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| Item Details | Price | ||
|---|---|---|---|
Data Analytics for Beginners
| Instructor: Devendra Kumar Maindola |
Language: English, Hindi |
About the course
This course teaches you the complete data analytics process in a simple and practical way. You will learn the five main steps used in real analytics projects: Ask → Prepare → Process → Analyze → Share You will work with tools like Excel, SQL (SQLite), and Python Pandas to clean data, analyse it, and find useful insights. At the end of the course, you will also learn data storytelling, which means presenting your findings clearly to managers or teams.
Who this course is for
This free course is ideal for:
- Students and freshers who want to start a career in data analytics.
- Working professionals who use Excel and want to learn more advanced data skills.
- Anyone who wants to understand data and make better decisions.
What you’ll learn
By the end of this free course, learners will be able to:
- The data lifecycle: Ask, Prepare, Process, Analyze, Share.
- Key topics: Clean data, Create Pivot Tables, Importance of SQL
- Load and analyze data in Python with Pandas.
- Basic statistics: Mean vs Median, Correlation, and Outliers.
- Create clear charts: Bar, Line, Heatmaps.Present results through data storytelling.
Syllabus
What do we offer
Lifetime Access
Get life long access to all the courses with unlimited learning.
Structured learning
Our curriculum is designed by experts to make sure you get the best learning experience.
Community & Networking
Interact and network with like-minded folks from various backgrounds in exclusive chat groups.
Learn with the best
Stuck on something? Discuss it with your peers and the instructors in the inbuilt chat groups.
Practice tests
With the quizzes and live tests practice what you learned, and track your class performance.
Get certified
Flaunt your skills with course certificates. You can showcase the certificates on LinkedIn with a click.
LEARNING RESOURCES
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LEARNING PATH
Step 1: The Data Lifecycle
Know the 5 steps of analytics: Ask, Prepare, Process, Analyze, Share.
Step 2 : Excel for Data Cleaning:
Remove duplicates, use Text-to-Columns, and create Pivot Tables.
Step 3 : SQL for Data Querying:
Move to advanced prompts, feedback, and better reasoning.
Step 4 : Python for Data Analytics:
Load data with pd.read_csv(), explore DataFrames, and clean data.
Step 5 : Statistics & Insights:
Understand Mean vs Median, correlation, and outliers.
Step 6 : Data Visualization:
Choose the right charts.
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