Data Analytics Course | Zero To Job-Level Portfolio in 90 Days

Introduction to Data Analytics, Data Science, AI & ML

What is Data Analytics, Data Science, AI & ML

02:01:30
Interview Preparation Roadmap on Data Analytics, Data Science, AI & ML

01:26:51

Applied Statistics
Statistics is very important for Data Analyst & Data Scientist job positions. We will be discussing Applied Statistics and How we use them in Real-time with proper Pros and Cons behind every Statistics Topic with Mindmap Driven Approach.

What is Data, What is Random Variable, Types of Data?

01:08:31
Probability Distributions and Normal Distribution

01:29:44
Introduction to Statistics
Properties of Data
Sample vs Population
Types of Sampling Techniques
What is EDA
Analysing Data using EDA with Excel
Measures of Dispersion
Skewness & Normality
Correlation & Scatterplot
Barplot & Lineplot
Revision of Topics
Box & Whisker Plot
Outlier Identification
Treating Outliers
Types of Analytics
Statistical Analysis Consideration
Introduction to Inferential Statistics
Law of Large Numbers
Central Limit Theorem
Probability Distributions
Conditional Probability
Continuous Probability Distribution & Discrete Probability Distribution
PMF, CDF, PDF, PPF in Probability
Binomial Distribution
Poisson Distribution
Normal Distribution
Exponential Distribution
Laplace Distribution
Log-Normal Distribution
Standard Error
Confidence Interval Calculations
Z-Table for Confidence Interval
T-Table for Confidence Interval
Hypothesis Testing
Null & Alternate Hypothesis
Type-1 Error & Type-2 Error
2-Sample T Test
ANOVA Test
p-Value Calculations
Chi-Square Test
Project Demonstration using all the Statistical Techniques

Mastering Python[Zero to Hero]

Introduction to Python

01:02:54
Python Arithmetic Operators & Data Structures

01:04:10
Variables, Values, Data Types, Int in Python

01:01:53
List, Tuple, Set & Dict Commands in Python

57:16
Different List Methods

55:48
Different Tuple Methods

01:04:52
Different Set Methods

01:00:50
Different Dictionary & String Manipulations

50:56
Overview on Loops, IF, USD Functions, Escape Sequences, Lambda, Regular Exp

01:03:15
IF Statement, Types of Operators, Lambda, Python Modules

23:41
Loops, Break, Continue, Different Arguments

01:01:18
Functions, Escape Sequence & Lambda

52:23
Hackathon-1

54:32
Introduction to OOPS, POP vs OOP

16:16
Deep Dive into OOPS

56:53
Association, Composition, Polymorphism

50:02
OOPS Concept

36:29
Inheritance-Single, Multilevel, Hierarchical, Multiple, Encapsulation, Abstraction, Polymorphism [Brief] Basics

01:07:07
Encapsulation, Inheritance, Method Overriding, Method Overloading

53:59
Introduction to Pandas

01:05:22
Data Analysis with Pandas

01:34:29
Introduction to Numpy

57:26
Deep Into Numpy

01:03:00
Introduction to Data Visualisation

51:41
Matplotlib Deep Dive

01:00:20
Seaborn Commands

59:34
Plotly Data Visualisation

41:44
Why data Cleaning? Explanation with examples

47:05
Data Cleaning with Pandas

49:57
Regular Expression Basics

54:01

MySQL Deep Dive

Introduction to MySQL

23:29
Creating Database in MySQL

19:23
Understanding MySQL Interface

15:42
SELECT, UPDATE, DELETE, INSERT

29:03
Clauses in MYSQL Part-1

18:39
Clauses in MySQL Part-2

23:31
Clauses in MySQL Part-3
MySQL Data Handling Operations

41:21
MySQL Data Handling Operations-2

41:22
MySQL Aggregate Functions

Advanced MySQL

Data Integrity & Referential Integrity
Data Normalisation
First & Second Normal Form
Functional Dependency & Transitive Dependency
Boyce Codd Normal Form
Denormalization
Temporary Tables, CTE, Recursive CTE
When to Use Temporary Table, CTE, Recursive CTE
SubQuery in MySQL
Views in MySQL
Stored Functions
Stored Procedures
Triggers in MySQL
Create Events
In-depth DDL Commands
Different Functions in MySQL

Mastering Power BI[Zero to Hero]

Introduction to Power Bi

04:33
Creating a Bar Plot

09:58
Creating Pie Chart

03:32
Creating Ribbon Chart

02:37
Creating Scatter Plot

04:10
Creating WaterFall Charts

04:41
Creating Funnel Chart

01:59
Creating Line Plot & Area Plot

05:12
Creating Matrix & Conditional Formatting

06:09
Creating Decomposition Tree

02:26
Creating KPI Card

03:13
Creating Gauge Card

04:21
Creating Slicers

03:22
Creating Animated Bar Plot

02:37
Creating Sunburst Chart

02:34
Different Filters in Power BI

04:07
Include & Exclude Operations in Power BI

02:37
Introduction to Power Query

06:11
Groupby & Replace in Power Query

01:52
Python Visualizations in Power Query
Merge and Append Operations

02:36
Prefix, Suffix, Length

02:43
Pivot in Power Query

03:51
Python Data Cleaning with Power Query
Introduction to DAX Expressions

03:05
Creating DAX Measures

03:06
Creating DAX Columns

03:06
YTD, YoY, MTD, QTD using DAX
More DAX Expressions

02:52
Publishing Power BI Visualizations

06:39
M-Language in Power Query

End-to-End Data Analysis using Power BI & MySQL

Real-Time End-to-End Data Analytic Project Pipelines

Snowflake

Snowflake architecture, warehouses, virtual compute
Databases, schemas, micro-partitions & clustering
Loading data using COPY into stage/internal/external locations
Ingest using Snowpipe for near–real-time analytics
Transformations using Streams, Tasks & Procedures
Materialized views for analytics acceleration
Integration with Tableau/Power BI/Looker for BI
ELT with dbt + Snowflake transformations
End-to-end data pipeline project (Stage → Transform → Model → Dashboard)

Azure Data Factory

Introduction to ADF, Integration Runtime & Data Factory Architecture
Linked Services, Datasets, Dataflows, Pipelines
Copy Activity for Ingestion (Blob, ADLS, SQL, On-Prem, SaaS Sources)
Mapping Data Flows for Transformations
Data Wrangling Data Flows (Power Query Online)
Parameterisation, Variables, Expressions & Functions
Pipeline Orchestration (Activities, Triggers, Scheduling)
Control Flow (If, Switch, ForEach, Until, Web, Lookup, Set Variable)
Data Lake Gen2 Integration & Folder/File Hierarchy Design
ETL vs ELT using ADF + Synapse/Databricks
ADF Monitoring, Alerts & Operational Best Practices
Real-Time Pipelines with Event Hub / IoT Integration
End-to-End Project: Ingest → Transform → Load → Validate → Publish

Azure Data Bricks

Databricks architecture, clusters, compute, Unity Catalog
Notebook workflows with Python, SQL, PySpark
Reading/writing data from ADLS, Blob, S3, JDBC
Delta Lake fundamentals, ACID tables, time travel
Medallion architecture (Bronze → Silver → Gold)
Building ETL pipelines with PySpark transformations
Window functions & analytical transformations
SQL Warehouses for BI dashboards
End-to-end project: ingest → transform → aggregate → publish dashboards

Tableau Data Analysis

Introduction to Tableau

01:14:02
Basics of Tableau

32:02
Different Types of Charts in Tableau

30:01
Table Calculations & Parameters

21:51
Waterfall Chart, Pareto Chart, Types of Filters

25:12
LOD Expressions & Pareto Chart

16:08
MySQL Connection with Tableau

12:10
End-to-End Project using Tableau

19:25
Connecting Tableau Desktop with Server

11:35
Tableau Server, Scheduling, Alerts, Comments

04:54
Tableau Interview Preparation Process

07:34

Mastering Excel

Introduction to Excel
Basics of Excel Commands
Conditional Formatting in Excel
Different Calculations in Excel
Different Charts in Excel
Pivot Tables in Excel
Slicers in Excel
Filters in Excel
VlookUp, HLookup
Index & Match
Conditional Statements in Excel
SumIF & Count IF
Data Analysis Tool Kit
Forecasting in Excel
Correlation in Excel
Regression in Excel
What-If Analysis
Goal Seek
Data Tables
Scenario Manager
End-to-End Dashboard using Excel
Power Query in Excel

End-to-End Data Analytics using Excel

End-to-End Data Analytics using Tableau

Course Files

Level

All Levels
Last Updated

January 26, 2026
Certificate

Certificate of completion

BEPEC Help you, Setup below Lab:

Windows Laptop or MacBook(Pre-requisite)
Python above 3.11 Version
Jupyter & Cursor
MySQL or Postgres
Power BI
Azure Databricks
Snowflake

Experience Building Course on Data Analytics

Cohort Start Date

2nd Feb, 2026

Time Commitment

1.5 - 2Hours/Day

Program Duration

3 Months

Learning Format

Live Classes with Experience Building

We Help You Make Career Switch!

60 - 100% Salary Hike

500+ Hiring Partners

30000+ Career Transitions

What I will learn?

Updated Data Analytics Course Syllabus

Designed for 2026 Job Market with Job-Level Experience

Introduction to Data Analytics, Data Science, AI & ML

What is Data Analytics, Data Science, AI & ML

Interview Preparation Roadmap on Data Analytics, Data Science, AI & ML

Applied Statistics Statistics is very important for Data Analyst & Data Scientist job positions. We will be discussing Applied Statistics and How we use them in Real-time with proper Pros and Cons behind every Statistics Topic with Mindmap Driven Approach.

What is Data, What is Random Variable, Types of Data?

Probability Distributions and Normal Distribution

Introduction to Statistics

Properties of Data

Sample vs Population

Types of Sampling Techniques

What is EDA

Analysing Data using EDA with Excel

Measures of Dispersion

Skewness & Normality

Correlation & Scatterplot

Barplot & Lineplot

Revision of Topics

Box & Whisker Plot

Outlier Identification

Treating Outliers

Types of Analytics

Statistical Analysis Consideration

Introduction to Inferential Statistics

Law of Large Numbers

Central Limit Theorem

Probability Distributions

Conditional Probability

Continuous Probability Distribution & Discrete Probability Distribution

PMF, CDF, PDF, PPF in Probability

Binomial Distribution

Poisson Distribution

Normal Distribution

Exponential Distribution

Laplace Distribution

Log-Normal Distribution

Standard Error

Confidence Interval Calculations

Z-Table for Confidence Interval

T-Table for Confidence Interval

Hypothesis Testing

Null & Alternate Hypothesis

Type-1 Error & Type-2 Error

2-Sample T Test

ANOVA Test

p-Value Calculations

Chi-Square Test

Project Demonstration using all the Statistical Techniques

Mastering Python[Zero to Hero]

Introduction to Python

Python Arithmetic Operators & Data Structures

Variables, Values, Data Types, Int in Python

List, Tuple, Set & Dict Commands in Python

Different List Methods

Different Tuple Methods

Different Set Methods

Different Dictionary & String Manipulations

Overview on Loops, IF, USD Functions, Escape Sequences, Lambda, Regular Exp

IF Statement, Types of Operators, Lambda, Python Modules

Loops, Break, Continue, Different Arguments

Functions, Escape Sequence & Lambda

Hackathon-1

Introduction to OOPS, POP vs OOP

Deep Dive into OOPS

Association, Composition, Polymorphism

Applied Statistics
Statistics is very important for Data Analyst & Data Scientist job positions. We will be discussing Applied Statistics and How we use them in Real-time with proper Pros and Cons behind every Statistics Topic with Mindmap Driven Approach.