Named the ‘sexiest job of the 21st century’ by Harvard Business Review, the field of data science has rapidly become one of the most sought-after for professionals from many disparate backgrounds. Data Analysts lie close to the top of the food chain, with job security, healthy salaries and benefits. So, let’s discuss how to become a data analyst.
What Do Data Analysts Do?
A data analyst collects, processes, and performs statistical analysis of data. Or to put it another way, they make data useful in one way or another. They help other people make the right decisions and prioritize the raw data that has been collected to make work easier, using specific formulas and applying the right algorithms.
If you're passionate about numbers, algebraic functions, and enjoy sharing your work with other people, then you will excel as a data analyst. Here’s an overview of the role to help lay a roadmap on how to become a data analyst.
How to Become a Data Analyst: Skills Required to Become a Successful Data Analyst
- Microsoft Excel: The data is of no use if it is not structured correctly. Excel provides a suite of functionality to make data management convenient and hassle-free.
- Basic SQL skills
- Basic web development skills.
- Ability to find patterns in large data sets.
- Data mapping skills.
- Ability to derive actionable insights from processed data.
At one end of the spectrum, data analysis overlaps with statistics and higher mathematics, while at the other, it merges seamlessly with programming and software development.
How to Become a Data Analyst: Programming Skills for a Data Analyst Career
R and Python are two of the most popular programming languages for data analysts to master. While R supports statistical computing and graphics, Python’s ease of use makes it a good language for use in large projects.
R Programming
When talking about R, there are certain areas that you should focus on to get a good grasp of the language and your work.
Dplyr acts as a bridge between R and SQL. It not only translates the codes in SQL language, but it also works together with both types of data.
ggplot2 is a system that helps users build plots iteratively, which can be edited later if necessary, based on the graphics. Further, two Ggplot2 sub-systems are useful: ggally (helps prepare network plots), and ggpairs (matrix).
reshape2: this is based on two formats, meta, and cast. While meta converts data from broad format data to long format data, the cast does the opposite.
Python
Python is one of the simplest programming languages, and because of this, it’s ideal for beginners. These packages or libraries will give you a head start in the data analyst world: numpy, pandas, matplotlib, scipy, scikit-learn, ipython, ipython notebooks, anaconda, and seaborn.
Statistics
Programming is of no use if the data is not interpreted correctly. Furthermore, if we are talking about data, statistics will always enter the picture. Many statistical skills are necessary to build a successful data analyst career path, such as forming data sets, basic knowledge of mean, median, mode, SD and other variables, histograms, percentiles, probability, ANOVA, chaining and distributing the data in certain groups, correlation, causation, and more.
Mathematics
Data analytics is a game of numbers: If you are good with numbers, you will fit right in.
Advanced knowledge of matrices and linear algebra, relational algebra, CAP theorem, framing data, and series are also essential to succeed as a data analyst.
Machine Learning
Machine learning is one of the most powerful skills to learn if you want to learn how to become a data analyst. It is essentially a combination of multivariable calculus and linear algebra, along with statistics. You don’t need to invest in any of the machine-learning algorithms as you need to upgrade your skills.
There are three kinds of machine learning:
- In supervised learning, the computer algorithm learns in two stages: the learning phase and the test phase. In the first stage, the computer learns and adapts to the learning, while in the second, it comes alive. For example, with a modern smartphone, voice identification first determines the user’s authentic voice and intonation before applying it to future use cases. The tools that you would be using are logistic regression, decision trees, support vector machines, and Naive Bayes classification.
- Unsupervised learning is when there are multiple relationships between several items, and a suggestion engine delivers real-time suggestions. A good example is Facebook’s friends’ list. The tools that you would be using are Principal Component Analysis, Singular Value Decomposition, clustering algorithms, and Independent Component Analysis.
- Reinforcement learning is a space between supervised learning and unsupervised learning where there is a chance of either improvement or going the extra mile. The tools that you would use include TD-Learning, Q-Learning, and genetic algorithms.
Data Wrangling
In a sense, data wrangling is where all the research data comes together to form a single, cohesive whole. In data wrangling, raw data is transformed into properly structured, logical sets that are workable. For this, you may need to work with both SQL and noSQL-based databases, which act as central hubs. A few examples include PostgreSQL, Hadoop, MySQL, MongoDB, Netezza, Spark, Oracle, etc.
Communication and Data Visualization
The job of a data analyst is not limited to data interpretation and reporting. Data analysts are also expected to communicate derived insights to all the stakeholders involved. Knowledge of visual encoding tools, like as.ggplot, matplotlib, d3.js, and seaborne, is essential to accomplish this effectively.
Data Intuition
Let's suppose you work in an organization as a data analyst. You have analyzed a set of data and have submitted your report to the team so that they can begin their work. Before commencing work on the project, the team may have a few questions to get a proper understanding of the project and how the data could be used. But you might not have enough time to answer all these questions.
That’s where data intuition steps in. With experience, you learn what questions are likely to be raised and how to curate a set of answers that addresses all blind spots. This will also help you categorize questions as good-to-know or need-to-know.
How to Become a Data Analyst: Tasks Performed by Data Analysts
- Gathering and extracting numerical data.
- Finding trends, patterns, and algorithms within the data.
- Interpreting the numbers.
- Analyzing market research.
- Applying these decisions back to the business.
To be a successful data analyst, you need to have a passion for numbers, the ability to extract useful insights from processed data, and the skill to present these insights in the visual form accurately. These skills cannot be learned overnight. With patience, hard work, and the right guidance, anything is possible. And yes, it all begins with a plan.
What Does a Data Analyst Salary Look Like?
We’ve talked about how data analysts are well compensated, but so far, you’ve seen no hard numbers. Let’s change that. According to Payscale, Data Analysts can earn an annual average salary of USD 61,881. Payscale also indicates that Data Analysts in India earn an average of ₹439,260 per year.
Furthermore, Ziprecruiter shows that even entry level data analyst jobs offer generous compensation, with a range between USD 25,500 and 69,500, and a national average of USD 43,250 per year.
What Kind of Data Analyst Job Can You Get?
To the surprise of no one, data analysts require proficiency in data analytics. But once you get into data analytics, you are suddenly eligible for many different kinds of data analyst-related jobs. Here is a partial list:
- Business Intelligence Analyst
- Data Analyst
- Data Scientist
- Data Engineer
- Quantitative Analyst
- Data Analytics Consultant
- Operations Analyst
- Marketing Analyst
- Project Manager
- IT Systems Analyst
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If you're interested in becoming a Data Science expert, then we have just the right guide for you. The Data Science Career Guide will give you insights into the most trending technologies, the top companies that are hiring, the skills required to jumpstart your career in the thriving field of Data Science, and offers you a personalized roadmap to becoming a successful Data Science expert.
