso Regression

Lecture 12 : Elastic Net

Lecture 13 : Logistics Regression In-Depth Intuition

Lecture 14 : In-Depth Mathematical Intuition

Lecture 15 : In-Depth Geometrical Intuition

Lecture 16 : Hyper Parameter Tuning

Lecture 17 : Grid Search Cv

Lecture 18 : Randomize Search Cv

Lecture 19 : Data Leakage

Lecture 20 : Confusion Matrix

Lecture 21 : Explanation Precision,Recall,F1 Score ,Roc, Auc

Lecture 22 : Best Metric Selection

Lecture 23 : Multiclass Classification In Lr

Decision Trees & SVM +

Lecture 1 : Gini Index

Lecture 2 : Entropy

Lecture 3 : Information Gain

Lecture 4 : Advantages & Disadvantages

Lecture 5 : Pruning

Lecture 6 : Visualization

Lecture 7 : Gini Vs Entropy

Lecture 8 : Hyper Parameter Tuning

Lecture 9 : SVM Introduction

Lecture 10 : Maximal Margin Classifier

Lecture 11 : Support Vector Classifier

Lecture 12 : Support Vector Machines

Lecture 13 : Kernel SVM

Neural Network: A Simple Perceptron +

Lecture 1 : Introduction to Deep Learning

Lecture 2 : Neural Network Overview And Its Use Case.

Lecture 3 : Detail Mathematical Explanation

Lecture 4 : Various Neural Network Architect Overview.

Lecture 5 : Various Neural Network Architect Overview.

Lecture 6 : Use Case Of Neural Network In Nlp And Computer Vision.

Lecture 7 : Activation Function -All Name

Lecture 8 : Multilayer Network.

Lecture 9 : Loss Functions. - All 10

Lecture 10 : Forward And Backward Propagation.

Lecture 11 : Optimizers. - All 10

Lecture 12 : Forward And Backward Propagation.

Lecture 13 : Vanishing Gradient Problem

Lecture 14 : Weight Initialization Technique

Lecture 15 : Exploding Gradient Problem

Lecture 16 : Visualization Of Neural Network

Lecture 17 : The Learning Mechanism

Deep Learning Frameworks +

Lecture 1 : Colab Pro Setup

Lecture 2 : TensorFlow Installation 2.0

Lecture 3 : TensorFlow 2.0 Function

Lecture 4 : TensorFlow 2.0 Neural Network Creation

Lecture 5 : Mini Project In TensorFlow

Lecture 6 : Tensor space

Lecture 7 : Tensor board Integration

Lecture 8 : TensorFlow Playground

Lecture 9 : Netron

Lecture 10 : Pytorch Installation

Lecture 11 : Pytorch Functional Overview

Lecture 12 : Pytorch Neural Network Creation

CNN Architecture +

Lecture 1 : CNN Fundamentals

Lecture 2 : CNN Explained In Detail - CNNexplainer, Tensor space

Lecture 3 : Various CNN Based Architecture

Lecture 4 : Training CNN From Scratch

Lecture 5 : Building Webapps For CNN

Lecture 6 : Deployment In AWS

Image Classifications +

Lecture 1 : Various CNN Architecture With Research Paper And Mathematics

Lecture 2 : Lenet-5 Variants With Research Paper And Practical

Lecture 3 : Alexnet Variants With Research Paper And Practical

Lecture 4 : Googlenet Variants With Research Paper And Practical

Lecture 5 : Transfer Learning

Lecture 6 : VGGNet Variants With Research Paper And Practical

Lecture 7 : ResNet Variants With Research Paper And Practical

Lecture 8 : Inception Net Variants With Research Paper And Practical

RCNN & YOLO +

Lecture 1 : Intro to RCNN

Lecture 2 : Fast & Faster RCNN

Lecture 3 : Overview of all previous version

Lecture 4 : Introduction To Yolov9

Lecture 5 : Installation Of Yolov9

Lecture 6 : Data Annotation & Preparation

Lecture 7 : Download Data & Configure Path

Lecture 8 : Download & Configure Pretrained Weight

Lecture 9 : Start Model Training

Lecture 10 : Evaluation Curves Yolov9

Lecture 11 : Inferencing Using Trained Model

Lecture 12 : Evaluation Curves TFOD2

Lecture 13 : Inferencing Using Trained Model

Detectron2 & TFOD 2 +

Lecture 1 : Introduction To Detectron2

Lecture 2 : Installation Of Detectron2

Lecture 3 : Data Annotation & Preparation

Lecture 4 : Download Data & Configure Path

Lecture 5 : Download & Configure Pretrained Weight

Lecture 6 : Start Model Training

Lecture 7 : Evaluation Curves Detectron2

Lecture 8 : Inferencing Using Trained Model

Image Segmentation & Mask R-CNN Practical +

Lecture 1 : Introduction To TFOD2

Lecture 2 : Installation Of TFOD2

Lecture 3 : Data Annotation & Preparation

Lecture 4 : Download Data & Configure Path

Lecture 5 : Download & Configure Pretrained Weight

Lecture 6 : Start Model Training

Lecture 7 : Evaluation Curves TFOD2

Lecture 8 : Inferencing Using Trained Model

Lecture 9 : Scene Understanding

Lecture 10 : More To Detection

Lecture 11 : Need Accurate Results

Lecture 12 : Segmentation

Lecture 13 : Types Of Segmentation

Lecture 14 : Understanding Masks

Lecture 15 : Mask R-CNN

Lecture 16 : From Bounding Box To Polygon Masks

Lecture 17 : Mask R-CNN Architecture

Object Tracking +

Lecture 1 : What Is Object Tracking?

Lecture 2 : Doing Annotations Or Labeling Data

Lecture 3 : Registering Dataset For Training

Lecture 4 : Selection Of Pretrained Model From Model Zoo

Lecture 5 : Let's Start Training

Lecture 6 : Stop Training Or Resume Training

Lecture 7 : Inferencing Using The Custom Trained Model In Colab

Lecture 8 : Evaluating The Model

Lecture 9 : Kalman Filter

Lecture 10 : Data Preprocessing

Lecture 11 : Using YOLO For Detection

Lecture 12 : Preparing Deep SORT With YOLO

Lecture 13 : Combining Pipelines For Tracking & Detection

GAN +

Lecture 1 : Introduction To GANs

Lecture 2 : GAN Architecture

Lecture 3 : Discriminator

Lecture 4 : Generator

Lecture 5 : Controllable Generation

Lecture 6 : WGANs

Lecture 7 : DCGANs

Lecture 8 : StyleGANs

Lecture 9 : GAN Practical's Implementation

NLP Introduction +

Lecture 1 : Overview Computational Linguistic

Lecture 2 : History Of Nlp

Lecture 3 : Why Nlp

Lecture 4 : Use Of Nlp

Text Processing for NLP +

Lecture 1 : Text Processing

Lecture 2 : Understanding Regex

Lecture 3 : Text Normalization

Lecture 4 : Word Count

Lecture 5 : Frequency Distribution

Lecture 6 : String Tokenization

Lecture 7 : Annotator Creation

Lecture 8 : Sentence Processing

Lecture 9 : Lemmatization In Text Processing

Lecture 10 : Word Embedding

Lecture 11 : Co-Occurrence Vectors

Lecture 12 : Word2Vec

Lecture 13 : Doc2Vec

NLP Libraries & Networks +

Lecture 1 : Nltk

Lecture 2 : Text Blob

Lecture 3 : Recurrent Neural Networks

Lecture 4 : Long Short Term Memory (Lstm)

Lecture 5 : Bi Lstm

Lecture 6 : Stacked Lstm

Lecture 7 : Gru Implementation

Attention Based Model & Transfer Learning +

Lecture 1 : Seq 2 Seq

Lecture 2 : Encoders And Decoders

Lecture 3 : Attention Mechanism

Lecture 4 : Self-Attention

Lecture 5 : Introduction To Transformers

Lecture 6 : Bert Model

Lecture 7 : Gpt2 Model

Introduction of Generative AI +

Lecture 1 : Introduction of Generative AI and its use cases

Lecture 2 : Probabilistic modeling and generative models

Generative AI for Text Generation +

Lecture 1 : Autoencoders and variational autoencoders

Lecture 2 : RNN & LSTM

Lecture 3 : Generative pre-trained transformers

Generative AI for Machine Translation +

Lecture 1 : SMT & NMT

Lecture 2 : Attention mechanisms in NMT

Lecture 3 : Generative pre-trained transformers

Generative AI for Creative Content Generation +

Lecture 1 : Poetry generation and music composition using generative AI

Lecture 2 : Creative writing and storytelling with generative AI

Lecture 3 : Ethical considerations and biases in creative content generation

Advanced Topics in Generative AI with NLP +

Lecture 1 : Reinforcement learning in generative AI for NLP

Lecture 2 : Multimodal generative models for NLP

Lecture 3 : Generative AI for natural language understanding (NLU)

LLMs: Use Cases and Potentials +

Lecture 1 : Introduction to Large Language Models (LLMs)

Lecture 2 : Exploring the diverse applications of LLMs

Lecture 3 : Understanding the potential of LLMs to transform various industries

Hugging Face Hub: A Gateway to Generative AI +

Lecture 1 : Navigating the Hugging Face Hub and its vast repository of pre-trained models

Lecture 2 : Leveraging Hugging Face Hub's tools and resources for generative AI tasks

Lecture 3 : Exploring real-world applications built upon Hugging Face Hub's models

Prompt Engineering: The Art of Guiding LLMs +

Lecture 1 : Understanding the role of prompts in guiding LLMs

Lecture 2 : Crafting effective prompts to generate desired outputs

Lecture 3 : Exploring advanced prompt engineering techniques

RAG: A Versatile Tool for Generative AI +

Lecture 1 : Introduction to the Retrieval Augmented Generation (RAG)

Lecture 2 : Discussing frameworks to achieve RAG

Lecture 3 : Utilizing RAG for various generative AI tasks

Lecture 4 : Understanding the advantages and limitations of RAG

Fine-tuning: Unveiling the Potential of LLMs +

Lecture 1 : Delving into the concept of fine-tuning LLMs

Lecture 2 : Understanding the different approaches to fine-tuning LLMs

Lecture 3 : Exploring real-world examples of fine-tuning for specific applications

Project : On prompt image and caption generator +

Lecture 1 : Exploring the OpenAI library in python

Lecture 2 : Image Generation with OpenAI library

Lecture 3 : Image captioning with OpenAI library

Project : Customised Chat bot +

Lecture 1 : Exploring LangChain and Chainlit

Lecture 2 : Creative Chatbot using LangChain and Chainlit

Project : Genei: An Alexa like assistant +

Lecture 1 : Alexa like Assistant

Custom Chat GPT +

Lecture 1 : A custom chat bot which can create images based on the given input

Introduction to Power BI +

Lecture 1 : What is Power BI

Lecture 2 : Power BI Products

Lecture 3 : Components of Power BI

Lecture 4 : Why Power BI - PowerBI Vs Other BI Tools

Lecture 5 : Building Blocks of Power BI

Lecture 6 : Power BI Process

Lecture 7 : Power BI Desktop Installation

Lecture 8 : User Interface

Lecture 9 : Connecting to a local data Sources

Lecture 10 : Power Query User Interface

Lecture 11 : Data types

Lecture 12 : Home Tab

Lecture 13 : Transform Tab

Lecture 14 : Add Column

Lecture 15 : Other Transformations

Visualizations +

Lecture 1 : Bar/Column charts

Lecture 2 : Pie & Donut charts

Lecture 3 : Waterfall chart

Lecture 4 : Tree Map

Lecture 5 : Table & Matrix visualization

Lecture 6 : Ribbon chart

Lecture 7 : Funnel

Lecture 8 : Line

Lecture 9 : Slicers

Lecture 10 : KPI

Lecture 11 : Area

Lecture 12 : Cards

Lecture 13 : Scatter

Lecture 14 : Gauge Chart

Lecture 15 : Maps

Lecture 16 : Custom Visuals (Sankey, Sunburst, Playaxis, Wordcloud, Butterfly)

Data Modeling +

Lecture 1 : Data models, Importance

Lecture 2 : Schema

Lecture 3 : Creating and Managing relationships

Lecture 4 : Optimizing Data Models

DAX +

Lecture 1 : DAX and its Importance

Lecture 2 : Row & Filter context

Lecture 3 : Measures & Calculated columns

Lecture 4 : SUM function

Lecture 5 : SUMX function

Lecture 6 : MIN function

Lecture 7 : MINX function

Lecture 8 : MAX function

Lecture 9 : MAXX function

Lecture 10 : AVERAGE function

Lecture 11 : AVERAGEX function

Lecture 12 : COUNT function

Lecture 13 : DISTINCTCOUNT function

Lecture 14 : ALLSELECT function

Lecture 15 : FILTER function

Lecture 16 : CALCULATE function

Lecture 17 : CROSSFILTER function

Lecture 18 : RELATED function

Lecture 19 : CALENDAR function

Lecture 20 : DATE function

Lecture 21 : DATEDIFF function

Lecture 22 : MONTH function

Lecture 23 : WEEKDAY function

Lecture 24 : YEAR function

Lecture 25 : DATEADD function

Lecture 26 : DATESBETWEEN function

Lecture 27 : TOTALMTD function

Lecture 28 : TOTALQTD function

Lecture 29 : TOTALYTD function

Lecture 30 : CONCATENATE function

Lecture 31 : CONCATENATEX function

Lecture 32 : FORMAT function

Lecture 33 : LEFT function

Lecture 34 : RIGHT function

Lecture 35 : TRIM function

Lecture 36 : FIND function

Lecture 37 : SEARCH function

Lecture 38 : AND function

Lecture 39 : OR function

Lecture 40 : NOT function

Lecture 41 : FALSE function

Lecture 42 : TRUE function

Connectivity +

Lecture 1 : Introduction to using Excel data in Power BI

Lecture 2 : Exploring live connections to data with Power BI

Lecture 3 : Connecting directly to Database

Lecture 4 : Import Power View and Power Pivot to Power BI

Lecture 5 : Power BI Publisher for Excel

Lecture 6 : Content packs

Lecture 7 : Introducing Power BI Mobile

Creating Reports +

Lecture 1 : What is a report ?

Lecture 2 : Filters ; Visual level filter

Lecture 3 : Filters ; Page level filter

Lecture 4 : Filters ; Report level filter

Lecture 5 : Filters ; Drill through filter

Lecture 6 : Include, Exclude Filter

Lecture 7 : Formatting a report

Power BI Architecture +

Lecture 1 : Creating an account on PBI service

Lecture 2 : Introduction to PBI Service

Lecture 3 : Publishing a report to PBI Service

Lecture 4 : Creating a dashboard

Lecture 5 : Creating a gateway

Lecture 6 : Scheduling a refresh

Lecture 7 : Row level security

Lecture 8 : Introduction

Lecture 9 : M Language Functions

Your Complete Learning Experience

Industry-Oriented Curriculum

Comprehensive Learning Content

Weekend Live Sessions

Capstone Project

Practice Exercises

Assignments and Projects

Live Doubt Resolution Sessions

SME Support Session

Certification of Completion

Career Guidance & Interview Preparation

Email Support

Peer Networking

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