Data Analytics Mod - Engineer Sahab Education

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Data Science Analytics | Begin Your Career in Data Analytics

Data analytics Bootcamp
Duration 16 Weeks

Kickstart your data analytics career with our intensive bootcamp in Indore. Gain hands-on experience with tools like Excel, SQL, Python, and Power BI, guided by industry experts. Work on real-world projects, build a strong portfolio, and acquire the skills sought after in today's data-driven industries.

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What you’ll learn in 16 Weeks?

In just four Months, our Data Analytics Bootcamp will equip you with essential skills in Excel, SQL, Python, and Power BI. You'll gain hands-on experience working on real-world projects,
mastering data cleaning, visualization, and statistical analysis. By the end of the program, you'll have a solid foundation to launch your career in data analytics.

Module 1

Introduction to Data Analytics

  • What is Data Analytics?
  • Analytics Lifecycle & Types
  • Tools and Technologies Used
  • Real-world Use Cases
  • Career Paths and Roles in Analytics
Module 2

Python Programming Fundamentals

  • Why Learn Python?
  • Setting Up Python and IDEs (Jupyter, VS Code)
  • Comments, indentation
  • Variables, Data Types, and Operators
  • String Handling and Formatting
  • Conditional Statements
  • Loops – For and While
  • Functions – Built-in & User-Defined
  • Modules and Packages
  • Common Python Errors & Basic Debugging
Module 3

Python – Data Structures & Functional Programming

  • Lists, Tuples, Sets, Dictionaries
  • List & Dictionary Comprehension
  • File Handling (Text, CSV)
  • Exception Handling
  • Map, Filter, Reduce
  • Object-Oriented Programming Basics
  • Installing & Managing Libraries with PIP
Module 4

Data Handling with Pandas & NumPy

  • Pandas Overview: Series & DataFrames
  • Reading/Writing CSV, Excel, and Text Files
  • Indexing, Filtering, and Sorting Data
  • Merging, Joining, Concatenating DataFrames
  • Handling Missing and Duplicate Data
  • Group By, Aggregations, Pivot Tables
  • Date/Time Handling in Pandas
  • NumPy Arrays – Creation, Indexing, Slicing
  • Array Manipulation & Math Operations
  • Statistical Functions in NumPy
Module 5

Data Visualization – Matplotlib & Seaborn

  • Intro to Matplotlib – Basic Plots & Customizations
  • Common Charts: Line, Bar, Scatter, Histogram, Pie
  • Intro to Seaborn – Styling with Ease
  • Statistical Visuals: Boxplot, Heatmap, Pair plot
  • Combining Visual Libraries with Pandas
  • Visualization Best Practices for Analysis
Module 6

Exploratory Data Analysis (EDA) & Statistics

  • EDA Process: Why and How
  • Descriptive Statistics in Pandas
  • Measures of Central Tendency and Dispersion
  • Detecting Outliers & Data Distribution
  • Correlation and Covariance
  • Feature Engineering Basics
  • Hypothesis Testing Overview
  • EDA Reporting with Python Visuals
Module 7

SQL for Data Analysis

  • SQL Basics
  • Databases & Relational Concepts
  • Data Types & Table Design
  • DDL, DML, DCL, and TCL
  • Constraints, Operator and Relationships
  • Filtering, Sorting, and Aggregation
  • GROUP BY, HAVING, and Clauses
  • Joins (INNER, LEFT, RIGHT, FULL)
  • Subqueries and Nested Queries
  • Window Functions & Ranking
  • Date, String, and Numeric Functions
  • Triggers, Views, and Stored Procedures
  • Python & SQL Integration
Module 8

Data Visualization with Power BI

  • Power BI Overview & Installation
  • Power Query: Data Transformation
  • Data Modeling & Relationships
  • Writing DAX Functions
  • Creating Visuals and Reports
  • Dashboards & Sharing Online
Module 9

Data Visualization with Tableau

  • Tableau Interface & Navigation
  • Connecting to Various Data Sources
  • Worksheets, Dashboards, Stories
  • Filters, Parameters, Calculated Fields
  • Advanced Visuals & Interactivity
Module 10

Excel for Data Analytics

  • Excel Interface Overview
  • Data Entry, Formatting & Cleaning
  • Formulas and Built-in Functions
  • IF, VLOOKUP, INDEX-MATCH
  • Text, Date & Math Functions
  • Conditional Formatting & Data Validation
  • Pivot Tables and Charts
  • Sorting, Filtering, and Named Ranges
  • Excel Dashboards – Tips & Templates
  • Importing and Exporting Data

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  • Work on machine learning, SQL integration, and BI tools.

Frequently asked Questions

Check out some frequently asked questions.

Our course is beginner-friendly and requires no prior design experience. However, basic computer literacy and an interest in design are helpful.

The course covers a comprehensive range of UI/UX design principles, including user research, information architecture, interaction design, visual design, and prototyping.

You'll gain hands-on experience with industry-standard UX/UI design tools like Figma, Adobe XD, Photoshop, Illustrator and Miro.

UI/UX design is a rapidly growing field with diverse career paths such as UX/UI Designer, User Researcher, Interaction Designer, and Visual Designer.

Yes, the course includes career guidance modules to help you build your portfolio, prepare for interviews, and navigate the job market.

Many courses offer alumni communities or mentorship programs for ongoing support after completion.

Course fees vary depending on format and duration. We offer flexible payment options such as installments.

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