Talented data analytics professionals are currently in short supply. Consequently, now is an excellent time to get into the field; therefore, if you plan to break into data analytics quickly, a bootcamp is a perfect beginning. While there are many reputable choices, course content, cost, and location are important matters to consider in your decision.
Data analytics bootcamps are worth it if your goal is to become a data analyst but have no prior experience or education. An immersive and inclusive data analytics bootcamp is excellent for individuals trying to enter the data science field on a short timeline.
Consider that data analytics bootcamps are beneficial, especially for the beginner. This article defines the data analyst career and necessary training topics and qualifications that are valued in the workplace. Also, read about well-known bootcamps that fulfill basic requirements and your competency gaps and get a customized experience.
Important Sidenote: We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and identified 6 proven steps to follow for becoming a data scientist. Read my article: ‘6 Proven Steps To Becoming a Data Scientist [Complete Guide] for in-depth findings and recommendations! – This is perhaps the most comprehensive article on the subject you will find on the internet!
Table of Contents
What Is a Data Analyst?
As a subset of the field of data science, data analytics is simply techniques used to examine data to increase productivity and enrich the business. Insights are gained from data that is collected, analyzed, and used to formulate a resolution to a business problem. Although a data analyst can be entry-level, the role expands as knowledge and experience increases. However, the necessary skill set is:
- Mathematics and statistical knowledge.
- Computer programming language.
- Data visualization, both static and dynamic reporting.
- Communicate and report in clear, concise business language to all levels.
Depending on the size of the company, the analyst will perform all duties or a subsection. Nevertheless, significant contributions are made to predict the organization’s future costs, failures, and business direction.
To start the workflow, identify a business problem, gather, clean, store, and manipulate data for analysis—test, experiment, and model data for the decision or predictive forecast. Subsequently, the resolution is developed and communicated to the stakeholders, and the data engineer puts the model into production.
Mathematics and Statistics
The data analyst has a solid comprehension of math and statistics and needs to make sense of the data, recognize patterns, and understand the importance of data to the business. Statistical learning is used to create data models and interpret them; therefore, deliberately learning theory and concepts is integral to solving business issues.
A business case may be a prediction as to how likely something will occur. If so, the data analyst will use the data pipeline, execute an algorithm to train the data, and build a prediction model to derive an outcome, using supervised and unsupervised learning.
- Supervised learning is a metaphor for learning with a teacher’s feedback. Accordingly, an algorithm, or a set of instructions, is executed to predict the outcome of the business problem, based on multiple input measures.
- Unsupervised learning is a metaphor for learning with no teacher by deducing the outcome based on probabilities without teacher feedback. Correspondingly, patterns are found, and associations are made from the data, and the output is surmised from the organization of that input.
Computer Programming
Although not a requirement, you will certainly garner attention from the hiring manager if you are capable of programming, especially in Python. Moreover, R has gained traction in recent years and is steadily building a following, but Python is in the lead.
What is Python? It’s used to write algorithms to execute data. Although, as a data analyst, you may not be required to write one, you will want to be able to discuss, understand, read, execute, and customize an algorithm.
For a clearer understanding, watch this 10-minute video of the 8 algorithms widely used in data analytics. A data scientist reviews them on a whiteboard for a quick overview.
Python is an efficient and extremely easy to learn language, and companies will pay a higher salary for competent individuals. As an industry standard, it is portable, integrates well with other languages, has an extensive library and community support, and works on any platform, like UNIX.
The Python official website has an extensive resource for all things pertaining to the language. Most bootcamps will include a language, but if unsure, you can’t go wrong with Python. To get your feet wet, here is a 2-hour video lecture describing what it takes to be effective at Python, and eventually, discussing the extra effort needed to become an expert at Python.
Accordingly, if you are proficient in Java, C++, MATLAB, or any other programming language, add it to your resume. Provide an explanation of how you have used the language on a project to bring credibility to your skill. Also, note that a query language, such as SQL, is a programming language tool used to manipulate data, works well with Python.
Data Visualization
Data visualization software used to be a specialization but is now an expected skill for the data analyst. It’s used to provide a snapshot view of up to date, real-time information.
Python and Tableau, an industry favorite data visualization software, integrate well to create an analytical extension called TabPy, expanding Tableaus capabilities. Python scripts are executed in Tableau to add value and additional capabilities. TabPy can be downloaded on the Python developers website, and there are also plenty of instructions and resources to be found.
Data analysis insights and predictions are gathered and depicted visually as historical, real-time, or predictive data graphs, plots, and other informational representations. Complex activities are performed using statistics and other methods to achieve the goal.
Tableau Prep Builder is used for data preparation and analysis. Building on that, dashboards are built on the Tableau Desktop to aggregate data and communicate succinctly to stakeholders in a user-friendly, collaborative manner. A dashboard can easily be offered to constituents to be accessed in real-time and can be reworked by the data analyst as often as necessary.
A 14-day free trial is available by completing an online form. Multiple resources are available on the Tableau support website. Plus, the purchased version includes a support package.
For practice applying Tableau knowledge to the business world, read The Big Book of Dashboards Visualizing Your Data Using Real-World Business Scenarios, by Jeffrey Shaffer. Useful scenarios that have proven successful in real life are documented, ready to be repurposed to your advantage.
Are you a visual person? The Tableau public gallery is absorbing. Visualizations are created using Tableau Public and are nominated to be the feature of the day. Subscribe to get a daily email with the chosen Viz.
As another option, Microsoft Power BI is a powerful business intelligence solution but is not so user-friendly. However, Power BI is still a common choice, particularly for businesses that utilize a Microsoft platform; therefore, it is worth considering if it is the best choice for your organization. Both Tableau and Power BI are high performing and can powerfully handle massive amounts of data.
Communication and Soft Skills
Interpersonal skills and traits necessary to the data analyst should complement your data science knowledge.
- Collaborative teammate
- Strong communication ability
- Presentation skills
- Continual learner
- Adaptability
- Critical thinking
- Time management
- Ethics and integrity
From Stanford Graduate School of Business, this 60-minute video presented by Matt Abrahams describes effective speaking in a spur of moment circumstances. He describes how to learn the skills and practice to ace interviews and other situations that require fast-talking.
Data Analytics Bootcamps
Although many skills can be learned on your own in an unstructured environment, for the beginner, a bootcamp will help make sense of the big picture and to realize where there are gaps.
Listed are a few reputable choices for beginners and are all offered online, although Galvanize and the University of Berkeley take place on campus. Based on your competency level, location, and budget restrictions, choose your best case scenario and begin.
| Website | Length | Skillset | Prerequisite |
|---|---|---|---|
| Galvanize | 12 weeks part-time | SQL, Excel, Tableau | None |
| General Assembly | 10 weeks part-time or 1 week immersive | SQL, Excel, Tableau | None |
| UC Berkeley | 24 weeks | Excel, Statistics, Python, Databases, Front End Web Visualization, Tableau, Hadoop, Machine Learning | Bachelor’s degree |
Data Analytics Career
Now that you have some knowledge and practical experience by completing a bootcamp, keep in contact with your new network and map out a plan to fill in knowledge gaps.
- Collect a portfolio of projects
- Conduct informational interviews
- Attend networking events
- Apply for an internship
- Add specific data science-related value to your resume
- Job Shadow
- Bootcamps
- Certifications
- Obtain an entry-level data analyst position or related job
- Compensation analyst
- Budget analyst
- Process analyst
- Business analyst
- Learn software and computer programming languages
- Python or R
- Tableau or Power BI
- SQL
- Advanced Excel
- Earn a college degree in Computer science, Mathematics, Statistics, Data Science, or Data Analysis
In addition, watch this 7-minute video that describes top interview tips for entry-level data analytics. Interview questions and answers are included.
Due to the rapid increase in the need for data analysis, companies post a variety of job titles. If just breaking into the field, carefully read the qualifications, and ask applicable questions to determine what is expected. Also, keep your career goals in mind and ask about professional development at the company.
- Data analyst
- Database administrator
- Database Developer
- Data Mining Analyst
- Data Scientist
- Data Warehousing Specialist
- Statistician
For an exciting career path, gain as much experience and knowledge related to data science at every position you take. Remember to keep a log of all your projects and what skills were used to complete.
Author’s Recommendations: Top Data Science Resources To Consider
Before concluding this article, I wanted to share few top data science resources that I have personally vetted for you. I am confident that you can greatly benefit in your data science journey by considering one or more of these resources.
- DataCamp: If you are a beginner focused towards building the foundational skills in data science, there is no better platform than DataCamp. Under one membership umbrella, DataCamp gives you access to 335+ data science courses. There is absolutely no other platform that comes anywhere close to this. Hence, if building foundational data science skills is your goal: Click Here to Sign Up For DataCamp Today!
- IBM Data Science Professional Certificate: If you are looking for a data science credential that has strong industry recognition but does not involve too heavy of an effort: Click Here To Enroll Into The IBM Data Science Professional Certificate Program Today! (To learn more: Check out my full review of this certificate program here)
- MITx MicroMasters Program in Data Science: If you are at a more advanced stage in your data science journey and looking to take your skills to the next level, there is no Non-Degree program better than MIT MicroMasters. Click Here To Enroll Into The MIT MicroMasters Program Today! (To learn more: Check out my full review of the MIT MicroMasters program here)
- Roadmap To Becoming a Data Scientist: If you have decided to become a data science professional but not fully sure how to get started: read my article – 6 Proven Ways To Becoming a Data Scientist. In this article, I share my findings from interviewing 100+ data science professionals at top companies (including – Google, Meta, Amazon, etc.) and give you a full roadmap to becoming a data scientist.
Conclusion
An individual with little data science knowledge will benefit greatly from taking a bootcamp as the first step in becoming a data analyst. Currently, the data science field is wide open due to the shortage of experienced professionals. Prioritize the holes in your education and experience, work on soft skills, and learn how to market yourself. Look at the pros and cons of reputable bootcamps and save your money for continual professional development.
After reading this article, you should have a thorough understanding of the value of a data analytics bootcamp; however, only you can look at your strengths and weaknesses and fill in the gaps. Demand for data analysts will continue to grow, especially as data science advances. Build a solid base and grow with your career in the years to come.
BEFORE YOU GO: Don’t forget to check out my latest article – 6 Proven Steps To Becoming a Data Scientist [Complete Guide]. We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and created this comprehensive guide to help you land that perfect data science job.
Affiliate Disclosure: We participate in several affiliate programs and may be compensated if you make a purchase using our referral link, at no additional cost to you. You can, however, trust the integrity of our recommendation. Affiliate programs exist even for products that we are not recommending. We only choose to recommend you the products that we actually believe in.
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