Data Analysis

Course Details

Learn How To Use Data Analysis In Real-World

Fundamentals of data analysis and its real-world applications
Data cleaning, preprocessing, and transformation
Exploratory data analysis (EDA) and visualization techniques
Basic statistical concepts and hypothesis testing
Hands-on experience with tools like Excel, Python (pandas, matplotlib), or R

Basic computer literacy (file handling, using web browsers, etc.)
Familiarity with spreadsheets (e.g., Excel or Google Sheets) is helpful
No prior programming experience required — we’ll guide you through the basics
A laptop or desktop with internet access
Willingness to learn and solve real-world problems using data

Beginners who want to start a career in data analysis or data science
Students and recent graduates looking to build practical, in-demand skills
Professionals from non-technical backgrounds (business, marketing, HR, etc.) seeking to make data-driven decisions
Entrepreneurs and small business owners who want to analyze customer or sales data
Anyone curious about how to turn data into actionable insights, even without a technical background

Description

Master the essentials of data analysis and transform raw data into valuable insights. This course covers data cleaning, exploration, visualization, and basic statistics using tools like Excel, Python, or R. Perfect for beginners and professionals looking to upskill.

Data Analysis Course Syllabus

This course provides learners with practical and theoretical knowledge of data analysis, covering data collection, cleaning, visualization, and interpretation using modern tools and techniques. Students will gain skills to analyze real-world data and make data-driven decisions.

What is data analysis?
Types of data (structured vs. unstructured)
Real-world applications
Overview of tools: Excel, Python, R

Data sources: surveys, databases, APIs, web scraping
Data types and formats (CSV, Excel, JSON)
Cleaning data: handling missing values, duplicates, formatting
Introduction to pandas (Python) or tidyverse (R)

Descriptive statistics: mean, median, mode, variance
Data summarization and grouping
Sorting and filtering data
Introduction to visualizations: histograms, bar charts, scatter plots

Principles of effective data visualization
Creating charts and dashboards in Excel
Using matplotlib / seaborn (Python) or ggplot2 (R)
Telling stories with data

Correlation and causation
Hypothesis testing basics
Confidence intervals
A/B testing fundamentals

Analyze a real-world dataset
Apply full data analysis workflow
Present findings with visuals and insights
Peer review and feedback

Course Details:

Instructor

15+ Year experience

Lesson Duration

12 Weeks

Lessons

Places for Students

28+

Language:

English,

Certifications

Digital, Physical

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🎓 Get a certificate of completion to showcase your skills to employers or add to your LinkedIn profile.

Course Completion Certificate: Acknowledge your hard work and commitment with a certificate from Xchieve upon successfully finishing the program.
Internship Certificate: After completing a hands-on internship, you will receive an official certificate from Xchieve and top-tier universities.
Industry Certificate: Earn a prestigious industry certificate from well-known and respected companies as a recognition of your practical expertise.