Hop on a journey to become a data analyst with our comprehensive roadmap. You can learn key steps, skills, and insights that can navigate your path successfully.
You can become a data analyst by following this structured roadmap that involves learning statistical and analytical skills. You will master all the relevant software tools, gain practical experience and finally earn the necessary qualifications.
Enhance your data skills with Tableau by enrolling in one of the top certification courses available in our detailed guide.
This comprehensive guide provides a step-by-step plan for individuals who aspire to become data analysts. It outlines the key skills, tools, and techniques required to succeed in this field and offers practical advice on how to acquire them. Whether you're just starting or looking to advance your career as a data analyst, this roadmap will provide valuable insights and direction to help you achieve your goals. Get certified in Data Lakes for free and gain the skills you need to excel in your field.
Entry-Level Data Analyst Skills:
Mid-Career Data Analyst Skills:
Senior Data Analyst Skills:
Remember: These skills are a good starting point but employers may prioritize more skills depending on their specific needs.
SQL is a programming language used to manage and manipulate relational databases, including inserting, updating, deleting, and retrieving data. It is used in various applications, such as business intelligence, data analysis, and web development.
As an entry-level data analyst, you should have following skills:
# | Course Name | Provider | Duration |
1. | SQL 101: Databases for Beginners | Skillshare | 1 Hour |
2. | The Complete SQL Bootcamp: Go from Zero to Hero | Udemy | 9 Hours |
3. | Learn SQL Basics for Data Analysis | University of California, Davis | 18 Hours |
4. | SQL: Data Reporting and Analysis | LinkedIn Learning | 2.5 Hours |
As a mid-career data analyst, you should have following skills:
As an senior-level data analyst, you should have following skills:
# | Course Name | Provider | Duration |
1. | The Advanced SQL Course | Udemy | 6 Hours |
2. | Databases: Advanced Topics in SQL | Stanford University | 20 Hours |
3. | Databases and SQL for Data Science with Python | IBM | 39 Hours |
4. | Advanced Relational Database and SQL | Coursera Project Network | 1.5 Hours |
5. | SQL Data Analysis | Learn SQL by Doing | Udemy | 6 Hours |
Microsoft Excel is a spreadsheet software program used for organizing, analyzing, and manipulating numerical data.
It’s a great tool for data analysts who want to organize, analyze, and manipulate numerical data. It has lots of features and functions that make it easy to manage large amounts of data and draw insights from it. If you want to improve your Excel skills as a data analyst, you can choose among the following listed online courses.
# | Course Name | Provider | Duration |
1. | Excel for Everyone: Data Analysis Fundamentals | University of British Columbia | 24 Hours |
2. | Excel Basics for Data Analysis | IBM | 12 Hours |
3. | Excel Skills for Business: Intermediate | Macquarie University | 27 Hours |
4. | Excel Skills for Business: Advanced | Macquarie University | 25 Hours |
5. | Microsoft Excel – Advanced Excel Formulas & Functions | Udemy | 10 Hours |
6. | Data Visualization with Advanced Excel | PwC | 15 Hours |
Follow here for a complete list of Excel Data Analysis courses & classes. |
Moreover you can follow here for a complete list of best Excel certification courses and best online Excel courses. Excel can also be used with other tools like Tableau, which we compared in our Tableau vs Excel article.
Statistics is the discipline that deals with the collection, analysis, interpretation, presentation, and organization of numerical data.
As an entry-level data analyst, you should have a good foundation in basic statistics concepts and be able to apply them to analyze and interpret data. Some of the key statistical skills you should have include descriptive and Inferential statistics, regression analysis, probability theory, data visualization and manipulation etc.
# | Course Name | Provider | Duration |
1. | Introduction to Statistics | Stanford University | 14 Hours |
2. | Introduction to Predictive Modeling | University of Minnesota | 12 Hours |
3. | Calculating Descriptive Statistics in R | Coursera Network Project | 2 Hours |
As a mid-career data analyst, you should have a strong understanding of the basic statistics concepts and skills required for entry-level roles, as well as more advanced statistical techniques. Some of the key statistical skills you should have at this stage in your career include multivariate statistics, Time series analysis, Experimental design, Bayesian statistics and more.
As a senior data analyst, a deep understanding of statistical models is crucial for designing and implementing effective data analysis strategies, and for communicating statistical findings to stakeholders. It involves understanding the assumptions and limitations of different models, selecting appropriate models for different types of data and research questions, and interpreting and communicating the results of statistical models to non-technical audiences.
Statistical models are mathematical models that are used to describe the relationships between variables in a dataset. They are used in statistical analysis to help researchers understand the patterns and relationships in their data, and to make predictions about future observations.
A statistical model typically involves a set of assumptions about the data, and a set of parameters that describe the relationships between variables. The parameters are estimated from the data using statistical techniques such as maximum likelihood estimation or Bayesian inference.
There are many different types of statistical models, depending on the nature of the data and the research question being investigated. Some examples include:
# | Course Name | Provider | Duration |
1. | Statistical Inference and Modeling for High-throughput Experiments | Harvard University | 16 Hours |
2. | Advanced Linear Models for Data Science 2: Statistical Linear Models | John Hopkins University | 5 Hours |
3. | Bayesian Statistics Specialization | University of California, Santa Cruz | 96 Hours |
4. | Advanced Bayesian Statistics Using R | University of Canterbury | 60 Hours |
5. | Python for Time Series Data Analysis | Udemy | 16 Hours |
6. | Applied Statistical Modeling for Data Analysis in R | Udemy | 10 Hours |
If you are looking for a complete list of statistics courses that come with certificates, including courses on business statistics and statistics for data science, you can check out our website by following the link provided.
Data cleaning is the process of identifying and correcting errors, inconsistencies, and inaccuracies in a dataset. This involves detecting and removing irrelevant, duplicate, or incorrect data, and transforming the data into a consistent and usable format for analysis.
Data cleaning is a critical step in the data analysis process as it ensures that the data used for analysis is accurate, complete, and reliable. Without data cleaning, the analysis results may be skewed or incorrect, leading to flawed conclusions and ineffective decision-making.
Data wrangling, also known as data munging or data preparation, is the process of transforming and preparing raw data into a format that is suitable for analysis. This involves a variety of tasks, including cleaning, transforming, merging, and reshaping data.
Data wrangling is an important step in the data analysis process because it allows analysts to work with data that is in a format that can be easily analyzed. It can also help to ensure that the data is consistent, complete, and accurate.
Data wrangling often involves using programming languages such as Python or R to automate the process of cleaning and transforming the data. This can be a time-consuming process, but it is essential for ensuring that the data is ready for analysis.
Data cleaning and data wrangling are both important steps in the data analysis process, but they refer to different aspects of preparing data for analysis.
Data cleaning involves identifying and correcting errors, inconsistencies, and inaccuracies in a dataset. This includes removing or correcting duplicate, irrelevant, or incorrect data, and ensuring that the data is complete, accurate, and consistent. The goal of data cleaning is to ensure that the data is usable for analysis.
Data wrangling, on the other hand, involves transforming and preparing raw data into a format that is suitable for analysis. This includes tasks such as merging, reshaping, and transforming data, as well as addressing missing data and dealing with outliers. The goal of data wrangling is to create a dataset that is optimized for analysis.
In other words, data cleaning is a subset of data wrangling. Data cleaning focuses on removing errors and inconsistencies in the data, while data wrangling involves preparing the data for analysis by transforming it into a more usable format.
# | Course Name | Provider | Duration |
1. | Getting and Cleaning Data | John Hopkins University | 19 Hours |
2. | Prepare, Clean, Transform, and Load Data using Power BI | Coursera Project Network | 2 Hours |
3. | Master Course in Tableau Prep – Prepare & Clean Data | Udemy | 3 Hours |
4. | Data Wrangling | Harvard University | 16 Hours |
5. | Complete Data Wrangling & Data Visualisation With Python | Udemy | 6.5 Hours |
6. | Data Wrangling in R | LinkedIn Learning | 3 Hours |
Data visualization is the graphical representation of data and information using visual elements such as charts, graphs, and maps to communicate complex information clearly and effectively.
At entry-level, a data analyst should have a basic understanding of data visualization principles and tools. Some of the key skills include:
# | Course Name | Provider | Duration |
1. | Data Visualization | Harvard University | 16 Hours |
2. | Mastering Data Visualization: Theory and Foundations | Udemy | 5 Hours |
3. | Introduction to Data Visualization: From Data to Design | Skillshare | 1.5 Hours |
4. | Data Visualization and Building Dashboards with Excel and Cognos | IBM | 12 Hours |
At mid-career level, a data analyst should have a strong understanding of data visualization principles and tools, and should be able to create more complex and sophisticated visualizations. Some of the key skills include:
At senior level, a data analyst should have a deep understanding of data visualization principles and tools, and should be able to create complex and sophisticated visualizations that can communicate insights effectively to a variety of stakeholders. Some of the key skills include:
# | Course Name | Provider | Duration |
1. | Data Visualization in Power BI | Datacamp | 3 Hours |
2. | Data Visualization with Tableau Specialization | University of California | 72 Hours |
3. | Visualizing Data with Python | IBM | 20 Hours |
4. | Data Visualization: Customizing Charts for Beauty & Impact | Skillshare | 1 Hours |
5. | Mastering data visualization in D3.js | Udemy | 6.5 Hours |
R is a programming language and software environment for statistical computing and graphics, widely used for data analysis, data visualization, and statistical modeling.
# | Course Name | Provider | Duration |
1. | R Programming – R Language for Absolute Beginners | Udemy | 10.5 Hours |
2. | Data Analysis with R | IBM | 16 Hours |
3. | R Programming: Advanced Analytics In R For Data Science | Udemy | 6 Hours |
Python is a high-level, interpreted programming language that is widely used in data analysis, machine learning, and scientific computing due to its powerful libraries, simplicity, and ease of use.
# | Course Name | Provider | Duration |
1. | Python for Everybody Specialization | University of Michigan | 96 Hours |
2. | Data Analysis with Python | IBM | 14 Hours |
3. | Python Data Analysis | LinkedIn Learning | 2.5 Hours |
4. | Data Analysis with Pandas and Python | Udemy | 21.5 Hours |
5. | The Ultimate Pandas Bootcamp: Advanced Python Data Analysis | Udemy | 32 Hours |
At mid-career level, a data analyst should have a good understanding of the fundamentals of machine learning and should be able to apply basic machine learning algorithms to solve business problems. Some of the key algorithms they should be familiar with include:
Mid-career data analysts should also have experience in selecting and applying appropriate algorithms to solve specific business problems, optimizing hyperparameters, and interpreting the results.
At senior level, a data analyst should have a deep understanding of machine learning algorithms and should be able to apply advanced techniques to solve complex business problems. Some of the key algorithms they should be familiar with include:
In addition to these algorithms, senior data analysts should also have experience in data preparation, feature engineering, and model selection, as well as experience in leading data science projects and mentoring junior team members.
Curious to discover Advanced Data Engineering Training? Explore our comprehensive guide for a diverse range of courses and classes tailored to your expertise level.
# | Course Name | Provider | Duration |
1. | Machine Learning Specialization | Stanford & DeepLearning.AI | 106 Hours |
2. | Production Machine Learning Systems | Google Cloud | 16 Hours |
3. | Advanced NLP with Python for Machine Learning | LinkedIn Learning | 2 Hours |
4. | Neural Networks and Deep Learning | DeeLearning AI | 24 Hours |
5. | Convolutional Neural Networks | DeepLearning AI | 36 Hours |
It’s important to note that these skills lists are not exhaustive, and the specific algorithms that a data analyst should be familiar with will vary depending on the industry, organization, and job role.
Here are some skills that a senior data analyst should have in big data technologies:
# | Course Name | Provider | Duration |
1. | Big Data Analytics with Hadoop and Apache Spark | LinkedIn Learning | 1 Hour |
2. | Big Data Modeling and Management Systems | University of California San Diego | 13 Hours |
3. | Big Data Analytics | University of Adelaide | 100 Hours |
4. | Graph Analytics for Big Data | University of California San Diego | 13 Hours |
It’s important to note that different industry or organizations may require a bit different big data skills for senior data analysts.
Data storytelling is the process of using data to create a narrative or tell a story. It involves using data visualization techniques and data analysis to communicate insights and key messages to an audience.
The goal of data storytelling is to make data more accessible and understandable to a wider audience. By presenting data in a way that is easy to digest, data storytelling can help to communicate complex information and insights in a more compelling way.
Data storytelling often involves using tools such as charts, graphs, and infographics to present data visually. It also involves selecting the right data points and presenting them in a way that supports the narrative or story being told.
Data storytelling is used in a variety of contexts, including business, journalism, and public policy. It is an effective way to communicate insights and recommendations to stakeholders, and can help to drive decision-making and action based on data.
Ready to enhance your data narrative skills? Check out our recommended courses with certification to level up your storytelling game.
At entry level, a data analyst should be able to:
At mid-career level, a data analyst should be able to:
At senior level, a data analyst should be able to:
# | Course Name | Provider | Duration |
1. | Data Storytelling Basics | LinkedIn Learning | 1 Hour |
2. | Data Storytelling and Data Visualization | Udemy | 6 Hours |
3. | Telling Stories with Data | LinkedIn Learning | <1 Hours |
4. | Storytelling and Persuading using Data and Digital Technologies | University of Maryland | 12 Hours |
5. | How to tell a story with data | Udemy | 1.5 Hours |
As a mid-career data analyst with experience in project management and leading data-driven projects, the following skills are important to have:
# | Course Name | Provider | Duration |
1. | Project Management: Methods & Tactics for Success | Skillshare | 1 Hour |
2. | Google Project Management: Professional Certificate | 6 Months |
Leadership and communication skills required for a senior data analyst:
# | Course Name | Provider | Duration |
1. | High-Performance Collaboration: Leadership, Teamwork, and Negotiation | Northwestern University | 14 Hours |
2. | Building Your Leadership Skills | HEC Paris | 17 Hours |
3. | Essential Communication Skills for Professionals | Skillshare | 1 Hours |
In conclusion, becoming a data analyst requires a specific set of skills that can be developed over time with the right training and education. By following our data analyst roadmap, you can learn the skills necessary to succeed in this field at any level, whether you are just starting out or are a seasoned professional.
By mastering skills like SQL, data visualization, and data analysis tools like R or Python, statistical models, machine learning algorithms, big data technologies you can gain a competitive edge in the job market and advance your career. We have compiled a comprehensive list of online courses and resources to help you acquire these skills and excel as a data analyst. With the right mindset and dedication, you can become a successful data analyst and make meaningful contributions to your organization.
Becoming a professional truck driver starts with proper training, and choosing the right truck driving…
Are you passionate about helping others and interested in starting a career in Applied Behavior…
The world of coding offers diverse career opportunities, but understanding the essential skills needed to…
It’s a tough reality: every year, over 14.1 million workers suffer from work-related injuries. For…
If you’ve ever wanted to learn how to cook, but didn’t know where to start,…
Choosing the right career path can be a daunting task, especially with the myriad of…