How to Start a Career in Data Science With No Experience.

How Do I Become a Data Analyst?

To become a Data Analyst, you must have key data analysis skills and an ability to glean insights from large data sets.

Data Analysts Career

Data analytics is a field ripe with opportunity, as companies across all industries have made big commitments to big data, building out their analytics departments – especially in telecommunications, insurance, advertising, financial services, healthcare, and technology. That growth is expected to continue well into the future as industries lagging in big data analytics adoption – including education, government, and manufacturing – have pledged to increase their big data analytics activity in the future.

1. Learn Data Analytics Fundamentals

When thinking about how to become a Data Analyst, your first step should be to learn the data analysis fundamentals and data analysis tools like advanced Microsoft Excel; programming languages SQL, Python and R; Spark and Hadoop for number-crunching; and Tableau, Matplotlib, or ggplot2 for creating beautiful visualizations that bring data to life.

Data analysis courses can be a great way to learn these fundamental data skills, giving you a strong footing on all these platforms, and the ability to gain hands-on experience with data management, statistical programming, programming languages, data cleaning, data visualization, and more.

2. Work on Projects to Develop Your Data Analytics Skills

If you want to become a Data Analyst, you'll need to get a firm grasp on a Data Analyst’s basic tools. Once you've done so, you can begin putting your knowledge to work. Successful Data Analysts will need to keep up-to-date with the latest and greatest tools associated with data analysis, including:

• Spreadsheets and querying languages depended on by Data Analysts, such as XML and SQL

• Programming languages and frameworks commonly used by Data Analysts like Python, R, and Hadoop

• Visualization tools popular with Data Analysts like Tableau, PowerBI; Plotly, Bokeh, and Matplotlib

They will also need to have experience with one or several leading data analysis platforms, including Google Analytics and Adobe Analytics.

Put together practice projects that touch on all the different stages of data analysis: researching companies and market opportunities, establishing the parameters of the data you need to collect, gathering and cleaning that data, then modeling and analyzing it using custom-built algorithms. Finally, turn the insights you’ve drawn from your work into beautiful visualizations, or even try organizing them into a dashboard that allows others to query and interact with your dataset in a user-friendly way.

As you go, you’ll also be practicing the soft skills that elevate the best Data Analysts above their peers: a good eye for detail, an ability to think creatively and analytically, excellent communication skills, and, of course, a head for numbers.

3. Develop Visualizations and Practice Presenting Them

To become a Data Analyst, you'll want to start using programs like Tableau, PowerBI, Bokeh, Plotly, or Infogram. Practice building your own visualizations from scratch, finding the best way to let the data speak for itself. Excel comes into play even during this step: although the basic premise behind spreadsheets is straightforward – making calculations or graphs by correlating the information in their cells – Excel remains incredibly useful after more than 30 years and is virtually unavoidable in the field of data science.

But creating visualizations is just the beginning. Data Analysts also need to be able to use these visualizations to present their findings. These communication skills may come naturally to you, but if not, you can improve with practice. Start small, if necessary, delivering presentations to a single friend for example, before moving on to colleagues.

4. Develop a Data Analyst Portfolio to Showcase Your Work

One of the most important steps to take when planning how to become a Data Analyst is deciding how you will demonstrate your data skills and knowledge. A professional portfolio is a must, and to get started, you should put the code you’ve written (even as part of your coursework) up on GitHub to show what you can do and begin building your professional portfolio.

Becoming a member of an online data science network like Kaggle is another great way to show that you’re engaged with the community, show off your chops as an aspiring Data Analyst, and continue to grow both your expertise and your outreach.

Finally, a Well-executed project that you pull off on your own can be a great way to demonstrate your data analysis abilities and impress potential hiring managers. Pick something that you’re really interested in, ask a question about it, and try to answer that question with data. Document your journey and present your findings—beautifully visualized—with a clear explanation of your process, highlighting your technical skills and creativity.

5. Apply to Relevant Data Analyst Jobs

There are a wide variety of Data Analyst jobs you can get with data skills. All of the following roles draw heavily on data analytics and can be entry-level or more senior data roles.

• Researcher

• Database Administrator

• Data and Analytics Manager

• Digital Marketing Manager

• Statistician

• Transportation Logistics

• Business Analyst

• Systems Analyst

• Healthcare Data Analyst

• Operations Analyst

• Data Engineer

• Data Analyst

• Quantitative Analyst

• Data Architect

• Data Scientist

There are many other variations out there new applications for data analytics are being developed all the time, and even the jobs listed here will continue to evolve as data analysis becomes more prevalent. Such a highly dynamic field, according to consulting firm Mckinsey & Co., means demand may outpace the projected supply of data professionals by 50 or 60 percent, making Data Analyst jobs even harder to fill. All of which is to say that if you have Data Analyst skills, you’re already in a great position when it comes to following a Data Analyst career path.

How Do I Become a Data Analyst With No Experience?

If you're wondering how to become a Data Analyst without any work experience in the field, your first step is to acquire the relevant data skills; then, you can demonstrate them publicly. Some of these skills are relatively easy to acquire individually, others are more complex. Even so, the field as a whole is large and diverse enough that it can be difficult to know where to even start. A structured learning environment that systematically covers all the basics is the best introduction to the field and will ensure that, from the outset, you’ll be clear on what you still need to learn.

Data Analytics courses and data science bootcamps, for example, are a popular option for aspiring Data Analysts. Here, you can learn key data skills and gain hands-on experience in an accelerated learning format with the confidence that the time you’re spending on learning is focused on the areas where it will benefit you most. 

Once you’ve acquired key data skills, the next step to becoming a Data Analyst is to practice using these skills, ideally by building your own projects that you can share publicly. An effective way to display your work – and your data skills – is by posting the code you’ve written, even as part of your coursework, on GitHub. This will show off what you can do, and form the beginning of your professional portfolio.

Don’t stop at the basics. An ambitious, well-executed data project that you pull off on your own is a great way to demonstrate your data abilities and impress potential hiring managers hiring when applying for a Data Analyst job. Pick a topic that you’re really interested in, ask a question about it, and try to answer that question with data. Document your journey and present your findings – beautifully visualized – with a clear explanation of your process, highlighting your technical data skills and creativity.

Finally, joining an online data science network like Kaggle can be a great way to show that you’re engaged with the community, show off your chops as an aspiring Data Analyst, and continue to grow both your expertise and your outreach.  

Salaries for Data Analysts Rise With Competencies

Specific competencies can nudge average salaries for Data Analysts even higher. According to IBM’s report, Data Analysts with Map Reduce expertise bring home an average annual income of $115,907. Similarly, Data Analysts with experience using Apache Pig, Hive, and Hadoop are in the market for jobs that average over $110,000 per year.

As serious as the projected talent shortage in data is, those estimates might even be conservative when you consider how technological innovation has the potential to unlock further opportunities for Data Analysts. 

It is a lot easier to get a job in data analytics than in data science.

Most data science positions require you to have a post-graduate degree in a quantitative field. However, most data analysts I know come from a completely unrelated background and do not possess technical degrees.

Data analytic skills can easily be gained by taking online courses and doing boot camps. The learning curve isn’t as steep as that in data science, and it can be learned in a shorter span of time.

Even if you have no previous programming or technical experience, you can gain the skills required to become a data analyst in just a few months.

In this article, I will describe the steps I took to learn data analytics. It took a lot of trial and error to find these resources and create a roadmap for myself.

If you follow these steps, you can learn the skills required to get an entry level data analytics job in just a few months. You can even do it faster than six months depending on the amount of time you spend studying everyday.

Step 1: Learn Python

To get into the field of analytics, you will first need to learn a programming language. Python and R are the two most commonly used languages in this domain.

If you’re just starting out, I strongly suggest learning Python. It is a lot more user-friendly than R and it is easier to pick up. Python also has a wide array of libraries that make tasks like data Pre-Processing a lot easier.

Python is also more widely used than R. If you were to move into a field like web development or machine learning in the future, you won’t need to learn a new language.

Step 2: Learn SQL

SQL skills are necessary to get a job in analytics. Your daily task would usually involve querying large amounts of data from a database, and manipulating the data according to business requirements.

Many companies integrate SQL with other frameworks, and will expect you to know how to query data using these frameworks.

SQL can be used within languages like Python, Scala, and Hadoop. This will differ depending on the company you are working with. However, if you know SQL for data manipulation, you will be able to pick up on other SQL integrated frameworks easily.

I took this free course by Udacity to learn SQL for data analysis. Data Camp also has a popular SQL for data analytics track that you can try out.

Step 3: Data Analysis and Visualization

You will need to know how to analyze data and derive insights from it. Knowing how to code or query data isn’t enough. You need to be able to answer questions and solve problems with this data.

To learn data analysis in Python, you can take this Udemy course I mentioned above. You can also pursue the data analyst career track at Data Camp.

After deriving insights from data, you should be able to present these insights. Stakeholders need to make business decisions based on the insights you present, so you need to make sure your presentation is clear and concise.

These insights are usually presented with the help of data visualization tools. Visualisations can be created using Excel, Python libraries, or business intelligence tools like Tableau.

If you want to become a data analyst, I suggest learning Tableau. It is one of the most commonly used reporting tool and is sought after by most employers.

This Udemy course by Kirill Eremenko is one of the best resource to learn Tableau.

Step 4: Data Storytelling and Presentation

After completing the first three steps, you already have all the necessary skills to get an entry level job in data analytics.

Now, you will need to present these skills to a potential employer. If you don’t come from a technical background, you will need to show recruiters that you have the necessary skill set to become an analyst.

To do this, I strongly suggest building a data analytics portfolio. Build dashboards in Tableau, use Python to analyze Kaggle datasets, and write articles on your newly honed skills.