Course Overview
Curriculum
What is Data Analytics?
Importance of Data Analytics in Business
Types of Data Analytics: Descriptive, Diagnostic, Predictive, Prescriptive
Basic Functions and Formulas
Arithmetic Operations
Statistical Functions
Data Cleaning and Preparation
Removing Duplicates
Data Formatting
Data Visualization
Charts and Graphs
Conditional Formatting
Pivot Tables and Pivot Charts
Creating and Analyzing Pivot Tables
Understanding Databases
Basic SQL Commands : SELECT, INSERT, UPDATE, DELETE
Filtering Data
Joins and Subqueries
Joins and Subqueries
INNER JOIN, LEFT JOIN, RIGHT JOIN
Writing Subqueries
Python Basics
Syntax and Data Types
Control Structures (Loops and Conditionals)
Working with Libraries
Installing Libraries (pip)
Introduction to Jupyter Notebook
DataFrames and Series
Data Cleaning and Preparation
Handling Missing Data
Filtering and Selecting Data
Data Aggregation and Grouping
Merging and Joining Data Frames
Introduction to NumPy
Arrays and Their Properties
Array Operations
Mathematical Functions
Statistical Functions
Data Visualization with Power BI
Introduction to Power BI
Power BI Desktop vs. Power BI Service
Data Importing and Preparation
Connecting to Data Sources
Data Transformation using Power Query
Creating Reports and Dashboards
Visualizations: Charts, Tables, Maps
Slicers and Filters
Introduction to Tableau
Connecting to Data Sources
Creating Basic Visualizations
Bar Charts, Line Charts, Scatter Plots
Building Dashboards
Combining Visualizations
Interactivity in Dashboards
Real-world Data Analysis Project
Applying Skills Learned
Data Collection, Cleaning, Analysis, and Visualization
Presentation of Findings
Introduction to Git and GitHub
Setting Up a GitHub Account
Basic Git Commands
Git init, Git add, Git commit, Git push
Collaborating on GitHub
Forking and Cloning Repositories
Pull Requests
Task management
Communication Skills
Presentation Skills
- Resume Prep
- LinkedIn
- Job Hunting & outreaching
- Interview Preparation
- Mock Interviews
FAQs
Yes, placement assistance is included to help students secure jobs.
Open to beginners and professionals wanting data analytics skills.
Contact us for competitive fee details and discounts.
Yes, our training includes hands-on projects and applications.
Yes, our data science course is fully online.
Known for practical learning and experienced instructors.
Tools include SQL, Python, R, and Tableau.
Yes, hands-on projects help build a strong portfolio.
Yes, data visualization is a key part of our course.
Absolutely, we start with beginner modules.
Yes, a certificate is provided upon course completion.
Roles include data analyst, business analyst, and data scientist.
Expert trainers, practical projects, and placement support.
Yes, a flexible online format fits professionals’ schedules.
Focus on hands-on skills, portfolio, and placement support.