Learn Data Analytics from the Right Guide

Learn Data Analytics from the Right Guide:-

Here’s a complete plan to learn Data Analytics and find a job as a fresher:

1️⃣ Get Familiar with Basic Math: Understand concepts like statistics, probability, and algebra.

2️⃣ Learn Excel: Start with basic functions and formulas, then move on to pivot tables and data visualization.

3️⃣ Master SQL: Learn how to extract, manipulate, and analyze data using SQL queries.

4️⃣ Explore Data Visualization Tools: Get hands-on experience with tools like Tableau or Power BI for creating insightful visualizations.

5️⃣ Understand Data Cleaning: Learn techniques to clean and preprocess data, ensuring its quality and accuracy.

6️⃣ Dive into Python: Learn Python and libraries like Pandas, NumPy, and Matplotlib for data analysis and visualization.

7️⃣ Practice with Real Data: Work on projects using real datasets to gain practical experience.

8️⃣ Learn Machine Learning Basics: Understand the fundamentals of machine learning algorithms and how to apply them to analyze data.

9️⃣ Stay Updated: Keep learning new techniques, tools, and technologies through online courses, books, and communities.

🔟 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 forget✅️

Here’s a complete plan to learn Data Analytics and find a job as a fresher:

1️⃣ Get Familiar with Basic Math: Understand concepts like statistics, probability, and algebra.

2️⃣ Learn Excel: Start with basic functions and formulas, then move on to pivot tables and data visualization.

3️⃣ Master SQL: Learn how to extract, manipulate, and analyze data using SQL queries.

4️⃣ Explore Data Visualization Tools: Get hands-on experience with tools like Tableau or Power BI for creating insightful visualizations.

5️⃣ Understand Data Cleaning: Learn techniques to clean and preprocess data, ensuring its quality and accuracy.

6️⃣ Dive into Python: Learn Python and libraries like Pandas, NumPy, and Matplotlib for data analysis and visualization.

7️⃣ Practice with Real Data: Work on projects using real datasets to gain practical experience.

8️⃣ Learn Machine Learning Basics: Understand the fundamentals of machine learning algorithms and how to apply them to analyze data.

9️⃣ Stay Updated: Keep learning new techniques, tools, and technologies through online courses, books, and communities.

🔟 Build a Portfolio: Showcase your skills and projects through a portfolio to demonstrate your expertise to potential employers.

Free Resources:

1️⃣ Get Familiar with Basic Math:

Khan Academy: Offers comprehensive tutorials on statistics, probability, and algebra for free.

https://www.khanacademy.org

2️⃣ Learn Excel:

Excel Easy: Provides tutorials ranging from basic to advanced Excel functions.

https://www.excel-easy.com

Microsoft Excel YouTube channel: Official channel with tips and tutorials on using Excel.

https://youtube.com/@excelisfun?si=hlLhpuq_4CgUd5eQ

3️⃣ Master SQL:

Mode Analytics SQL Tutorial: A comprehensive guide to SQL for beginners.

https://mode.com/sql-tutorial

w3schools SQL Tutorial: Interactive SQL tutorials covering various topics.

https://www.w3schools.com/sql

4️⃣ 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

5️⃣ Understand Data Cleaning:

Data Cleaning with Python and Pandas: A free eBook covering data cleaning techniques using Python.

Data Cleaning Using Pandas in Python – Complete Guide for Beginners

Kaggle: Provides datasets and competitions for practicing data cleaning skills.

https://www.kaggle.com

6️⃣ Dive into Python:

Python.org: Official Python documentation and tutorials.

https://www.python.org

Corey Schafer’s Python Tutorials: YouTube channel with in-depth Python tutorials.

https://youtube.com/@coreyms?si=Krp3i04DBmD877Ju

7️⃣ Practice with Real Data:

Kaggle: Offers datasets and competitions for data analysis and machine learning projects.

https://www.kaggle.com

UCI Machine Learning Repository: Provides various datasets for machine learning projects.

https://archive.ics.uci.edu

8️⃣ 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

9️⃣ 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.

https://www.datacamp.com

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:

  1. Basic Math
  2. Excel
  3. SQL
  4. Data Visualization Tools
  5. Data Cleaning
  6. Python
  7. Practice with Real Data
  8. Machine Learning Basics
  9. Stay Updated
  10. Build Portfolios 

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!

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