Data Analytics

What you'll learn

This comprehensive Data Analytics course is designed to develop strong analytical thinking and practical skills required to turn raw data into actionable business insights. You will learn how to collect, clean, process, and analyze data using industry-standard tools and techniques widely used in modern organizations.

Throughout the program, you will gain hands-on experience with Excel, SQL, Python, Statistics and data visualization tools to perform exploratory data analysis, create interactive dashboards, and generate meaningful reports. Real-world case studies and projects will help you understand how data analytics supports decision-making across domains such as finance, marketing, healthcare, and operations.

By the end of this course, you will be confident in interpreting data, identifying trends, and presenting insights effectively, making you job-ready for roles such as Data Analyst, Business Analyst, and Reporting Analyst.

  • Fundamentals of Data Analysis and data-driven decision making.
  • Data cleaning, preprocessing, and transformation techniques.
  • SQL for querying databases and extracting meaningful insights.
  • Exploratory Data Analysis (EDA) using Excel and Python.
  • Creating interactive dashboards using Power BI / Tableau.
  • Data visualization and storytelling with real-world datasets.
  • Working with KPIs, reports, and business metrics.
  • Presenting actionable insights for business and organizational growth.

Course Content

  • About Python
  • How to Python Work
  • Python setup & environment
  • Python Virtual Machine
  • Installing Python (Anaconda / Standard)
  • Running Python scripts
  • Python Output/print function
  • Syntax, Comments
  • Variables, Data Types, Built in Data Types
  • Type Casting & Type Checking
  • Python Literal, Operators, Keywords and Identifiers, User Input
  • If, Elif, Else
  • Nested Conditions
  • Loops (For, While)
  • Break, Continue, Pass
  • Comprehensions
  • String : String indexing, String slicing, Edit and delete a string, Operations on String, Common String functions
  • List : Create and access a add items, Edit items in a list, Deleting items from a list, Arithmetic, membership and loop operations on a List, Various List functions, List comprehension, Zip() function
  • Tuple : Create and access a tuple, Can we edit and add items to a tuple?, Deletion, Operations on tuple, Tuple functions, List vs tuple, Tuple unpacking, Zip () on tuple
  • Set : Create and access a set, Can we edit and add items to a set?, Deletion, Operations on set, set functions, Frozen set (immutable set), Set comprehension
  • Dictionary : Create dictionary, Accessing items, Add, remove, edit key-value pairs, Operations on dictionary, Dictionary functions, Nested comprehension
  • Defining & Calling Functions
  • Parameters & Arguments
  • Return Values
  • Default & Keyword Arguments
  • *args and **kwargs
  • Lambda Functions
  • Scope (local, global)
  • Nested functions
  • Introduction to OOP
  • Classes & Objects
  • Attributes & Methods
  • Constructor (__init__), self Keyword
  • Encapsulation, Abstraction, Inheritance, Polymorphism
  • Special Methods (__str__, __add__, etc.)
  • How objects access attributes
  • Iterators and Iterable Objects in Python
  • Decorators for Code Reusability and Abstraction
  • Generators for Efficient Data Processing
  • Text Processing Using Regular Expressions (re Module)
  • Package Installation (pip)
  • Importing Modules
  • Standard Library (os, sys, math, random, datetime)
  • Virtual Environments (venv, pipenv, conda)
  • Reading & Writing Files (txt, json)
  • File modes (r, w, a, rb, wb)
  • Exception Handling with Files
  • JSON Handling (json module)
  • Pickle
  • Try, Except, Finally
  • Raise Exceptions
  • Introduction to Arrays
  • List vs Arrays
  • Array Operations
  • What is Time Complexity?
  • Big-O Notation
  • Common Complexity Examples
  • Introduction to Numpy
  • Numpy Fundamentals
  • Numpy Basics
  • Array Indexing & Slicing
  • Array Operations
  • Mathematical Functions
  • Array Shape & Reshaping
  • Stacking & Splitting
  • Random Module
  • Advanced Numpy
  • Introduction to Pandas
  • Pandas Data Structures
  • Data Inspection
  • Data Selection & Indexing
  • Data Cleaning
  • Data Transformation
  • Sorting & Ranking
  • Grouping & Aggregation
  • Combining & Merging Data
  • Reshaping & Pivoting
  • Advanced Pandas
  • Matplotlib : 2D Plot / 3D Plot (Line plot, Scatter plot, Bar plot, Stem plot, Step plot, Histogram plot, Box plot, Pie chart, Subplots, Violin plot, Stacked bar chart, Contour plot, Contourf plot, Heatmap)
  • Seaborn : Line plot, Scatter plot, Dis plot, Cat plot, Rel plot, Hist plot, Kde plot, Rug plot, Bar plot, Violin plot, Heatmap, Cluster map, Strip plot, Joint plot
  • Plotly : Plotly Go, Plotly Express, Dash, Line plots, Scatter plots, Bar charts, Pie charts, Donut charts, Bubble charts, Histogram, Box plot, Violin plot, Strip plot, Waterfall charts
  • Dashboards with Plotly : Basic Dash app structure, Linking Plotly figures to Dash, Advance Dash app
  • All functions of plots
  • All changing styles of plots
  • Project using Dashboard with Plotly
  • Requests
  • BeautifulSoup
  • Selenium
  • Introduction to Statistics
  • Types of Statistics
  • Basics of Data : Population vs. Sample, Raw data vs. Grouped data, Types of data, Frequency distribution
  • Measures of Central Tendency : Mean, Median, Mode, Weighted Mean, Trimmed Mean (robust measure)
  • Measures of Dispersion (Spread of Data) : Range, Standard Deviation, Variance, Coefficient of Variation, Interquartile Range (IQR)
  • Measures of Position (Relative Standing) : Percentiles, Quartiles (Q1, Q2, Q3), Z-scores (Standard scores)
  • Measures of Shape : Skewness
  • Data Visualization in Descriptive Statistics : Categorical – Categorical, Numerical – Numerical, Categorical – Numerical
  • Hypothesis Testing
  • Null and alternate hypothesis
  • Type I & Type II errors
  • p-value interpretation
  • One-tailed vs. Two-tailed tests
  • Z-test, T-test, Chi-square test
  • ANOVA
  • 2D density plots
  • Normal Distribution
  • Standard Normal Variate
  • Properties of Normal Distribution
  • Skewness
  • CDF of Normal Distribution
  • QQ plot
  • Uniform Distribution
  • Log-normal distribution
  • Power Transformer
  • Log Transform
  • Box-Cox Transform
  • Yeo-Johnson Transformation
  • Data Understanding
  • Data Collection & Import
  • Data Cleaning
  • Feature Engineering
  • Statistical Testing
  • Visualization & Reporting
  • What is Git?
  • What is VCS/SCM?
  • How Git works?
  • Installing git
  • Creating and Cloning repository
  • add, commit, add, gitignore
  • seeing commits
  • Nonlinear Development (Branching)
  • Merging branches
  • Working with a remote repository
  • Introduction to Databases & SQL : What is a Database?, CRUD operations, Types of Database, DBMS
  • SQL : Database, Table, Row, Column concepts, SQL Data Types, Creating & Dropping Databases, Creating & Dropping Tables, Altering Tables
  • CRUD Operations : INSERT INTO, SELECT, UPDATE, DELETE
  • Filtering & Sorting : WHERE, Logical Operators, Comparison Operators, Sorting Data, Limiting Results
  • SQL Functions : Aggregate Functions, String Functions, Date & Time Functions, Mathematical Functions
  • Grouping Data, GROUP BY, HAVING
  • SQL Joins : INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN, CROSS JOIN, SELF JOIN
  • SQL Subqueries : Subquery in SELECT, Subquery in WHERE, Nested Queries
  • SQL Set Operations : UNION vs. UNION ALL, EXCEPT, INTERSECT
  • Keys : Primary Key, Foreign Key, Unique Key, Candidate Key, Super Key, Composite Key, Surrogate Key
  • Triggers : What are Triggers?, BEFORE & AFTER Triggers
  • Advanced Functions : Window Functions, Pivot, Case Statements
  • SQL Projects
  • Introduction to Power BI
  • Data Sources & Connectivity
  • Data Cleaning with Power Query
  • Data Modeling
  • DAX (Data Analysis Expressions)
  • Data Visualization
  • Dashboard Design
  • Advanced Analytics
  • Power BI Project
  • Introduction to Data Visualization & Tableau
  • Data Connections
  • Data Preparation
  • Data Modeling
  • Calculations in Tableau
  • Data Visualization
  • Dashboard Design
  • Advanced Analytics
  • Tableau Project
  • Introduction to FastAPI
  • Background Processing
  • Creating your first FastAPI app
  • Path parameters
  • Query parameters
  • Request body
  • Response models
  • GET, POST, PUT, DELETE
  • Flow Diagram
  • Databases
  • Request Handling
  • Testing FastAPI
  • Introduction to MongoDB
  • MongoDB Basics : Database, collection, document structure, CRUD operations
  • Querying Documents
  • Data Modeling in MongoDB
  • Aggregation
  • Advanced MongoDB
  • Excel Basics
  • Data entry and basic functions
  • Logical and data validation functions
  • Lookup and reference functions
  • Text manipulation functions
  • Excel tables and structured data
  • Pivot tables for data analysis
  • Advanced pivot table techniques
  • Data visualization basics
  • Advance charting techniques
  • Conditional formatting and sparklines
  • Dashboard design principles
  • Advance dashboarding techniques
  • All Topics Interview Questions & Answers
  • All Topics Coding Practice
  • Real-world Problem Solving
  • Total Duration : 250+ hours
  • Lectures : 135+
  • Skill Level : Expert
  • Language : Hindi
  • Industry Based Course Curriculum
  • Weekdays & Weekend Classes
  • Resume & LinkedIn Profile Building
  • Regular Assessments
  • Quiz
  • Hands-on Practice & Projects
  • Globally Recognized Certification
  • Interview Preparation & Mock Interview Session
  • 100% Job Assistance
  • 24/7 Student Support
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