Learn Data Analytics in 12 Weeks with This Step-by-Step Roadmap

Table of Contents

• Introduction
• Week 1: Learn Excel Basics
• Week 2: Introduction to SQL
• Week 3: Learn Python
• Week 4: Python Libraries for Data Analysis
• Week 5: Statistical Analysis and Machine Learning
• Week 6: Natural Language Processing
• Week 7: End-to-End Data Science Project
• Week 8: Big Data and Spark
• Week 9: Data Visualization
• Week 10: Advanced SQL
• Week 11: Deep Learning
• Week 12: Complete Portfolio Projects

Introduction to the 12-Week Data Analytics Learning Roadmap

Embarking on a career in data analytics can seem daunting to total beginners. With so many programming languages, analysis tools, and statistical concepts to learn, it's easy to feel overwhelmed. But with the right roadmap and commitment, anyone can gain data analytics skills within a few months.

The key is to break down the learning process into manageable weekly steps. Over a 12-week or 3 month period, you can realistically transition from a novice to having intermediate job-ready abilities. By studying 2-4 hours daily and practicing new concepts through hands-on projects, data analytics mastery is within your reach.

Overview of the 12-Week Data Analytics Learning Roadmap

The roadmap provided here structures the key data analytics topics that need to be covered, from basic Excel usage to machine learning algorithms. Each week introduces new tools and techniques that build on previous knowledge. SQL, Python, statistics, advanced modeling, and visualization tools are all incorporated. Crucially, the final weeks focus on real-world applications like A/B testing, developing end-to-end data pipelines, and building a projects portfolio. This culminates in a comprehensive skillset that makes candidates highly employable.

Week 1: Learn Excel Basics

It may seem surprising that Excel skills are highlighted in the first week, given Excel's reputation as basic spreadsheet software. But Excel is used ubiquitously in business settings to store, organize, and analyze datasets.

Learning Excel should not be overlooked even for aspiring data scientists. Recruiters routinely list Excel proficiency as a core job requirement. Excel is useful even when working with large-scale big data tools.

In Week 1, the focus should be on understanding Excel formulas, functions, charts, dashboards, and general data manipulation functionality. With ample practice datasets available online, you can quickly gain competency within a week.

The links provided have curated video tutorials for efficiently leveling up your Excel abilities. Invest 2-4 hours daily into practicing real-world business cases, and you will be off to a great start!

Why Excel skills are crucial for data analytics

The ubiquity of Excel in corporate environments makes it essential for career progression. Even large tech firms use Excel for quick daily or ad-hoc analyses. Learning Excel demonstrates general data aptitude highly valued by employers. The ability to import, clean, analyze, and visualize datasets carries over to tools like SQL and Python. Excel supports developing interactive dashboards to clearly showcase insights. Strong Excel expertise paired with programming or statistical knowledge is a potent combination sought after by companies.

Key Excel functions and features to learn

Some Excel focus areas for Week 1 include: formulas and functions, keyboard shortcuts, conditional formatting, data validation techniques, INDEX/MATCH, PivotTables, Goal Seek, Scenario Manager, creating charts/graphs, data filtering/sorting, Power Query/Power Pivot, VBA macros if time permits. Dedicating a week to thoroughly learning best practices sets a strong foundation for adding more advanced data manipulation tools like Python and SQL in the coming weeks. Take advantage of Excel's built-in Data Analysis ToolPak which includes regression modeling, ANOVA analysis, Fourier analysis, and more. Get exposure now to statistics concepts covered later.

FAQ

Q: How long does it take to learn data analytics?
A: With consistent weekly effort, this 12-week roadmap can take you from beginner to intermediate/advanced level.

Q: What skills are covered in this learning plan?
A: This roadmap covers Excel, SQL, Python, statistical analysis, machine learning, big data tools, visualization, and real-world project work.

Q: What resources should I use for learning?
A: For each topic, use online courses, documentation, books, and hands-on practice projects.

Q: Do I need any prerequisites to start?
A: Basic programming knowledge will be helpful but not required. Excel skills are a plus.

Q: How much time should I spend each week?
A: Aim for 10-15 hours per week dedicated to learning data analytics through this roadmap.

Q: Is the order of the roadmap important?
A: The skills build on each other week-by-week. It's recommended to follow the sequence.

Q: Should I skip any sections?
A: All sections contribute to a well-rounded analytics skillset. But tailor based on your goals.

Q: What kind of projects should I complete?
A: End-to-end projects analyzing real datasets are best for portfolio building.

Q: How do I stay motivated through the roadmap?
A: Set weekly goals, join an online community, and apply skills to real problems.

Q: What should I do after completing the 12 weeks?
A: Continue growing through advanced certifications, Kaggle competitions, and analytics jobs.