How to Build a Successful Data Analyst Career

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 a variety of backgrounds. Data Analyst's lie close to the top of the food chain, with healthy salaries and benefits.

What Do Data Analysts Do?

A data analyst collects, processes, and performs statistical analysis of data, i.e., makes the data useful in one way or another way. 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 to your success.

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.

Programming Skills for a Data Analyst Career

R and Python are two of the most popular programming languages for data analysts to learn. 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 hand-in-hand 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, which beginners tend to prefer. 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. 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, this is the way to go.

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 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 insights derived 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 of 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.

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.

Are you looking forward to becoming a Data Science expert? This career guide is a perfect read to get you started in the thriving field of Data Science. Download the eBook now!

Begin Your Training with Our Data Analyst Master’s Program

If you’re ready to take the next step by earning your certification in data analysis, you’ve come to the right place! At Simplilearn, we’re excited to announce our partnership with IBM is one of our newest online learning programs: the Data Analyst Master’s Program.

Data Analytics Certification Course teaches students the ins and outs of data analysis and includes everything from fundamentals to advanced principles. Students learn an array of advanced analytics tools, data visualization tools, and programming tools, all of which are essential to thrive in a data analyst role.

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.