Harvard Business Review named the Data Analyst job as the “hottest job of the 21st century”. This field has grown a lot and has become one of the most sought-after professions in the past few years.
A data analyst is responsible for collecting, processing, and performing statistical analysis of data for business use. This data will be useful in taking many important decisions for an organization. A Data analyst also helps in prioritizing the raw data collected to make the process smooth. For this purpose, Data Analysts use specific formulas and algorithms.
If you like to work with numbers and algebraic functions then becoming a Data analyst may be the right choice for you. In this article, we will try to give you an overview of resources that can help you become successful as a Data analyst.
Data Analysts should know how to work with Microsoft Excel. Although this is a very basic skill and the non-structured data on Excel is not so important. Data analysts should have basic Data mapping, SQL, and web development skills. They also can find patterns in large data sets and derive actionable insights from processed data.
Data analysis covers everything from statistics and higher mathematics to programming and software development. For this reason, this field promises a great number of jobs and success.
Programming languages required for Data analytics
R and Python are among the extensively used programming languages for data analysis. These are the languages that a Data Analyst should learn. The programming language R supports statistical computing and graphics while Python is easy to use in large projects.
Programming with R:
Using the programming language R requires you to know some important topics if you want to become an expert and do your work efficiently. Three of those areas are listed here.
- Dplyr combines R and SQL. Along with the translation of codes in the SQL language, it is capable of working with both types of data.
- ggplot2 helps build editable plots that can be edited according to the need based on the graphics. Two Ggplot2 sub-systems are being used these days and they are ggally (helps prepare network plots), and ggpairs (matrix).
- Reshape2 has two formats: meta and cast. The meta format converts data from broad format data to long format data while the casting format does the opposite.
Programming with Python:
Python is mainly preferred by beginners as it is very easy to understand. The Python programming language has the packages or libraries that provide you a head start in the data analyst world. Some popular and highly used libraries are NumPy, pandas, matplotlib, scipy, scikit-learn, ipython, ipython notebooks, anaconda, and seaborn.
Other required skills:
Data and statistics are so related to each other that whenever you talk about Data, statistics will automatically come into the picture. Statistical skills such as forming data sets, mean, median, mode, SD, and other variables; histograms, percentiles, probability, ANOVA, correlation, and causation are the important skills a Data Analyst should have.
Data analytics is called a game of numbers so you must be great at working with numbers. Data Analysts should have advanced knowledge of matrices, linear and relational algebra; CAP theorem, framing data, and series.
Machine learning is a combination of multivariable calculus and linear algebra, along with statistics. In machine learning, you don’t have to get involved in any of the machine-learning algorithms for upgrading your skills. These features make it the most powerful skill for Data analytics. Supervised, unsupervised, and reinforcement are three types of machine learning being used these days.
In Data wrangling, all the research data is taken together to form a single and united whole. In this process, raw data is converted to structured, logical sets making them usable for future purposes. The required tools for this process are SQL and nodSQL-based databases.
Communication and Data Visualization
Data analysts are not only responsible for data interpretation and reporting but they also have to communicate important information to all the stakeholders involved. In this process, they use visual encoding tools like asggplot, matplotlib, d3.js, and seaborne.
Roles and responsibilities of Data analysts
- Interpret the numbers
- Analyze market research
- Gather and extract numerical data
- Find important trends, patterns, and algorithms within the data
Data analysts mostly deal with numbers so you need to be handy with numbers. The ability to extract useful information from processed data and present this data accurately is necessary to become a successful Data Analyst. No skill can be learn overnight. You will have to make a good plan and follow it step by step with patience and hard work to become a Data Analyst. With the help of the right guidance, you can achieve your dreams and accelerate your career trajectory.
If you want to start your career in this field then you should have a certificate in data analysis. Becoming a certified Data Analyst will help you get the attention of the employer and reduce competition. If you are ready to prepare for the Data Analyst certification, then uCertify is the right place for you. we’re excited to tell you that we have purses equipped with the best learning resources to help you pass the certification exam with flying colors.
The uCertify Data Analysis course will teach students all the basic and advanced concepts covered in data analysis. Students will gain knowledge on topics like the fundamentals of data analysis, big data, working with data sources, tools for capturing, analyzing, and reporting data. Check out our course and become a Data Analyst!