Roadmap To Learn Data Analytics In 6 Phases

Roadmap To Learn Data Analytics:-

Hello Everyone, Ready to kickstart your data analytics journey? Checkout Roadmap To Learn Data Analytics

Here’s a complete roadmap to guide you!

Start by mastering the basics, such as Excel and SQL, and then dive into data visualization tools like Power BI or Tableau.

Next, build your statistical and Python skills, followed by real-world projects to strengthen your portfolio.

This roadmap covers everything you need to become job-ready in data analytics.

Follow along and start your journey towards a high-demand career! 📊

Phase 1 – Foundation

Understand the Role

• What is data analytics?
• Differences between data analyst, data scientist, and data engineer.
• Common tasks and responsibilities.

Basic Mathematics and Statistics

• Descriptive statistics (mean, median, mode, standard deviation).
• Inferential statistics (hypothesis testing, confidence intervals).

Learn Excel

• Data manipulation and cleaning.
• Pivot tables and charts.
• Basic formulas and functions.

Phase 2 – Data Analysis Tools

SQL (Structured Query Language)

• Basics of SQL (SELECT, WHERE, JOIN).
• Advanced SQL (subqueries, window functions, CTES).

Python/R for Data Analysis

• Python: Libraries such as Pandas, NumPy, Matplotlib, Seaborn.
• R: Libraries such as dplyr, ggplot2, tidyr.

Data Visualization

• Principles of data visualization.
• Tools: Tableau, Power Bl, matplotlib (Python), ggplot2 ( R )

Phase 3- Data Cleaning and Preparation

Data Wrangling

• Handling missing values.
• Data transformation.
• Feature engineering.

Data Quality

• Ensuring data accuracy and consistency.
• Identifying and handling outliers.

Phase 4 – Advance Analytics

Advanced Statistics

• Regression analysis.
• Time series analysis.
• Multivariate analysis.

Machine Learning Basics

• Supervised vs. unsupervised learning.
• Key algorithms (linear regression, decision trees, clustering)

Phase 5 – Practical Applications

Projects and Case Studies

• Work on real-world datasets.
• Kaggle competitions and datasets.
• End-to-end projects from data collection to reporting.

Domain Knowledge

• Understanding the specific industry you are interested in (e.g., finance, healthcare, e-commerce).
• How data analytics is applied in that domain.

Phase 6 – Job Preparation

Building a Portfolio

• Showcase your projects on GitHub.
• Include a variety of projects that demonstrate your skills.

Resume and Linkedin Profile

Tailor your resume for data analyst positions.

Highlight, relevant skills, projects, and experiences.

Interview Preparation

• Common interview questions and answers.
• Mock interviews.
• Case study and technical test

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