Build A Successful Data Analyst Career

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 quickly become one of the most sought-after fields for professionals from various backgrounds. Data analysts are located near the top of the food chain, with healthy wages and benefits.

Preparing for a career in data science? Take this test to find out where you stand!

But what do data analysts do?

A data analyst collects, processes and performs statistical data analysis, i.e., makes the data useful in one way or another. They help others make the right decisions and prioritize the raw data that has been collected to facilitate the work by using certain formulas and applying appropriate algorithms.

If you are passionate about numbers, algebraic functions, and enjoy sharing your work with others, then you excel as a data analyst. Here is an overview of the role that will help you establish a roadmap to success.

Skills required to become an effective data analyst:

Microsoft Excel: Data is useless if it is not properly structured. Excel provides a suite of features to make data management convenient and hassle-free.

Basic SQL skills

  • Basic web development skills.
  • Ability to find models in large data sets.
  • Data mapping skills.
  • Ability to extract useful information from processed data.

At one end of the spectrum, data analysis overlaps with higher statistics and mathematics, while at the other end it merges 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 calculation and graphics, Python’s ease of use makes it a good language for large projects.

Programming with R

When you talk about R, there are some areas that you should really focus on getting a good understanding of the language and your work.

Dplyr acts as a b/w bridge for both R and SQL. It not only translates codes into SQL language, but works hand in hand with both types of data.

Ggplot2 is a system that helps you build iterative plots that can be edited later if necessary based on the graphs. In addition, two Ggplot2 subsystems are useful: ggally (helps you prepare network plots), and ggpairs (matrix).

Reshape2: this is based on two formats, meta and cast. Although the meta converts data from large format data to long format data, the 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 an edge in the world of data analysts: numpy, pandas, matplotlib, scipy, scikit-learn, ipython, ipython notebooks, anaconda and seaborn.


Programming is useless if the data is not correctly interpreted. If we are talking about data, statistics will always enter the image. Many statistical skills are required to build a successful data analyst career, such as data set formation, basic knowledge of the mean, median, mode, SD and other variables, histograms, percentiles, probability, anova, linking and distribution of data in certain groups, Correlation, causality, and more.


Data analysis is a set 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 for a data analyst.

Automatic learning

Auto learning is one of the most powerful skills you can take if you want to become a data analyst. It is basically a combination of multivariate calculations, linear algebra, with statistics. You don’t really need to invest in any of the machine learning algorithms that you just need to upgrade your skills.

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 learning, while in the second stage it becomes 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 you will use are logistic regression, decision trees, support vector machines, Naive Bayes classification, Naive Bayes classification, Naive Bayes classification, Naive Bayes classification.

Unsupervised learning is when there are several relationships between several elements and a suggestion engine provides real-time suggestions. A good example is Facebook’s friend list. The tools you will use are main component analysis, singular value decomposition, grouping algorithms and independent component analysis.

Enhancing learning is a space between supervised and unsupervised learning where there is a chance of improvement either or go an extra mile. The tools you will use will be TD-Learning, Q-Learning and genetic algorithms.