How to Build a Successful Data Analyst Career

Named the ‘sexiest job of the 21^st century’ by Harvard Business Review, the field of data science has rapidly become one of the most sought-after domains for professionals from a variety of backgrounds. Specialist data analysts lie close to the top of the food chain, with healthy salaries and benefits.

Preparing for a career in Data Science? Take this test to know where you stand!

But 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 certain 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 data analyst. Here’s an overview of the role to help you lay a roadmap to success.

Skills required to become a successful data analyst:

• Microsoft Excel: The data is of no use if it is not structured properly. 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 to learn for data analysts. While R supports statistical computing and graphics, Python’s ease-of-use makes it a good language for use in large projects.

Programming With R

When talking about R, there are certain areas that you should really focus on to get a good grasp on the language and your work.

Dplyr acts as a bridge b/w both R and SQL. It doesn't only translate the codes in SQL language but works hand-in-hand with both types of data.

Ggplot2 is a system which helps you build plots iteratively which can be edited later if necessary based on the graphics. Further, two Ggplot2 sub-systems are useful: ggally (helps you prepare network plots), and ggpairs (matrix).

reshape2: this is based on two formats, meta and cast. While meta converts data from wide format data to long format data, cast does the opposite.

Programming with Python

Python is one of the simplest programming languages and is preferred by beginners. These packages 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 properly. If we are talking about data, statistics will always enter the picture. Many statistical skills are necessary to build a successful data analyst career, 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 important to a data analyst.

Machine Learning

Machine learning is one of the most powerful skills to pick up if you want to become a data analyst. It is basically a combination of multivariable calculus, linear algebra, along with statistics. You don’t really need to invest in any of the machine-learning algorithms as you just need to upgrade your skills.

There are three kinds of machine learning:

• In supervised learning, the computer algorithm learns in two stages: learning phase, and test phase. In the first stage, the computer learns and adapts to the learning, while in the second it comes alive. Examples: in a modern smartphone, voice identification first learns 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, Naive Bayes classification, Naive Bayes classification, 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 an extra mile. The tools that you would use will be 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 non-SQL based databases which act as a central hub. A few examples are 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 asggplot, matplotlib, d3.js, and seaborne are 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 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 the data intuition skill 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 accurately present these insights in visual form. 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.