How to become a data analyst

How to Become a Data Analyst

A data analyst can work for a variety of organizations collecting and analyzing large amounts of data. They typically use a variety of software systems and statistical analysis techniques to analyze the data, which is then used to increase profits and improve business-related decisions for the organization.

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.

Data Analysis with Excel for the 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 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.

Recommended Tools and SKills

Programming with 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.


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.


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 important 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 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: 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 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, 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 non-SQL-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 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 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.

Data Analysis with Excel for the Data Analyst

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