Teach Yourself Data Analytics in 30 Days: A Step-by-Step Guide
Data analytics is one of the most in-demand and fastest-growing career fields today. As companies collect ever-increasing amounts of data, there is a huge need for skilled professionals who can analyze this data to uncover valuable insights and inform business decisions.
According to the U.S. Bureau of Labor Statistics, employment of data scientists and mathematical science occupations is projected to grow 31% from 2020 to 2030, much faster than the average for all occupations. This growth is expected to result in about 40,000 new jobs over the decade.
Source: U.S. Bureau of Labor Statistics, Employment Projections program
As a full-stack developer, learning data analytics can take your skill set to the next level. You‘ll be able to not only build applications to collect data, but also analyze that data to make your applications smarter and provide more value to users. Data analytics skills are highly transferable and can open up new opportunities across a variety of industries.
The good news is that you don‘t necessarily need to go back to school and earn another degree to get started in data analytics. Thanks to the wealth of free online courses and resources available today, it‘s possible to teach yourself the foundations of data analytics in as little as 30 days of focused study.
In this guide, I‘ll share a roadmap you can follow to start building practical, job-ready data analytics skills in just 30 days, with key milestones broken down week-by-week. I‘ll include my top recommendations for free courses and hands-on projects you can complete to accelerate your learning. By the end, you‘ll have a solid foundation in data analytics to build on.
Week 1: Excel and SQL Basics
In your first week, start by building your skills with essential tools for working with data: Microsoft Excel and SQL.
Excel
Even as more advanced analytics tools emerge, Excel remains a key part of the data analyst‘s toolkit for data cleaning, manipulation, analysis, and visualization.
Here are some of the most important Excel features to master for data analytics:
- PivotTables – Allow you to quickly summarize and explore large datasets
- VLOOKUP/XLOOKUP – For combining data from multiple sources
- Logic functions (IF, AND, OR) – To apply conditional operations
- Statistical functions (AVERAGE, MEDIAN, STDEV.P) – For summarizing data
- Visualization tools (charts, graphs, sparklines) – To spot trends and outliers
SQL
SQL (Structured Query Language) is the standard language for working with relational databases. Knowing SQL is essential for querying and extracting data that will then be analyzed.
Some of the foundational SQL concepts to learn include:
- SELECT statements – For retrieving data that matches specific conditions
- Joins – For combining data from multiple tables
- Aggregations (COUNT, SUM, AVG) – To summarize data
- Subqueries – For breaking complex queries into manageable parts
- CREATE, INSERT, UPDATE, DELETE – To modify data and database structures
To build your Excel and SQL skills, I recommend completing these free courses in Week 1:
- Data Analysis in Excel from Rice University (edX)
- Intro to SQL from Khan Academy
Set a goal to get through both courses, including all the practice problems and quizzes, to cement your knowledge. If you have extra time, try applying your new Excel and SQL skills to analyze a dataset relevant to your interests.
Week 2: Statistics and Probability Foundations
A strong understanding of statistics and probability is essential for data analytics. Understanding statistical concepts empowers you to properly collect, describe, and interpret data. It allows you to know what metrics to use when, and what conclusions you can validly draw.
Some of the most important statistical concepts to understand as a data analyst include:
- Sampling and study design – To collect unbiased, representative data
- Descriptive statistics (mean, median, mode, standard deviation) – For summarizing datasets
- Distributions – To understand how data is spread out and identify outliers
- Inference (hypothesis testing, confidence intervals) – For making data-driven decisions
- Correlation vs causation – To avoid drawing false conclusions
- Regression analysis – To model relationships between variables
In Week 2, deepen your understanding of these key concepts with the following free resources:
- Statistics and Probability courses from Khan Academy
- Statistics Fundamentals from StatQuest on YouTube
Work through the examples and practice problems to test your knowledge. Consider how you could apply the statistical techniques to practical questions and decision-making scenarios you face as a developer.
Week 3: Data Visualization
The ability to create compelling visualizations to communicate insights from data is arguably the most important skill for an analyst. While complex analyses are essential, if you can‘t explain your findings clearly through charts and graphs, much of the value is lost.
Fortunately, as a developer, many of the key principles of data visualization will already be familiar to you, like:
- Choosing the right visual for the data and message (line graphs for trends over time, bar charts for comparisons, scatter plots for correlations, etc.)
- Decluttering charts to highlight the key takeaway
- Focusing on simple, interpretable visualizations over flashy ones
- Telling a story with data by structuring dashboards effectively
There are many tools available for data visualization today, from spreadsheets to programming libraries to business intelligence software. As a developer learning data analytics, I recommend starting with Tableau. It is one of the most popular platforms and provides a good balance of power and ease-of-use.
To learn Tableau, complete these two free resources in Week 3:
- Tableau Desktop Training Videos
- Data Visualization with Tableau Specialization on Coursera (audit for free)
Practice building clear, impactful visualizations in Tableau, keeping your audience and goals in mind. Experiment with different chart types, layouts, and color schemes to tell a compelling data story.
Week 4: Capstone Projects
With a foundation in data analytics tools and concepts, the best way to solidify and showcase your new skills is through end-to-end projects. Hands-on data analytics projects take you through the full workflow of obtaining, cleaning, analyzing, and visualizing data to solve problems and answer questions.
Some ideas for data analytics projects include:
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Analyzing data from APIs – Collect data from public APIs on social media, finance, weather, etc. to find trends and insights
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Building interactive dashboards – Combine data from multiple sources into an interactive Tableau dashboard that allows users to explore trends
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Joining a Kaggle competition – Put your skills to the test and get experience working with large, messy datasets on pre-defined problems
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Analyzing your own data – Collect data on your habits, hobbies, or work and see what interesting patterns you can find through analysis
For each project, follow this 5-step framework:
- Define the question you want to answer
- Find a suitable public dataset
- Clean and prepare the data using Excel/SQL
- Analyze the data and test hypotheses using statistics
- Visualize and communicate your findings
An example data analytics workflow, via Udacity
Document your projects thoroughly in a digital format like a GitHub repository or Tableau Public workbook. Include your code, visualizations, and clear write-ups of your process and conclusions. Having a strong portfolio of projects is key to demonstrating your skills to potential employers.
Next Steps
After completing this 30-day plan, you‘ll have a strong foundation in the tools, skills, and workflow of data analytics. But your learning journey is just getting started. To continue advancing:
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Learn Python programming for data science, starting with libraries like pandas, NumPy, Matplotlib, and scikit-learn. Vik Paruchuri‘s Data Science Crash Course is a great introduction.
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Dive into machine learning concepts and techniques like supervised learning, decision trees, and neural networks. Andrew Ng‘s Machine Learning course on Coursera is a classic starting point.
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Get experience with big data tools like Hadoop and Spark to work with huge datasets. Udacity offers several free Big Data courses to explore.
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Develop domain expertise in the industry you‘re most interested in, and seek out opportunities to apply data analytics to real-world problems through internships, freelancing, or volunteering. Having in-depth knowledge of a particular domain (e.g. healthcare, finance, marketing) will make you a much more effective and valuable analyst.
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Consider earning a professional certification like the IBM Data Science Professional Certificate or the Google Data Analytics Professional Certificate to validate your skills and stand out to employers.
The field of data analytics is constantly evolving, with new tools and techniques emerging all the time. To stay competitive, commit to continuous learning and skill development throughout your career.
Top tools and skills for data analysts to know, via CIO
Conclusion
The demand for data analytics skills is only going to continue growing in the coming years. As a developer, investing time to learn data analytics will make you more valuable and open up exciting new career opportunities.
While this 30-day plan is only the beginning, it provides a focused, step-by-step path to start building job-ready skills. The key is to learn by doing – as you work through the courses and projects, always be thinking about how you can apply your new knowledge to real-world problems.
Remember, you don‘t need to become an expert in every area of data analytics. Focus on developing a strong foundation in the core tools and techniques, and then specialize based on your interests and the needs of your target industry.
By dedicating time each day to learning and practicing data analytics, you can make significant progress in just a month. Stay curious, ask questions, and don‘t be afraid to tackle messy datasets and challenging problems. Before you know it, you‘ll be uncovering valuable insights from data like a pro!