Are you eager to dive into the world of Data Analysis but feel overwhelmed about where to begin? Many aspiring data professionals find themselves asking: “What’s the best starting point for learning data analysis?” and “Where can I find effective resources to master these skills?”. If these questions resonate with you, you’re in the right place.
Drawing from my experience as a data analytics instructor for over two years, I often guide students on their learning journeys. While the data analysis field encompasses various powerful tools, the foundational skills are often built upon a solid understanding of programs you might already be familiar with. The truth is, you don’t need to start with complex coding languages. Excel, a tool many have used in school or work, is an incredibly potent starting point and arguably the best place to learn data analysis with an excel course.
The essential skills for a data analyst include Excel, Power BI, Tableau, SQL, and Python. While countless online courses, both free and paid, exist, the specific course isn’t as critical as the practical application of your learning. Focus on completing 2-3 hands-on projects for each skill to solidify your understanding.
This article aims to provide a comprehensive, day-by-day plan to learn data analysis skills, emphasizing the importance of starting with Excel and suggesting resources and project ideas to guide you. This structured approach is designed to be your long answer, your detailed roadmap to becoming data proficient.
Important Note: This guide is focused on skill acquisition and practical application, not certifications. Despite misleading information suggesting certifications are mandatory for data roles, this plan prioritizes building a strong skill set, creating a portfolio of projects, and developing your professional network. These are the true keys to transitioning into a data-driven career.
Disclaimer: This 100-day structure is inspired by the duration of a full Data Analytics course I teach at technology schools and bootcamps. Feel free to adjust the timeline to fit your own learning pace and schedule.
Your 100-Day Path to Data Analytics Proficiency: Start with Excel
Formal education offers structure, networking opportunities, and accountability, all crucial for effective learning. This guide provides the structure, but networking and maintaining accountability are aspects you’ll need to actively cultivate.
Pre-Work: Building Your Network
Networking might sound daunting, but in today’s interconnected world, it’s essential, especially in tech.
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Establish Your Professional Online Presence: Create profiles on LinkedIn and Twitter before you begin your tech learning journey. These don’t need to be elaborate initially – a professional photo and a concise headline stating “Student Data Analyst” are sufficient to start. You’ll use these platforms throughout your 100 days to share your projects, insights, and progress. Ultimately, these networks will be invaluable in your job search.
- LinkedIn: Set a goal to connect with relevant professionals daily, aiming for 500+ connections. Reaching this number on LinkedIn often unlocks enhanced networking opportunities. Search for terms like “data analyst” and “business intelligence analyst” to find relevant connections. While personalized connection requests are ideal, simply sending requests can also be effective. Join data-focused groups to ask questions, get project feedback, and connect with peers.
- Twitter: Follow influential tech content creators and relevant hashtags like #dataanalytics, #excel, #dataviz to stay updated and engage with the data community.
- Optional: Explore virtual tech communities on platforms like Facebook, Discord, and attend in-person networking events in your area through Meetup.
Pre-Work: Structuring Your Learning Schedule
As the saying goes, “a goal without a plan is just a wish.” Effective learning requires planning. To master data analysis skills within 100 days (or any timeframe), a structured schedule is crucial.
Optional Pre-Work: Leverage Online Learning Platforms like DataCamp
During my data science bootcamp, platforms like DataCamp were instrumental. They provided structured assignments to learn core concepts, allowing us to dedicate class time to projects and in-depth Q&A. DataCamp is a highly recommended resource for anyone pursuing a data career. Their courses offer a blend of reading materials, video lectures, and hands-on exercises, facilitating both learning and practical application of skills to real-world problems.
DataCamp offers a free Basic plan with access to the first chapter of every course and six full courses. Their Premium plan ($300/year or $39/month) unlocks courses for tools like Tableau and Power BI, which are not available on the Basic plan. You can cancel your subscription at any time. (Disclaimer: I am not affiliated with DataCamp and this is not a paid endorsement).
Day 1-5: Foundations in Data & Statistics
Do you need to be a math whiz to work in tech? No. Do you need a decent grasp of statistics for data analysis? Absolutely, yes. My data education, encompassing a Udacity Nanodegree, a local bootcamp, and two master’s programs, all began with statistics courses. If you’re venturing into data science, a strong statistical foundation is even more critical.
Key statistical concepts every data analyst should know include:
- Types of graphs/plots and their appropriate applications
- Understanding Population and Sample
- Normal Distribution principles
- Measures of Central Tendency
- Variance and Standard Deviation
- Covariance and Correlation
- Central Limit Theorem
- P-value interpretation
- Probability basics
Resource: The Statistics course by freeCodeCamp is an excellent starting point. It comprehensively covers all essential statistical concepts for data analysis. The video description includes timestamps, allowing you to navigate to specific topics as needed.
Day 6-20: Mastering Microsoft Excel – Your Data Analysis Launchpad
Many are already familiar with Excel from school or personal use. However, Excel is far more than just spreadsheets; it’s a powerful tool frequently used by data analysts daily. Starting with an excel course is often the best place to learn data analysis because of its accessibility and versatility.
As a data analyst, you will use Excel to:
- Convert and manipulate data types, including text, time, and dates
- Create complex formulas, functions, and conditional aggregations
- Implement VLOOKUP for data merging and lookup
- Utilize Pivot Tables for data summarization and analysis
- Develop insightful data visualizations and dashboards
- And much more!
Resources:
- Microsoft Excel Tutorial for Beginners by freeCodeCamp: This course teaches Excel techniques through six practical, real-world projects.
- MyOnlineTrainingHub’s Interactive Excel Dashboards Demo: This resource provides a fantastic walkthrough of interactive Excel dashboards. You can also access the data and follow along to build your own.
- DataCamp: Data Analysis in Excel course: A structured course on DataCamp for a more guided learning experience.
Excel Project Ideas:
- Data Import and Cleaning with Import Wizard: Import a text file (.txt or .csv) and clean the data using Excel’s Import Wizard.
- Data Import and Cleaning without Import Wizard: Import a text file (.txt or .csv) and clean the data using Excel functions and formulas, without relying on the Import Wizard.
- Messy Data Cleanup: Clean a messy dataset using techniques like removing extra spaces, converting numbers to text, removing duplicates, standardizing text case, and using find and replace.
- Business Dashboard Creation: Create an Excel dashboard to answer specific business questions or tell a data-driven story.
Day 21-35: Power BI – Stepping into Data Visualization
Power BI, another Microsoft product, offers a similar interface to Excel, making the transition smoother. It’s a popular tool for data visualization and also has data cleaning capabilities.
You can download Power BI Desktop here. Note that it’s currently primarily available for PC.
Resources:
- Edureka’s Full Power BI Course: With over 2.5 million views, this comprehensive course provides an excellent overview of Power BI’s functionalities.
- MyOnlineTrainingHub’s Power BI Demo: This resource offers a start-to-finish Power BI demo with downloadable data files, allowing you to follow along and create your own project. You can also apply the techniques to a different dataset for further practice.
- DataCamp Power BI Courses (for Mac users or those preferring DataCamp’s style):
- WiseOwl Power BI Exercises: Offers additional hands-on practice exercises to solidify your Power BI skills.
Power BI Project Ideas:
- Interactive Dashboard with Slicers: Create a dashboard with at least four visualizations (each with a clear title) and interactive slicer filters. Design it to tell a cohesive data story or answer specific business questions.
- Map-Based Dashboard with Drill-Downs: Develop a dashboard that incorporates a map visualization with drill-down features to explore geographical data.
- Time Series and KPI Dashboard: Build a dashboard focused on time series analysis, using cards to display Key Performance Indicators (KPIs).
- Web Data Connection Dashboard: Connect your dashboard to a live web data source and visualize the data in real-time.
- Data Transformation with Query Editor: Use Power BI’s Query Editor to rename columns, join tables from different sources, and then create a dashboard from the transformed data.
Day 36-50: Tableau – Advanced Data Visualization Techniques
Tableau is another leading data visualization software, offering similar functionalities to Power BI. While debates about which is “better” exist, Tableau is often favored for its aesthetic appeal and cross-platform compatibility (including Mac).
Resources:
- Edureka’s Full Tableau Course: This course guides you from understanding the Tableau UI to building complex charts, dashboards, functions, and calculations.
- freeCodeCamp’s In-Depth Tableau Course: Another comprehensive option, though it might be redundant if you’ve already completed Edureka’s course. Choose based on your learning preference.
- Alex The Analyst’s Tableau Content: Alex The Analyst provides extensive Tableau tutorials, including a detailed walkthrough of a complete Tableau project in this video.
- DataCamp Tableau Courses:
Tableau Project Ideas:
- Tableau Resume: Create an interactive resume using Tableau, showcasing your skills and experience visually. Explore examples in the Tableau Resume Gallery.
- Tableau Story with Multiple Dashboards: Develop a Tableau Story using a series of dashboards, including an introductory dashboard and a data dictionary dashboard for context.
- Data Joining and Geographic Mapping: Import multiple datasets and utilize Tableau’s join features to merge tables (e.g., merging state columns to incorporate geographical data for map visualizations).
- Cluster Analysis Project: Conduct a Cluster Analysis in Tableau, following tutorials like DataCamp’s guide.
- Replicate Power BI Projects in Tableau: Apply the Power BI project ideas listed earlier to Tableau to practice and compare the tools.
Day 51-72: SQL – Data Extraction and Management
SQL (Structured Query Language) is arguably the most crucial skill for data analysis. Without the ability to extract data from databases, analysis and visualization become impossible. Dedicate significant time to SQL, taking both introductory and intermediate courses.
Resources:
- freeCodeCamp’s Full SQL Course: This course uses MySQL Relational Database Management System (RDBMS) and provides a solid introduction to databases and SQL fundamentals.
- DataCamp SQL Courses:
- W3Schools SQL Tutorial: W3Schools is an invaluable supplemental resource for SQL, offering clear query definitions and practical examples.
Practice Exercises & Project Ideas: (Resources for SQL project ideas and practice exercises were intended here in the original article, but were not explicitly listed). Look for SQL practice websites and datasets online to build projects around querying and data manipulation.
Day 73-100: Python – Expanding Your Analytical Toolkit
While many data analysts perform their roles effectively without Python or other programming languages, learning Python can significantly enhance your capabilities and make you stand out in the job market. It’s particularly beneficial for roles in finance, forecasting, and advanced analytics.
Resources:
- Edureka’s Python, Anaconda, and Jupyter Notebook Tutorial: A quick beginner-friendly tutorial to get you started with Python and its data science environment.
- freeCodeCamp’s Python for Beginners Course: A comprehensive general Python course covering installation, data types, variables, lists, functions, conditional statements, loops, and file handling. While it’s a general Python course, the foundational knowledge is transferable to data analysis.
- Edureka’s Exploratory Data Analysis (EDA) with Python Video: Follow along with this video using your own dataset to learn and practice Exploratory Data Analysis techniques in Python – a great project starter!
- DataCamp Python Courses (Highly Recommended for Data-Specific Python): (DataCamp Python courses were intended to be listed here, but were not in the original article. Search DataCamp for Python for Data Science courses).
Python Project Ideas: (Python project ideas were intended here, but were not explicitly listed in the original article.) Focus on projects involving data manipulation, analysis, and visualization using libraries like Pandas, NumPy, and Matplotlib/Seaborn.
Final Thoughts:
Data is abundant and readily available for practice! Explore public datasets, participate in data challenges, and most importantly, start applying what you learn. Starting with an excel course is truly the best place to learn data analysis and build a solid foundation for your data career. Good luck on your 100-day data journey!
