Are you fascinated by the world of data and eager to dive into data analysis, but feel lost about where to begin? You’re not alone. As a data analytics instructor with over two years of experience, I frequently encounter two common questions from aspiring data analysts: “What skills are essential to become a data analyst?” and “Which resources or courses are the most effective for acquiring these skills?”
Let’s address these questions directly.
- The core skills you need to become a proficient data analyst include: Excel, Power BI, Tableau, SQL, and Python.
- In this guide, I’ll share some of my go-to resources that have proven invaluable in my own data journey and that I consistently recommend to my students. While countless online courses (both free and paid) are available, the specific course you choose is less critical than dedicating yourself to completing at least 2-3 practical projects for each skill.
This article aims to provide a comprehensive, day-by-day roadmap for learning data analysis skills, complete with recommended resources and project ideas to solidify your understanding.
Important Note: This learning path is focused on building practical skills, creating a project portfolio, and networking within the data community, rather than pursuing certifications. While certifications can be valuable, they are not the primary requirement for transitioning into data analysis roles. This guide prioritizes hands-on experience and demonstrable abilities.
Disclaimer: This guide is structured as a 100-day plan because that mirrors 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.
The 100-Day Data Analysis Learning Path
Formal education often provides structure, networking opportunities, and accountability – three key advantages over self-directed learning. This article provides the structure you need. Networking and accountability are aspects you’ll need to actively cultivate yourself.
Pre-Work: Building Your Network
Networking, while sometimes perceived negatively, is crucial in today’s interconnected world, especially within the tech industry.
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Establish a Professional Online Presence: Create profiles on LinkedIn and Twitter before embarking on your data analysis learning journey. Your profiles don’t need to be elaborate initially; a professional photo and a concise headline indicating your status, such as “Aspiring Data Analyst,” are sufficient. You’ll use these platforms throughout your 100-day journey to share your projects, insights, and progress. After completing this guide, these platforms will be instrumental in your job search!
- LinkedIn: Set a goal to connect with relevant professionals daily, aiming to reach 500+ connections. Reaching this connection threshold on LinkedIn often unlocks greater networking opportunities. Search for “data analyst,” “business intelligence analyst,” and related roles to find people to connect with. Personalizing your connection requests with a brief note explaining your interest is helpful but not always necessary. Joining relevant tech or data analysis groups on LinkedIn is also highly recommended. These groups are excellent spaces to ask questions, receive project feedback, and connect with peers and experienced professionals.
- Twitter: Follow influential tech content creators and relevant hashtags such as #DataAnalytics, #DataScience, #Analytics, and communities like #BlackTechTwitter or #WomenInData if they resonate with you.
- Optional: Explore virtual tech communities on platforms like Facebook, Discord, and Slack. For in-person networking, Meetup is a great resource to find local tech events in your area.
Pre-Work: Creating a Study Schedule
The saying “a goal without a plan is just a wish” rings particularly true when learning new skills. To effectively Learn Data Analysis skills within 100 days (or any timeframe), a structured plan is essential.
Optional Pre-Work: Consider DataCamp Subscription
During my data science bootcamp, DataCamp was a primary resource for learning foundational concepts. We used DataCamp’s interactive courses for assignments, allowing class time to focus on project work and in-depth Q&A. I highly recommend DataCamp to anyone pursuing a data-related career. Their courses combine reading materials, video lectures, and hands-on exercises to facilitate learning and applying skills to practical problems.
DataCamp offers a free Basic plan, providing access to the first chapter of every course and six complete courses. Their Premium plan ($300 annually or $39 monthly) unlocks access to courses on Tableau and Power BI, which are not available in 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: Introduction to Data & Statistics Fundamentals
Is a strong math background necessary for a tech career? Not necessarily. However, a solid understanding of basic statistics is crucial for data analysis. My own data journey, including a Udacity Nanodegree, a local bootcamp, and two master’s degrees, all began with foundational statistics coursework. If you’re interested in advancing to data science, a strong grasp of statistical concepts becomes even more vital.
Key statistical concepts for data analysis include:
- Types of graphs and plots and their appropriate applications
- Understanding the difference between Population and Sample
- Normal Distribution and its properties
- Measures of Central Tendency (Mean, Median, Mode)
- Variance and Standard Deviation for data dispersion
- Covariance and Correlation to understand relationships between variables
- Central Limit Theorem and its implications
- P-value and Hypothesis Testing
- Basic Probability concepts
This comprehensive Statistics course by freeCodeCamp covers all these essential statistical concepts. The video description includes timestamps, enabling you to navigate directly to specific topics as needed.
Day 6-20: Mastering Microsoft Excel for Data Analysis
Many individuals are already familiar with Excel, perhaps from academic or personal use. Excel is a surprisingly powerful tool and remains a daily staple for many data analysts.
As a data analyst, you will leverage Excel to:
- Convert data types, including text, time, and dates, to ensure data integrity.
- Create formulas, functions, and conditional aggregations to manipulate and summarize data.
- Implement VLOOKUP for efficient data merging and lookup tasks.
- Utilize Pivot Tables to create dynamic summaries and explore data patterns.
- Develop data visualizations and dashboards for insightful reporting.
- And much more for data manipulation and exploration.
The “Microsoft Excel Tutorial for Beginners” course by freeCodeCamp effectively teaches Excel techniques through six practical, real-world projects, making learning engaging and relevant.
MyOnlineTrainingHub offers an excellent walkthrough demonstration of interactive Excel dashboards. You can also access the dataset used in the demo to follow along and practice building your own.
DataCamp Course: Data Analysis in Excel course provides a structured, hands-on learning experience within the DataCamp platform.
Excel Project Ideas to Practice Your Skills:
- Import a text file (.txt or .csv) and clean the data using Excel’s Import Wizard, exploring its features and options.
- Import a text file (.txt or .csv) and clean the data without using the Import Wizard, practicing manual cleaning techniques and formulas.
- Clean a messy dataset by applying various data cleaning techniques, such as removing extra spaces, converting numbers stored as text, eliminating duplicate entries, standardizing text case, and using find and replace functionalities.
- Create a comprehensive dashboard in Excel to answer specific business questions and/or tell a compelling data story through visualizations.
Day 21-35: Power BI for Data Visualization and Reporting
Power BI, another Microsoft product, shares a similar interface and feel with Excel, making the transition smoother. Power BI is a widely used tool for creating interactive data visualizations and dashboards, and it also offers data cleaning and transformation capabilities.
You can download Power BI Desktop for free here. Note that Power BI Desktop is currently only available for Windows.
Edureka’s comprehensive Power BI Full Course has garnered over 2.5 million views, indicating its popularity and value. This course provides a great overview of Power BI’s interface, functionalities, and capabilities.
MyOnlineTrainingHub also provides a start-to-finish Power BI dashboard demonstration, complete with downloadable data files to follow along. Challenge yourself further by applying the demonstrated techniques to a different dataset to create your own unique project!
If you are a Mac user, leveraging DataCamp is highly recommended: Intro to Power BI, Data Visualization in Power BI, DAX in Power BI. These courses provide Mac-compatible, hands-on learning for Power BI.
For additional hands-on practice, WiseOwl offers a range of Power BI exercises: WiseOwl Power BI Exercises.
Power BI Project Ideas to Enhance Your Portfolio:
- Develop a Power BI dashboard featuring at least four distinct visualizations (always include clear titles) and incorporating slicer filters for interactivity. Ensure your dashboard tells a cohesive data story and/or effectively answers specific business questions.
- Create a dashboard that incorporates a map visualization with drill-down features, allowing users to explore geographical data at different levels of detail.
- Design a dashboard focused on time series analysis, incorporating relevant charts and utilizing cards to display Key Performance Indicators (KPIs) for quick insights.
- Connect your Power BI dashboard to a live web data source and visualize the dynamically updated data.
- Utilize Power Query Editor within Power BI to perform data transformations such as renaming columns and joining tables from different sources. Then, build a dashboard based on the transformed and combined data.
Day 36-50: Tableau for Advanced Data Visualization
Tableau is another leading data visualization software with similar functionalities to Power BI. Discussions about which tool is “better” are common online, often highlighting personal preferences. I personally lean towards Tableau, appreciating its sleek and intuitive user interface, reminiscent of Apple’s design aesthetic. Tableau Desktop is also compatible with Macs.
Edureka’s comprehensive Tableau Full Course guides you from understanding the user interface to building various charts and dashboards, and mastering functions and calculations within Tableau.
freeCodeCamp also offers an in-depth Tableau course. Depending on your learning style and preference, you might choose either the Edureka or freeCodeCamp course, or utilize both as complementary resources.
Alex The Analyst provides a wealth of Tableau content on his YouTube channel (Alex The Analyst Tableau Videos). This video, in particular, walks through a complete Tableau project from start to finish, offering a practical learning experience.
DataCamp Courses: Intro to Tableau, Analyzing Data in Tableau. These DataCamp courses provide structured, interactive learning within the platform.
Tableau Project Ideas to Showcase Your Visualization Expertise:
- Create a visually engaging Tableau Resume (Tableau Resume Examples, Interactive Resume Gallery). This is a creative way to showcase your skills and stand out to potential employers.
- Develop a Tableau Story using multiple dashboards, incorporating an introductory dashboard to set the context and a data dictionary dashboard to explain the datasets used.
- Import multiple datasets into Tableau and utilize join features to combine tables based on common fields (e.g., merging state columns from two datasets to integrate geographical data for map visualizations).
- Perform a Cluster Analysis in Tableau (DataCamp Cluster Analysis Tutorial). This project explores a more advanced analytical technique within Tableau.
- Replicate the Power BI project ideas listed previously, but implement them in Tableau to gain experience and compare the two tools.
Day 51-72: SQL for Data Extraction and Management
SQL (Structured Query Language) is arguably the most critical skill for data analysis. You cannot effectively analyze or visualize data without the ability to extract it from databases. Dedicate significant time to mastering SQL, focusing on both introductory and intermediate concepts.
freeCodeCamp, once again, provides an excellent resource with their comprehensive SQL full course. This course uses MySQL Relational Database Management System (RDBMS) and provides a solid introduction to databases and SQL fundamentals.
DataCamp Courses: Intro to SQL, Joining Data in SQL, Intermediate SQL, Relational Databases in SQL. DataCamp offers a structured learning path for SQL, progressing from basic to more advanced topics.
W3Schools SQL Tutorial is an invaluable supplementary resource for SQL learning. It provides clear query definitions, practical examples, and an interactive “Try SQL” environment for hands-on practice.
SQL Practice Exercises & Project Ideas (Resources to be added – Placeholder in original article): (This section would ideally be populated with links to SQL practice platforms and project ideas. As the original article notes it as a placeholder, it should be acknowledged but not expanded without further instructions.)
Day 73-100: Python for Data Analysis and Automation (Optional but Highly Recommended)
While many data analysts perform their roles effectively without using Python or other programming languages, learning Python can significantly enhance your skillset and career prospects. This section is technically optional but is strongly recommended, especially if you aim to differentiate yourself in the job market or prepare for roles that require programming proficiency. Python is particularly valuable in fields like finance and forecasting, and for automating data analysis tasks.
Edureka offers a concise Python, Anaconda, and Jupyter Notebook tutorial for beginners, providing a quick start to the Python ecosystem for data analysis.
freeCodeCamp’s comprehensive “Python for Beginners” full course covers installation, data types, variables, lists, tuples, functions, conditional statements, loops, and file handling. Note that this is a general Python course, as Python’s applications extend far beyond data analysis. Focus on the concepts relevant to data manipulation and analysis.
Edureka also provides an excellent Exploratory Data Analysis (EDA) tutorial video that you can follow along with using your own dataset to create a practical EDA project!
DataCamp Courses for Python Data Analysis: DataCamp offers a wide array of Python courses specifically tailored for data analysis. Explore their Python track to find courses that align with your learning goals.
Python Project Ideas (Resources to be added – Placeholder in original article): (Similar to the SQL section, this section in the original article is a placeholder for Python project resources. It should be noted but not expanded without further direction.)
Final Thoughts:
Data is everywhere, and there are countless datasets available to practice with and hone your newly acquired data analysis skills! Start exploring, experimenting, and building your portfolio. Your journey to becoming a data analyst starts now!
