Learn Data Analytics from the Right Guide:-
Hereβs a complete plan to learn Data Analytics and find a job as a fresher:
1οΈβ£ Get Familiar with Basic Math: Understand concepts like statistics, probability, and algebra.
2οΈβ£ Learn Excel: Start with basic functions and formulas, then move on to pivot tables and data visualization.
3οΈβ£ Master SQL: Learn how to extract, manipulate, and analyze data using SQL queries.
4οΈβ£ Explore Data Visualization Tools: Get hands-on experience with tools like Tableau or Power BI for creating insightful visualizations.
5οΈβ£ Understand Data Cleaning: Learn techniques to clean and preprocess data, ensuring its quality and accuracy.
6οΈβ£ Dive into Python: Learn Python and libraries like Pandas, NumPy, and Matplotlib for data analysis and visualization.
7οΈβ£ Practice with Real Data: Work on projects using real datasets to gain practical experience.
8οΈβ£ Learn Machine Learning Basics: Understand the fundamentals of machine learning algorithms and how to apply them to analyze data.
9οΈβ£ Stay Updated: Keep learning new techniques, tools, and technologies through online courses, books, and communities.
π Build a Portfolio: Showcase your skills and projects through a portfolio to demonstrate your expertise to potential employers.
Free resources link to learn Data Analytics that consists of free resources, YouTube channels, roadmap and interview guide
Learn Data Analytics from the right guide
Save this before you forgetβ οΈ
Hereβs a complete plan to learn Data Analytics and find a job as a fresher:
1οΈβ£ Get Familiar with Basic Math: Understand concepts like statistics, probability, and algebra.
2οΈβ£ Learn Excel: Start with basic functions and formulas, then move on to pivot tables and data visualization.
3οΈβ£ Master SQL: Learn how to extract, manipulate, and analyze data using SQL queries.
4οΈβ£ Explore Data Visualization Tools: Get hands-on experience with tools like Tableau or Power BI for creating insightful visualizations.
5οΈβ£ Understand Data Cleaning: Learn techniques to clean and preprocess data, ensuring its quality and accuracy.
6οΈβ£ Dive into Python: Learn Python and libraries like Pandas, NumPy, and Matplotlib for data analysis and visualization.
7οΈβ£ Practice with Real Data: Work on projects using real datasets to gain practical experience.
8οΈβ£ Learn Machine Learning Basics: Understand the fundamentals of machine learning algorithms and how to apply them to analyze data.
9οΈβ£ Stay Updated: Keep learning new techniques, tools, and technologies through online courses, books, and communities.
π Build a Portfolio: Showcase your skills and projects through a portfolio to demonstrate your expertise to potential employers.
Free Resources:
1οΈβ£ Get Familiar with Basic Math:
Khan Academy: Offers comprehensive tutorials on statistics, probability, and algebra for free.
2οΈβ£ Learn Excel:
Excel Easy: Provides tutorials ranging from basic to advanced Excel functions.
Microsoft Excel YouTube channel: Official channel with tips and tutorials on using Excel.
https://youtube.com/@excelisfun?si=hlLhpuq_4CgUd5eQ
3οΈβ£ Master SQL:
Mode Analytics SQL Tutorial: A comprehensive guide to SQL for beginners.
w3schools SQL Tutorial: Interactive SQL tutorials covering various topics.
4οΈβ£ Explore Data Visualization Tools:
Tableau Public: Free version of Tableau for creating visualizations with public data.
https://public.tableau.com/app/discover
Microsoft Power BI: Offers a free version with limited features for data visualization.
https://www.microsoft.com/en-in/power-platform/products/power-bi
5οΈβ£ Understand Data Cleaning:
Data Cleaning with Python and Pandas: A free eBook covering data cleaning techniques using Python.
Data Cleaning Using Pandas in Python β Complete Guide for Beginners
Kaggle: Provides datasets and competitions for practicing data cleaning skills.
6οΈβ£ Dive into Python:
Python.org: Official Python documentation and tutorials.
Corey Schafer’s Python Tutorials: YouTube channel with in-depth Python tutorials.
https://youtube.com/@coreyms?si=Krp3i04DBmD877Ju
7οΈβ£ Practice with Real Data:
Kaggle: Offers datasets and competitions for data analysis and machine learning projects.
UCI Machine Learning Repository: Provides various datasets for machine learning projects.
8οΈβ£ Learn Machine Learning Basics:
Coursera: “Machine Learning” course by Andrew Ng, which offers a free version.
https://www.coursera.org/en-IN
scikit-learn documentation: Official documentation with tutorials and examples for machine learning in Python.
https://scikit-learn.org/stable
9οΈβ£ Stay Updated:
Towards Data Science: Medium publication with articles on various data science topics.
https://towardsdatascience.com
DataCamp: Offers free courses and tutorials on data science and analytics topics.
YouTube Channels:
Data School: Offers tutorials on data science concepts and tools.
https://youtube.com/@dataschool?si=YZZeauLQphtmxYxK
Ken Jee: Provides insights into data science and analytics topics.
https://youtube.com/@kenjee_ds?si=_LXmvNia2QqhtJJk
Corey Schafer: Offers Python tutorials useful for data analysis and visualization.
https://youtube.com/@coreyms?si=R5cT89E_nYtF6hMA
Roadmap:
- Basic Math
- Excel
- SQL
- Data Visualization Tools
- Data Cleaning
- Python
- Practice with Real Data
- Machine Learning Basics
- Stay Updated
- Build PortfoliosΒ
Interview Guide:
LeetCode: Practice coding questions commonly asked in data analytics interviews.
Glassdoor: Research common interview questions at companies you’re interested in.
YouTube channels like Data School and Ken Jee: Offer interview tips and insights into the data analytics industry.
This is a sample resume format
Click the link below:
https://docs.google.com/document/d/1nbIqoE1Hz8j62SvAuOavPkHauIhZd2P-RRfuX5zCt2w/edit?usp=drivesdk
Save this reel and refer back to it anytime you need guidance on your Data Analytics journey!
